From 7f773df79aaead73e28a518158b174e91a63bdba Mon Sep 17 00:00:00 2001
From: Nazim <i499773@fontysict.nl>
Date: Mon, 1 Jun 2026 23:26:00 +0200
Subject: [PATCH 1/3] added itransformer"

---
 .gitignore                                    |    2 +-
 docker/Dockerfile                             |   30 +-
 docker/docker-compose.yml                     |   28 +-
 frontend/src/pages/pages-config-editor.ts     |  319 +++--
 frontend/src/services/models.ts               |   33 +-
 pyproject.toml                                |    3 +
 src/service_ml_forecast/api/web_route.py      |    4 +-
 src/service_ml_forecast/config.py             |    8 +-
 .../ml/_itransformer_net.py                   |   87 ++
 .../ml/itransformer_model_provider.py         |  262 ++++
 .../ml/model_provider_factory.py              |   15 +
 .../models/model_config.py                    |   22 +-
 src/service_ml_forecast/models/model_type.py  |    1 +
 tests/conftest.py                             |    9 +-
 .../resources/itransformer-tariff-config.json |   25 +
 tests/ml/test_itransformer_model_provider.py  |  103 ++
 uv.lock                                       | 1164 +++++++++++------
 17 files changed, 1612 insertions(+), 503 deletions(-)
 create mode 100644 src/service_ml_forecast/ml/_itransformer_net.py
 create mode 100644 src/service_ml_forecast/ml/itransformer_model_provider.py
 create mode 100644 tests/ml/resources/itransformer-tariff-config.json
 create mode 100644 tests/ml/test_itransformer_model_provider.py

diff --git a/.gitignore b/.gitignore
index 6308ed62..d757f973 100644
--- a/.gitignore
+++ b/.gitignore
@@ -207,4 +207,4 @@ deployment/data/**
 **/**/.tmp
 
 # setuptools_scm generated version file
-src/service_ml_forecast/_version.py
+src/service_ml_forecast/_version.py
\ No newline at end of file
diff --git a/docker/Dockerfile b/docker/Dockerfile
index 60e201e5..4e8a8d98 100644
--- a/docker/Dockerfile
+++ b/docker/Dockerfile
@@ -24,9 +24,14 @@ WORKDIR /app
 # Version argument for setuptools-scm
 ARG VERSION=0.0.0
 
+# Optional extras to install (e.g. "itransformer" to include torch).
+# Pass --build-arg ML_EXTRAS="" to build a Prophet-only image without torch.
+ARG ML_EXTRAS=itransformer
+
 ENV PYTHONUNBUFFERED=1 \
     PYTHONDONTWRITEBYTECODE=1 \
-    SETUPTOOLS_SCM_PRETEND_VERSION=${VERSION}
+    SETUPTOOLS_SCM_PRETEND_VERSION=${VERSION} \
+    ML_EXTRAS=${ML_EXTRAS}
 
 RUN echo "Installing Python build dependencies..."
 RUN apt-get update \
@@ -44,9 +49,14 @@ COPY src/ ./src/
 COPY scripts/ ./scripts/
 COPY packages/ ./packages/
 
-# Install project dependencies using uv
+# Install project dependencies using uv (include optional extras if set)
 RUN echo "Installing Python project dependencies..."
-RUN uv sync --no-cache-dir \
+RUN uv lock --no-cache && \
+    if [ -n "${ML_EXTRAS}" ]; then \
+        uv sync --no-cache-dir --extra "${ML_EXTRAS}"; \
+    else \
+        uv sync --no-cache-dir; \
+    fi \
     && apt-get remove -y build-essential \
     && apt-get autoremove -y \
     && rm -rf /var/lib/apt/lists/* \
@@ -69,11 +79,15 @@ LABEL git-commit=$GIT_COMMIT
 # Version argument for setuptools-scm
 ARG VERSION=0.0.0
 
+# Must re-declare ARGs from builder stage to use them in the runtime stage
+ARG ML_EXTRAS=itransformer
+
 ENV PYTHONUNBUFFERED=1 \
     PYTHONDONTWRITEBYTECODE=1 \
     PYTHONPATH=/app/src \
     ML_ENVIRONMENT=${ML_ENVIRONMENT:-production} \
-    SETUPTOOLS_SCM_PRETEND_VERSION=${VERSION}
+    SETUPTOOLS_SCM_PRETEND_VERSION=${VERSION} \
+    ML_EXTRAS=${ML_EXTRAS}
 
 # Install any runtime dependencies
 RUN echo "Installing runtime dependencies..."
@@ -91,9 +105,13 @@ COPY src/ ./src/
 COPY scripts/ ./scripts/
 COPY packages/ ./packages/
 
-# Install project dependencies using uv
+# Install project dependencies using uv (include optional extras if set)
 RUN echo "Installing Python project dependencies..."
-RUN uv sync --no-cache-dir
+RUN if [ -n "${ML_EXTRAS}" ]; then \
+        uv sync --no-cache-dir --extra "${ML_EXTRAS}"; \
+    else \
+        uv sync --no-cache-dir; \
+    fi
 
 # Copy frontend build artifacts
 RUN echo "Copying frontend build artifacts..."
diff --git a/docker/docker-compose.yml b/docker/docker-compose.yml
index c87c9143..6c31376b 100644
--- a/docker/docker-compose.yml
+++ b/docker/docker-compose.yml
@@ -5,17 +5,23 @@ services:
     build:
       context: ..
       dockerfile: docker/Dockerfile
-    container_name: service-ml-forecast
+    container_name: ml-forecast
     ports:
       - "8000:8000"
+    env_file:
+      - ../.env
     environment:
-      - ML_LOG_LEVEL=${ML_LOG_LEVEL} # Log level to use
-      - ML_ENVIRONMENT=${ML_ENVIRONMENT} # Environment to run the service in
-      - ML_API_ROOT_PATH=${ML_API_ROOT_PATH} # Public path for the back-end (e.g. when behind a reverse proxy)
-      - ML_OR_URL=${ML_OR_URL} # OpenRemote URL
-      - ML_OR_KEYCLOAK_URL=${ML_OR_KEYCLOAK_URL} # OpenRemote Keycloak URL
-      - ML_OR_SERVICE_USER=${ML_OR_SERVICE_USER} # OpenRemote service user
-      - ML_OR_SERVICE_USER_SECRET=${ML_OR_SERVICE_USER_SECRET} # OpenRemote service user secret
+      # These override .env values with Docker-internal hostnames
+      - ML_OR_URL=https://energy-forecasting-dataset-proxy-1
+      - ML_OR_KEYCLOAK_URL=https://energy-forecasting-dataset-proxy-1/auth
+      - ML_OR_SERVICE_URL=https://localhost
+      - ML_OR_PUBLIC_URL=https://localhost
+      - ML_OR_PUBLIC_KEYCLOAK_URL=https://localhost/auth
+      - ML_VERIFY_SSL=false
+      - ML_API_MIDDLEWARE_KEYCLOAK=false
+      - ML_WEBSERVER_ORIGINS=["https://localhost"]
+      - ML_OR_REALM=master
+      - ML_LOG_LEVEL=${ML_LOG_LEVEL:-INFO}
     volumes:
       # Model storage
       - ../deployment/data/models:/app/deployment/data/models
@@ -24,9 +30,11 @@ services:
     restart: unless-stopped
     networks:
       - ml-network
-    extra_hosts:
-      - "host.docker.internal:host-gateway"
+      - openremote-network
 
 networks:
   ml-network:
     driver: bridge
+  openremote-network:
+    external: true
+    name: energy-forecasting-dataset_default
diff --git a/frontend/src/pages/pages-config-editor.ts b/frontend/src/pages/pages-config-editor.ts
index 2bee9a7c..8e6fce13 100644
--- a/frontend/src/pages/pages-config-editor.ts
+++ b/frontend/src/pages/pages-config-editor.ts
@@ -20,7 +20,7 @@ import { customElement, property, state } from 'lit/decorators.js';
 import { map } from 'lit/directives/map.js';
 import { when } from 'lit/directives/when.js';
 import { ModelTypeEnum, ProphetSeasonalityModeEnum } from '../services/models';
-import type { ProphetModelConfig } from '../services/models';
+import type { ITransformerModelConfig, ModelConfig, ProphetModelConfig } from '../services/models';
 import { APIService } from '../services/api-service';
 import { Router, RouterLocation } from '@vaadin/router';
 import { InputType, OrInputChangedEvent } from '@openremote/or-mwc-components/or-mwc-input';
@@ -36,6 +36,45 @@ import { OrAssetAttributePickerPickedEvent } from '@openremote/or-attribute-pick
 import { getAssetDescriptorIconTemplate } from '@openremote/or-icon';
 import { Asset, AssetModelUtil } from '@openremote/model';
 
+const BASE_FORM_DEFAULTS = {
+    realm: '',
+    name: 'New Model Config',
+    enabled: true as const,
+    target: { asset_id: '', attribute_name: '', training_data_period: 'P6M' },
+    regressors: null as null,
+    forecast_interval: 'PT1H',
+    forecast_periods: 24,
+    forecast_frequency: '1h'
+};
+
+const DEFAULT_PROPHET_FORM_DATA = (realm: string): ProphetModelConfig => ({
+    ...BASE_FORM_DEFAULTS,
+    realm,
+    type: ModelTypeEnum.PROPHET,
+    daily_seasonality: true,
+    weekly_seasonality: true,
+    yearly_seasonality: true,
+    changepoint_range: 0.8,
+    changepoint_prior_scale: 0.05,
+    seasonality_mode: ProphetSeasonalityModeEnum.ADDITIVE
+});
+
+const DEFAULT_ITRANSFORMER_FORM_DATA = (realm: string): ITransformerModelConfig => ({
+    ...BASE_FORM_DEFAULTS,
+    realm,
+    type: ModelTypeEnum.ITRANSFORMER,
+    seq_len: 96,
+    d_model: 128,
+    n_heads: 4,
+    n_layers: 2,
+    d_ff: 256,
+    dropout: 0.1,
+    epochs: 30,
+    batch_size: 64,
+    lr: 0.001,
+    val_split: 0.2
+});
+
 @customElement('page-config-editor')
 export class PageConfigEditor extends LitElement {
     @consume({ context: realmContext })
@@ -156,7 +195,7 @@ export class PageConfigEditor extends LitElement {
     configId?: string;
 
     @state()
-    protected modelConfig: ProphetModelConfig | null = null;
+    protected modelConfig: ModelConfig | null = null;
 
     @state()
     protected loading: boolean = true;
@@ -179,27 +218,7 @@ export class PageConfigEditor extends LitElement {
     protected readonly rootPath = getRootPath();
 
     @state()
-    protected formData: ProphetModelConfig = {
-        type: ModelTypeEnum.PROPHET,
-        realm: '', // Set during setup
-        name: 'New Model Config',
-        enabled: true,
-        target: {
-            asset_id: '',
-            attribute_name: '',
-            training_data_period: 'P6M'
-        },
-        regressors: null,
-        forecast_interval: 'PT1H',
-        forecast_periods: 24,
-        forecast_frequency: '1h',
-        daily_seasonality: true,
-        weekly_seasonality: true,
-        yearly_seasonality: true,
-        changepoint_range: 0.8,
-        changepoint_prior_scale: 0.05,
-        seasonality_mode: ProphetSeasonalityModeEnum.ADDITIVE
-    };
+    protected formData: ModelConfig = DEFAULT_PROPHET_FORM_DATA('');
 
     // Handle basic form field updates
     protected handleBasicInput(ev: OrInputChangedEvent | CustomEvent<{ value: string }>) {
@@ -210,6 +229,16 @@ export class PageConfigEditor extends LitElement {
             return;
         }
 
+        if (target.name === 'type') {
+            const newType = value as ModelTypeEnum;
+            if (newType === ModelTypeEnum.ITRANSFORMER) {
+                this.formData = { ...DEFAULT_ITRANSFORMER_FORM_DATA(this.realm), name: this.formData.name, target: this.formData.target };
+            } else {
+                this.formData = { ...DEFAULT_PROPHET_FORM_DATA(this.realm), name: this.formData.name, target: this.formData.target };
+            }
+            return;
+        }
+
         this.formData = {
             ...this.formData,
             [target.name]: value
@@ -376,7 +405,7 @@ export class PageConfigEditor extends LitElement {
 
     // Set up all the data for the editor
     protected async setupEditor() {
-        this.formData.realm = this.realm;
+        this.formData = DEFAULT_PROPHET_FORM_DATA(this.realm);
         await this.loadConfig();
     }
 
@@ -653,7 +682,7 @@ export class PageConfigEditor extends LitElement {
                                 @or-mwc-input-changed="${this.handleBasicInput}"
                                 label="Model Type"
                                 type="${InputType.SELECT}"
-                                .options="${[['prophet', 'Prophet']]}"
+                                .options="${[['prophet', 'Prophet'], ['itransformer', 'iTransformer']]}"
                                 .value="${this.formData.type}"
                             >
                             </or-mwc-input>
@@ -750,74 +779,182 @@ export class PageConfigEditor extends LitElement {
                     </div>
                 </or-panel>
 
-                <!-- Model parameters, these will be dynamic based on the model type -->
+                <!-- Model parameters — dynamic based on model type -->
                 <or-panel heading="PARAMETERS">
                     <div class="column">
-                        <div class="row">
-                            <!-- changepoint_range -->
-                            <or-mwc-input
-                                type="${InputType.NUMBER}"
-                                name="changepoint_range"
-                                @or-mwc-input-changed="${this.handleBasicInput}"
-                                label="Changepoint range"
-                                .value="${this.formData.changepoint_range}"
-                                max="1.0"
-                                min="0.0"
-                                step="0.01"
-                                required
-                            ></or-mwc-input>
-                            <!-- changepoint_prior_scale -->
-                            <or-mwc-input
-                                type="${InputType.NUMBER}"
-                                name="changepoint_prior_scale"
-                                @or-mwc-input-changed="${this.handleBasicInput}"
-                                label="Changepoint prior scale"
-                                .value="${this.formData.changepoint_prior_scale}"
-                                max="1.0"
-                                min="0.0"
-                                step="0.01"
-                                required
-                            ></or-mwc-input>
-                        </div>
-                        <div class="row">
-                            <!-- seasonality_mode -->
-                            <or-mwc-input
-                                type="${InputType.SELECT}"
-                                .options="${[
-                                    [ProphetSeasonalityModeEnum.ADDITIVE, 'Additive'],
-                                    [ProphetSeasonalityModeEnum.MULTIPLICATIVE, 'Multiplicative']
-                                ]}"
-                                name="seasonality_mode"
-                                @or-mwc-input-changed="${this.handleBasicInput}"
-                                label="Seasonality mode"
-                                .value="${this.formData.seasonality_mode}"
-                                required
-                            ></or-mwc-input>
-                            <!-- daily_seasonality -->
-                            <or-mwc-input
-                                type="${InputType.CHECKBOX}"
-                                name="daily_seasonality"
-                                @or-mwc-input-changed="${this.handleBasicInput}"
-                                label="Daily seasonality"
-                                .value="${this.formData.daily_seasonality}"
-                            ></or-mwc-input>
-                            <!-- weekly_seasonality -->
-                            <or-mwc-input
-                                type="${InputType.CHECKBOX}"
-                                name="weekly_seasonality"
-                                @or-mwc-input-changed="${this.handleBasicInput}"
-                                label="Weekly seasonality"
-                                .value="${this.formData.weekly_seasonality}"
-                            ></or-mwc-input>
-                            <!-- yearly_seasonality -->
-                            <or-mwc-input
-                                type="${InputType.CHECKBOX}"
-                                name="yearly_seasonality"
-                                @or-mwc-input-changed="${this.handleBasicInput}"
-                                label="Yearly seasonality"
-                                .value="${this.formData.yearly_seasonality}"
-                            ></or-mwc-input>
-                        </div>
+                        ${when(
+                            this.formData.type === ModelTypeEnum.PROPHET,
+                            () => {
+                                const p = this.formData as ProphetModelConfig;
+                                return html`
+                                    <div class="row">
+                                        <or-mwc-input
+                                            type="${InputType.NUMBER}"
+                                            name="changepoint_range"
+                                            @or-mwc-input-changed="${this.handleBasicInput}"
+                                            label="Changepoint range"
+                                            .value="${p.changepoint_range}"
+                                            max="1.0"
+                                            min="0.0"
+                                            step="0.01"
+                                            required
+                                        ></or-mwc-input>
+                                        <or-mwc-input
+                                            type="${InputType.NUMBER}"
+                                            name="changepoint_prior_scale"
+                                            @or-mwc-input-changed="${this.handleBasicInput}"
+                                            label="Changepoint prior scale"
+                                            .value="${p.changepoint_prior_scale}"
+                                            max="1.0"
+                                            min="0.0"
+                                            step="0.01"
+                                            required
+                                        ></or-mwc-input>
+                                    </div>
+                                    <div class="row">
+                                        <or-mwc-input
+                                            type="${InputType.SELECT}"
+                                            .options="${[
+                                                [ProphetSeasonalityModeEnum.ADDITIVE, 'Additive'],
+                                                [ProphetSeasonalityModeEnum.MULTIPLICATIVE, 'Multiplicative']
+                                            ]}"
+                                            name="seasonality_mode"
+                                            @or-mwc-input-changed="${this.handleBasicInput}"
+                                            label="Seasonality mode"
+                                            .value="${p.seasonality_mode}"
+                                            required
+                                        ></or-mwc-input>
+                                        <or-mwc-input
+                                            type="${InputType.CHECKBOX}"
+                                            name="daily_seasonality"
+                                            @or-mwc-input-changed="${this.handleBasicInput}"
+                                            label="Daily seasonality"
+                                            .value="${p.daily_seasonality}"
+                                        ></or-mwc-input>
+                                        <or-mwc-input
+                                            type="${InputType.CHECKBOX}"
+                                            name="weekly_seasonality"
+                                            @or-mwc-input-changed="${this.handleBasicInput}"
+                                            label="Weekly seasonality"
+                                            .value="${p.weekly_seasonality}"
+                                        ></or-mwc-input>
+                                        <or-mwc-input
+                                            type="${InputType.CHECKBOX}"
+                                            name="yearly_seasonality"
+                                            @or-mwc-input-changed="${this.handleBasicInput}"
+                                            label="Yearly seasonality"
+                                            .value="${p.yearly_seasonality}"
+                                        ></or-mwc-input>
+                                    </div>
+                                `;
+                            },
+                            () => {
+                                const t = this.formData as ITransformerModelConfig;
+                                return html`
+                                    <div class="row">
+                                        <or-mwc-input
+                                            type="${InputType.NUMBER}"
+                                            name="seq_len"
+                                            @or-mwc-input-changed="${this.handleBasicInput}"
+                                            label="Lookback window (seq len)"
+                                            .value="${t.seq_len}"
+                                            min="2"
+                                            required
+                                        ></or-mwc-input>
+                                        <or-mwc-input
+                                            type="${InputType.NUMBER}"
+                                            name="epochs"
+                                            @or-mwc-input-changed="${this.handleBasicInput}"
+                                            label="Training epochs"
+                                            .value="${t.epochs}"
+                                            min="1"
+                                            required
+                                        ></or-mwc-input>
+                                    </div>
+                                    <div class="row">
+                                        <or-mwc-input
+                                            type="${InputType.NUMBER}"
+                                            name="d_model"
+                                            @or-mwc-input-changed="${this.handleBasicInput}"
+                                            label="Embedding dimension"
+                                            .value="${t.d_model}"
+                                            min="1"
+                                            required
+                                        ></or-mwc-input>
+                                        <or-mwc-input
+                                            type="${InputType.NUMBER}"
+                                            name="n_heads"
+                                            @or-mwc-input-changed="${this.handleBasicInput}"
+                                            label="Attention heads"
+                                            .value="${t.n_heads}"
+                                            min="1"
+                                            required
+                                        ></or-mwc-input>
+                                        <or-mwc-input
+                                            type="${InputType.NUMBER}"
+                                            name="n_layers"
+                                            @or-mwc-input-changed="${this.handleBasicInput}"
+                                            label="Encoder layers"
+                                            .value="${t.n_layers}"
+                                            min="1"
+                                            required
+                                        ></or-mwc-input>
+                                        <or-mwc-input
+                                            type="${InputType.NUMBER}"
+                                            name="d_ff"
+                                            @or-mwc-input-changed="${this.handleBasicInput}"
+                                            label="Feed-forward dimension"
+                                            .value="${t.d_ff}"
+                                            min="1"
+                                            required
+                                        ></or-mwc-input>
+                                    </div>
+                                    <div class="row">
+                                        <or-mwc-input
+                                            type="${InputType.NUMBER}"
+                                            name="batch_size"
+                                            @or-mwc-input-changed="${this.handleBasicInput}"
+                                            label="Batch size"
+                                            .value="${t.batch_size}"
+                                            min="1"
+                                            required
+                                        ></or-mwc-input>
+                                        <or-mwc-input
+                                            type="${InputType.NUMBER}"
+                                            name="lr"
+                                            @or-mwc-input-changed="${this.handleBasicInput}"
+                                            label="Learning rate"
+                                            .value="${t.lr}"
+                                            min="0"
+                                            step="0.0001"
+                                            required
+                                        ></or-mwc-input>
+                                        <or-mwc-input
+                                            type="${InputType.NUMBER}"
+                                            name="dropout"
+                                            @or-mwc-input-changed="${this.handleBasicInput}"
+                                            label="Dropout"
+                                            .value="${t.dropout}"
+                                            min="0.0"
+                                            max="1.0"
+                                            step="0.01"
+                                            required
+                                        ></or-mwc-input>
+                                        <or-mwc-input
+                                            type="${InputType.NUMBER}"
+                                            name="val_split"
+                                            @or-mwc-input-changed="${this.handleBasicInput}"
+                                            label="Validation split"
+                                            .value="${t.val_split}"
+                                            min="0.0"
+                                            max="0.99"
+                                            step="0.05"
+                                            required
+                                        ></or-mwc-input>
+                                    </div>
+                                `;
+                            }
+                        )}
                     </div>
                 </or-panel>
                 <hr />
diff --git a/frontend/src/services/models.ts b/frontend/src/services/models.ts
index 009c1fd3..f8680a05 100644
--- a/frontend/src/services/models.ts
+++ b/frontend/src/services/models.ts
@@ -22,7 +22,8 @@
  * Corresponds to service_ml_forecast.models.model_type.ModelTypeEnum
  */
 export enum ModelTypeEnum {
-    PROPHET = 'prophet'
+    PROPHET = 'prophet',
+    ITRANSFORMER = 'itransformer'
 }
 
 /**
@@ -174,8 +175,36 @@ export interface ProphetModelConfig extends BaseModelConfig {
     changepoint_prior_scale?: number; // Optional as it has a default
 }
 
+/**
+ * iTransformer specific model configuration.
+ * Inverted Transformer: treats each variate's time series as a token.
+ */
+export interface ITransformerModelConfig extends BaseModelConfig {
+    type: ModelTypeEnum.ITRANSFORMER;
+    /** Input lookback window in datapoints. Must be >= 2 × forecast_periods. @default 96 */
+    seq_len?: number;
+    /** Transformer embedding dimension. @default 128 */
+    d_model?: number;
+    /** Number of attention heads. @default 4 */
+    n_heads?: number;
+    /** Number of encoder layers. @default 2 */
+    n_layers?: number;
+    /** Feed-forward hidden dimension. @default 256 */
+    d_ff?: number;
+    /** Dropout rate. @default 0.1 */
+    dropout?: number;
+    /** Training epochs. @default 30 */
+    epochs?: number;
+    /** Training batch size. @default 64 */
+    batch_size?: number;
+    /** AdamW learning rate. @default 0.001 */
+    lr?: number;
+    /** Fraction of data held out for validation. @default 0.2 */
+    val_split?: number;
+}
+
 /**
  * Represents a model configuration, which can be one of the specific model types.
  * This uses a discriminated union based on the 'type' field.
  */
-export type ModelConfig = ProphetModelConfig;
+export type ModelConfig = ProphetModelConfig | ITransformerModelConfig;
diff --git a/pyproject.toml b/pyproject.toml
index 84e8e0db..5dd03e78 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -30,6 +30,9 @@ dependencies = [
 ]
 
 
+[project.optional-dependencies]
+itransformer = ["torch>=2.2.0"]
+
 [tool.uv.workspace]
 members = ["packages/*"]
 
diff --git a/src/service_ml_forecast/api/web_route.py b/src/service_ml_forecast/api/web_route.py
index 3dcd7049..9f816dfd 100644
--- a/src/service_ml_forecast/api/web_route.py
+++ b/src/service_ml_forecast/api/web_route.py
@@ -50,8 +50,8 @@ def _render_index_html() -> HTMLResponse:
 
     runtime_config = {
         "ML_SERVICE_URL": ENV.ML_API_ROOT_PATH,
-        "ML_OR_URL": ENV.ML_OR_URL,
-        "ML_OR_KEYCLOAK_URL": ENV.ML_OR_KEYCLOAK_URL,
+        "ML_OR_URL": ENV.ML_OR_PUBLIC_URL or ENV.ML_OR_URL,
+        "ML_OR_KEYCLOAK_URL": ENV.ML_OR_PUBLIC_KEYCLOAK_URL or ENV.ML_OR_KEYCLOAK_URL,
     }
     config = json.dumps(runtime_config).replace("<", "\\u003c")
     html = index_path.read_text(encoding="utf-8")
diff --git a/src/service_ml_forecast/config.py b/src/service_ml_forecast/config.py
index 9255165c..e45b40b3 100644
--- a/src/service_ml_forecast/config.py
+++ b/src/service_ml_forecast/config.py
@@ -64,8 +64,12 @@ class AppEnvironment(BaseSettings):
     ]  # origins to allow
 
     # OpenRemote Settings
-    ML_OR_URL: str = "http://localhost:8080"  # OpenRemote Manager URL
-    ML_OR_KEYCLOAK_URL: str = "http://localhost:8081/auth"  # OpenRemote Keycloak URL
+    ML_OR_URL: str = "http://localhost:8080"  # OpenRemote Manager URL (backend-to-backend)
+    ML_OR_KEYCLOAK_URL: str = "http://localhost:8081/auth"  # OpenRemote Keycloak URL (backend-to-backend)
+    # Public URLs served to the browser frontend (defaults to ML_OR_URL / ML_OR_KEYCLOAK_URL).
+    # Set these when the backend uses internal Docker hostnames that the browser cannot resolve.
+    ML_OR_PUBLIC_URL: str | None = None
+    ML_OR_PUBLIC_KEYCLOAK_URL: str | None = None
     ML_OR_REALM: str = "master"  # OpenRemote realm to use for the OpenRemote Manager API
     ML_OR_SERVICE_USER: str = "serviceuser"  # OpenRemote Manager service user
     ML_OR_SERVICE_USER_SECRET: str = "secret"  # OpenRemote Manager service user secret
diff --git a/src/service_ml_forecast/ml/_itransformer_net.py b/src/service_ml_forecast/ml/_itransformer_net.py
new file mode 100644
index 00000000..105715fc
--- /dev/null
+++ b/src/service_ml_forecast/ml/_itransformer_net.py
@@ -0,0 +1,87 @@
+# Copyright 2025, OpenRemote Inc.
+#
+# This program is free software: you can redistribute it and/or modify
+# it under the terms of the GNU Affero General Public License as
+# published by the Free Software Foundation, either version 3 of the
+# License, or (at your option) any later version.
+#
+# This program is distributed in the hope that it will be useful,
+# but WITHOUT ANY WARRANTY; without even the implied warranty of
+# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+# GNU Affero General Public License for more details.
+#
+# You should have received a copy of the GNU Affero General Public License
+# along with this program. If not, see <https://www.gnu.org/licenses/>.
+#
+# SPDX-License-Identifier: AGPL-3.0-or-later
+
+import torch
+import torch.nn as nn
+
+
+class ITransformerEncoderLayer(nn.Module):
+    def __init__(self, d_model: int, n_heads: int, d_ff: int, dropout: float) -> None:
+        super().__init__()
+        self.attn = nn.MultiheadAttention(d_model, n_heads, dropout=dropout, batch_first=True)
+        self.ff = nn.Sequential(
+            nn.Linear(d_model, d_ff),
+            nn.GELU(),
+            nn.Dropout(dropout),
+            nn.Linear(d_ff, d_model),
+        )
+        self.norm1 = nn.LayerNorm(d_model)
+        self.norm2 = nn.LayerNorm(d_model)
+        self.drop = nn.Dropout(dropout)
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        residual = x
+        x = self.norm1(x)
+        x, _ = self.attn(x, x, x)
+        x = residual + self.drop(x)
+
+        residual = x
+        x = self.norm2(x)
+        x = self.ff(x)
+        x = residual + self.drop(x)
+        return x
+
+
+class ITransformerNet(nn.Module):
+    """Inverted Transformer for multivariate time series forecasting.
+
+    Each variate's full time series becomes a token; attention is computed over variates.
+    Paper: https://arxiv.org/abs/2310.06625
+    """
+
+    N_VARS = 6  # tariff_norm, hour_sin, hour_cos, dow_sin, dow_cos, is_weekend
+    TARGET_VAR_IDX = 0
+
+    def __init__(
+        self,
+        seq_len: int,
+        pred_len: int,
+        d_model: int = 128,
+        n_heads: int = 4,
+        n_layers: int = 2,
+        d_ff: int = 256,
+        dropout: float = 0.1,
+    ) -> None:
+        super().__init__()
+        self.seq_len = seq_len
+        self.pred_len = pred_len
+
+        self.embed = nn.Linear(seq_len, d_model)
+        self.encoder = nn.Sequential(
+            *[ITransformerEncoderLayer(d_model, n_heads, d_ff, dropout) for _ in range(n_layers)]
+        )
+        self.norm = nn.LayerNorm(d_model)
+        self.projection = nn.Linear(d_model, pred_len)
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        # x: [batch, seq_len, n_vars]
+        x = x.permute(0, 2, 1)           # [batch, n_vars, seq_len]
+        x = self.embed(x)                 # [batch, n_vars, d_model]
+        x = self.encoder(x)               # [batch, n_vars, d_model]
+        x = self.norm(x)                  # [batch, n_vars, d_model]
+        x = self.projection(x)            # [batch, n_vars, pred_len]
+        return x[:, self.TARGET_VAR_IDX, :]  # [batch, pred_len]
diff --git a/src/service_ml_forecast/ml/itransformer_model_provider.py b/src/service_ml_forecast/ml/itransformer_model_provider.py
new file mode 100644
index 00000000..a5c99172
--- /dev/null
+++ b/src/service_ml_forecast/ml/itransformer_model_provider.py
@@ -0,0 +1,262 @@
+# Copyright 2025, OpenRemote Inc.
+#
+# This program is free software: you can redistribute it and/or modify
+# it under the terms of the GNU Affero General Public License as
+# published by the Free Software Foundation, either version 3 of the
+# License, or (at your option) any later version.
+#
+# This program is distributed in the hope that it will be useful,
+# but WITHOUT ANY WARRANTY; without even the implied warranty of
+# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+# GNU Affero General Public License for more details.
+#
+# You should have received a copy of the GNU Affero General Public License
+# along with this program. If not, see <https://www.gnu.org/licenses/>.
+#
+# SPDX-License-Identifier: AGPL-3.0-or-later
+
+import base64
+import io
+import logging
+from datetime import datetime
+from uuid import UUID
+
+import numpy as np
+import pandas as pd
+import torch
+import torch.nn as nn
+from openremote_client import AssetDatapoint
+from torch.utils.data import DataLoader, Dataset, random_split
+
+from service_ml_forecast.common.time_util import TimeUtil
+from service_ml_forecast.ml._itransformer_net import ITransformerNet
+from service_ml_forecast.ml.model_provider import ModelProvider
+from service_ml_forecast.models.feature_data_wrappers import ForecastDataSet, ForecastResult, TrainingDataSet
+from service_ml_forecast.models.model_config import ITransformerModelConfig
+from service_ml_forecast.services.model_storage_service import ModelStorageService
+
+logger = logging.getLogger(__name__)
+
+_MODEL_FILE_EXT = "pt"
+
+ITransformerBundle = dict  # state_dict + scaler + last window + hyperparams
+
+
+class _TimeSeriesDataset(Dataset):  # type: ignore[type-arg]
+    def __init__(self, features: np.ndarray, seq_len: int, pred_len: int) -> None:
+        self.features = torch.from_numpy(features).float()
+        self.seq_len = seq_len
+        self.pred_len = pred_len
+
+    def __len__(self) -> int:
+        return len(self.features) - self.seq_len - self.pred_len + 1
+
+    def __getitem__(self, idx: int) -> tuple[torch.Tensor, torch.Tensor]:
+        x = self.features[idx : idx + self.seq_len]
+        y = self.features[idx + self.seq_len : idx + self.seq_len + self.pred_len, 0]
+        return x, y
+
+
+class ITransformerModelProvider(ModelProvider[ITransformerBundle]):
+    """iTransformer model provider.
+
+    Inverted Transformer for multivariate time series forecasting.
+    Each variate's full time series is treated as a token; attention is over variates.
+    Supports 6 built-in variates: z-scored target + hour_sin/cos + dow_sin/cos + is_weekend.
+    Paper: https://arxiv.org/abs/2310.06625
+    """
+
+    def __init__(self, config: ITransformerModelConfig) -> None:
+        self.config = config
+        self.model_storage_service = ModelStorageService()
+        self._device = "cuda" if torch.cuda.is_available() else "cpu"
+
+    def train_model(self, training_dataset: TrainingDataSet) -> ITransformerBundle | None:
+        datapoints = training_dataset.target.datapoints
+        if not datapoints:
+            logger.error("No target datapoints provided, cannot train model")
+            return None
+
+        min_required = self.config.seq_len + self.config.forecast_periods + 1
+        if len(datapoints) < min_required:
+            logger.error(
+                f"Need at least {min_required} datapoints (seq_len + forecast_periods + 1), "
+                f"got {len(datapoints)}"
+            )
+            return None
+
+        logger.info(f"Training iTransformer -- {self.config.id} on {len(datapoints)} datapoints")
+
+        features, timestamps_ms, mean, std = _build_feature_matrix(datapoints)
+        pred_len = self.config.forecast_periods
+
+        dataset = _TimeSeriesDataset(features, self.config.seq_len, pred_len)
+        val_size = max(1, int(len(dataset) * self.config.val_split))
+        train_size = len(dataset) - val_size
+        train_ds, val_ds = random_split(dataset, [train_size, val_size])
+
+        train_loader = DataLoader(train_ds, batch_size=self.config.batch_size, shuffle=True, drop_last=True)
+        val_loader = DataLoader(val_ds, batch_size=self.config.batch_size, shuffle=False)
+
+        model = ITransformerNet(
+            seq_len=self.config.seq_len,
+            pred_len=pred_len,
+            d_model=self.config.d_model,
+            n_heads=self.config.n_heads,
+            n_layers=self.config.n_layers,
+            d_ff=self.config.d_ff,
+            dropout=self.config.dropout,
+        ).to(self._device)
+
+        optimizer = torch.optim.AdamW(model.parameters(), lr=self.config.lr, weight_decay=1e-4)
+        scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer, T_max=self.config.epochs)
+        criterion = nn.MSELoss()
+
+        best_val_loss = float("inf")
+        best_state: dict = {}
+
+        for epoch in range(1, self.config.epochs + 1):
+            model.train()
+            train_loss = 0.0
+            for x, y in train_loader:
+                x, y = x.to(self._device), y.to(self._device)
+                optimizer.zero_grad()
+                loss = criterion(model(x), y)
+                loss.backward()
+                nn.utils.clip_grad_norm_(model.parameters(), 1.0)
+                optimizer.step()
+                train_loss += loss.item()
+            scheduler.step()
+
+            model.eval()
+            val_loss = 0.0
+            with torch.no_grad():
+                for x, y in val_loader:
+                    x, y = x.to(self._device), y.to(self._device)
+                    val_loss += criterion(model(x), y).item()
+
+            train_loss /= len(train_loader)
+            val_loss /= max(len(val_loader), 1)
+
+            if val_loss < best_val_loss:
+                best_val_loss = val_loss
+                best_state = {k: v.cpu().clone() for k, v in model.state_dict().items()}
+
+            if epoch % 5 == 0 or epoch == self.config.epochs:
+                logger.info(f"Epoch {epoch:3d}/{self.config.epochs} | train {train_loss:.4f} | val {val_loss:.4f}")
+
+        logger.info(f"Training complete -- {self.config.id} | best val MSE={best_val_loss:.4f}")
+
+        last_window = datapoints[-self.config.seq_len :]
+
+        return {
+            "state_dict": best_state,
+            "mean": float(mean),
+            "std": float(std),
+            "last_window": [(int(p.x), float(p.y)) for p in last_window],
+            "seq_len": self.config.seq_len,
+            "pred_len": pred_len,
+            "d_model": self.config.d_model,
+            "n_heads": self.config.n_heads,
+            "n_layers": self.config.n_layers,
+            "d_ff": self.config.d_ff,
+            "dropout": self.config.dropout,
+        }
+
+    def save_model(self, model: ITransformerBundle) -> None:
+        buf = io.BytesIO()
+        torch.save(model, buf)
+        encoded = base64.b64encode(buf.getvalue()).decode("utf-8")
+        self.model_storage_service.save(encoded, self.config.id, model_file_extension=_MODEL_FILE_EXT)
+        logger.info(f"Saved iTransformer -- {self.config.id}")
+
+    def load_model(self, model_config_id: UUID) -> ITransformerBundle:
+        encoded = self.model_storage_service.get(model_config_id, model_file_extension=_MODEL_FILE_EXT)
+        buf = io.BytesIO(base64.b64decode(encoded))
+        return torch.load(buf, map_location="cpu", weights_only=False)
+
+    def generate_forecast(self, forecast_dataset: ForecastDataSet | None = None) -> ForecastResult:
+        bundle = self.load_model(self.config.id)
+
+        net = ITransformerNet(
+            seq_len=bundle["seq_len"],
+            pred_len=bundle["pred_len"],
+            d_model=bundle["d_model"],
+            n_heads=bundle["n_heads"],
+            n_layers=bundle["n_layers"],
+            d_ff=bundle["d_ff"],
+            dropout=bundle["dropout"],
+        ).to(self._device)
+        net.load_state_dict(bundle["state_dict"])
+        net.eval()
+
+        mean: float = bundle["mean"]
+        std: float = bundle["std"]
+        last_window_raw: list[tuple[int, float]] = bundle["last_window"]
+
+        window_datapoints = [AssetDatapoint(x=x, y=y) for x, y in last_window_raw]
+        features, timestamps_ms, _, _ = _build_feature_matrix(window_datapoints, mean=mean, std=std)
+
+        x = torch.from_numpy(features).float().unsqueeze(0).to(self._device)
+        with torch.no_grad():
+            pred_norm = net(x).squeeze(0).cpu().numpy()
+
+        pred_values = pred_norm * std + mean
+
+        freq_ms = _freq_to_ms(self.config.forecast_frequency)
+        last_ts = int(timestamps_ms[-1])
+        datapoints = [
+            AssetDatapoint(x=last_ts + (i + 1) * freq_ms, y=float(v))
+            for i, v in enumerate(pred_values)
+        ]
+
+        return ForecastResult(
+            asset_id=self.config.target.asset_id,
+            attribute_name=self.config.target.attribute_name,
+            datapoints=datapoints,
+        )
+
+
+def _build_feature_matrix(
+    datapoints: list[AssetDatapoint],
+    mean: float | None = None,
+    std: float | None = None,
+) -> tuple[np.ndarray, np.ndarray, float, float]:
+    """Convert raw AssetDatapoints to a 6-variate feature matrix."""
+    sorted_pts = sorted(datapoints, key=lambda p: p.x)
+    timestamps_ms = np.array([p.x for p in sorted_pts], dtype=np.int64)
+    tariff = np.array([float(p.y) for p in sorted_pts], dtype=np.float32)
+
+    if mean is None:
+        mean = float(tariff.mean())
+    if std is None:
+        std = float(tariff.std()) + 1e-8
+
+    tariff_norm = (tariff - mean) / std
+
+    dts = [datetime.fromtimestamp(ts / 1000) for ts in timestamps_ms]
+    hours = np.array([dt.hour for dt in dts], dtype=np.float32)
+    days = np.array([dt.weekday() for dt in dts], dtype=np.float32)
+    weekend = (days >= 5).astype(np.float32)
+
+    features = np.stack(
+        [
+            tariff_norm,
+            np.sin(2 * np.pi * hours / 24),
+            np.cos(2 * np.pi * hours / 24),
+            np.sin(2 * np.pi * days / 7),
+            np.cos(2 * np.pi * days / 7),
+            weekend,
+        ],
+        axis=1,
+    )
+    return features, timestamps_ms, mean, std
+
+
+def _freq_to_ms(frequency: str) -> int:
+    """Convert a pandas frequency string (e.g. '1h', '30min') to milliseconds."""
+    offset = pd.tseries.frequencies.to_offset(frequency)
+    if offset is None:
+        raise ValueError(f"Unrecognised forecast_frequency: {frequency!r}")
+    nanos: int = int(pd.Timedelta(offset).value)  # type: ignore[arg-type]
+    return nanos // 1_000_000
diff --git a/src/service_ml_forecast/ml/model_provider_factory.py b/src/service_ml_forecast/ml/model_provider_factory.py
index 29c9e3be..19529086 100644
--- a/src/service_ml_forecast/ml/model_provider_factory.py
+++ b/src/service_ml_forecast/ml/model_provider_factory.py
@@ -47,4 +47,19 @@ def create_provider(
                     f"Error: {e!s}. Config details: {config.model_dump_json()}"
                 ) from e
 
+        if config.type == ModelTypeEnum.ITRANSFORMER:
+            try:
+                from service_ml_forecast.ml.itransformer_model_provider import ITransformerModelProvider  # noqa: PLC0415
+                return cast("ModelProvider[Any]", ITransformerModelProvider(config=config))
+            except ImportError as e:
+                raise ImportError(
+                    "torch is required for iTransformer. Install the 'itransformer' extra: "
+                    "pip install service-ml-forecast[itransformer]"
+                ) from e
+            except Exception as e:
+                raise ValueError(
+                    f"Failed to create iTransformer model provider for config {config.id}. "
+                    f"Error: {e!s}. Config details: {config.model_dump_json()}"
+                ) from e
+
         raise ValueError(f"Unsupported model type: {config.type}. Supported types: {[t.value for t in ModelTypeEnum]}")
diff --git a/src/service_ml_forecast/models/model_config.py b/src/service_ml_forecast/models/model_config.py
index a97f178a..49d961a2 100644
--- a/src/service_ml_forecast/models/model_config.py
+++ b/src/service_ml_forecast/models/model_config.py
@@ -162,7 +162,27 @@ class ProphetModelConfig(BaseModelConfig):
     )
 
 
+class ITransformerModelConfig(BaseModelConfig):
+    """iTransformer specific configuration."""
+
+    type: Literal[ModelTypeEnum.ITRANSFORMER] = ModelTypeEnum.ITRANSFORMER
+    seq_len: int = Field(
+        default=96,
+        description="Input lookback window in number of datapoints. Must be >= 2 × forecast_periods.",
+        ge=2,
+    )
+    d_model: int = Field(default=128, description="Transformer embedding dimension.", ge=1)
+    n_heads: int = Field(default=4, description="Number of attention heads.", ge=1)
+    n_layers: int = Field(default=2, description="Number of encoder layers.", ge=1)
+    d_ff: int = Field(default=256, description="Feed-forward hidden dimension.", ge=1)
+    dropout: float = Field(default=0.1, description="Dropout rate.", ge=0.0, le=1.0)
+    epochs: int = Field(default=30, description="Training epochs.", ge=1)
+    batch_size: int = Field(default=64, description="Training batch size.", ge=1)
+    lr: float = Field(default=1e-3, description="AdamW learning rate.", gt=0.0)
+    val_split: float = Field(default=0.2, description="Fraction of data held out for validation.", ge=0.0, lt=1.0)
+
+
 ModelConfig = Annotated[
-    ProphetModelConfig,
+    ProphetModelConfig | ITransformerModelConfig,
     Field(discriminator="type"),
 ]
diff --git a/src/service_ml_forecast/models/model_type.py b/src/service_ml_forecast/models/model_type.py
index d0a5a559..d8d02079 100644
--- a/src/service_ml_forecast/models/model_type.py
+++ b/src/service_ml_forecast/models/model_type.py
@@ -20,3 +20,4 @@
 
 class ModelTypeEnum(str, Enum):
     PROPHET = "prophet"
+    ITRANSFORMER = "itransformer"
diff --git a/tests/conftest.py b/tests/conftest.py
index a448ae0c..fdc796ec 100644
--- a/tests/conftest.py
+++ b/tests/conftest.py
@@ -19,7 +19,7 @@
 from service_ml_forecast.config import DIRS
 from service_ml_forecast.dependencies import get_config_service
 from service_ml_forecast.logging_config import LOGGING_CONFIG
-from service_ml_forecast.models.model_config import ProphetModelConfig
+from service_ml_forecast.models.model_config import ITransformerModelConfig, ProphetModelConfig
 from service_ml_forecast.services.model_config_service import ModelConfigService
 from service_ml_forecast.services.model_storage_service import ModelStorageService
 from service_ml_forecast.services.openremote_service import OpenRemoteService
@@ -97,6 +97,13 @@ def model_storage() -> ModelStorageService:
     return ModelStorageService()
 
 
+@pytest.fixture
+def itransformer_config() -> ITransformerModelConfig:
+    config_path = Path(__file__).parent / "ml/resources/itransformer-tariff-config.json"
+    with open(config_path) as f:
+        return ITransformerModelConfig(**json.load(f))
+
+
 @pytest.fixture
 def prophet_basic_config() -> ProphetModelConfig:
     config_path = Path(__file__).parent / "ml/resources/prophet-windspeed-config.json"
diff --git a/tests/ml/resources/itransformer-tariff-config.json b/tests/ml/resources/itransformer-tariff-config.json
new file mode 100644
index 00000000..0a9bc1fc
--- /dev/null
+++ b/tests/ml/resources/itransformer-tariff-config.json
@@ -0,0 +1,25 @@
+{
+    "id": "a1b2c3d4-e5f6-7890-abcd-ef1234567890",
+    "realm": "master",
+    "name": "iTransformer Tariff Forecast",
+    "enabled": true,
+    "type": "itransformer",
+    "target": {
+      "asset_id": "41ORIhkDVAlT97dYGUD9n5",
+      "attribute_name": "tariff",
+      "training_data_period": "P6M"
+    },
+    "forecast_interval": "PT120S",
+    "forecast_periods": 24,
+    "forecast_frequency": "1h",
+    "seq_len": 48,
+    "d_model": 16,
+    "n_heads": 2,
+    "n_layers": 1,
+    "d_ff": 32,
+    "dropout": 0.1,
+    "epochs": 2,
+    "batch_size": 16,
+    "lr": 0.001,
+    "val_split": 0.2
+}
diff --git a/tests/ml/test_itransformer_model_provider.py b/tests/ml/test_itransformer_model_provider.py
new file mode 100644
index 00000000..6df605a0
--- /dev/null
+++ b/tests/ml/test_itransformer_model_provider.py
@@ -0,0 +1,103 @@
+# Copyright 2025, OpenRemote Inc.
+#
+# This program is free software: you can redistribute it and/or modify
+# it under the terms of the GNU Affero General Public License as
+# published by the Free Software Foundation, either version 3 of the
+# License, or (at your option) any later version.
+#
+# This program is distributed in the hope that it will be useful,
+# but WITHOUT ANY WARRANTY; without even the implied warranty of
+# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+# GNU Affero General Public License for more details.
+#
+# You should have received a copy of the GNU Affero General Public License
+# along with this program. If not, see <https://www.gnu.org/licenses/>.
+#
+# SPDX-License-Identifier: AGPL-3.0-or-later
+
+from pydantic import TypeAdapter
+
+from openremote_client import AssetDatapoint
+
+from service_ml_forecast.ml.model_provider_factory import ModelProviderFactory
+from service_ml_forecast.models.feature_data_wrappers import AssetFeatureDatapoints, TrainingDataSet
+from service_ml_forecast.models.model_config import ITransformerModelConfig, ModelConfig
+
+
+def test_train_and_predict(
+    itransformer_config: ITransformerModelConfig,
+    windspeed_mock_datapoints: list[AssetDatapoint],
+) -> None:
+    """Verifies that iTransformer trains, saves, loads, and forecasts correctly."""
+    provider = ModelProviderFactory.create_provider(itransformer_config)
+
+    model = provider.train_model(
+        TrainingDataSet(
+            target=AssetFeatureDatapoints(
+                feature_name=itransformer_config.target.attribute_name,
+                datapoints=windspeed_mock_datapoints,
+            ),
+        ),
+    )
+    assert model is not None
+
+    provider.save_model(model)
+    assert itransformer_config.id is not None
+    assert provider.load_model(itransformer_config.id) is not None
+
+    forecast = provider.generate_forecast()
+    assert forecast is not None
+    assert forecast.datapoints is not None
+    assert len(forecast.datapoints) == itransformer_config.forecast_periods
+
+
+def test_train_returns_none_on_insufficient_data(
+    itransformer_config: ITransformerModelConfig,
+) -> None:
+    """train_model returns None when datapoints < seq_len + forecast_periods + 1."""
+    provider = ModelProviderFactory.create_provider(itransformer_config)
+
+    too_few = [AssetDatapoint(x=i * 3_600_000, y=float(i)) for i in range(5)]
+    model = provider.train_model(
+        TrainingDataSet(
+            target=AssetFeatureDatapoints(
+                feature_name=itransformer_config.target.attribute_name,
+                datapoints=too_few,
+            ),
+        ),
+    )
+    assert model is None
+
+
+def test_model_config_union_parses_itransformer() -> None:
+    """ModelConfig discriminated union correctly deserializes an iTransformer config dict."""
+    adapter = TypeAdapter(ModelConfig)
+    raw = {
+        "type": "itransformer",
+        "realm": "master",
+        "name": "Test",
+        "target": {"asset_id": "41ORIhkDVAlT97dYGUD9n5", "attribute_name": "tariff"},
+        "forecast_interval": "PT1H",
+        "forecast_periods": 24,
+        "forecast_frequency": "1h",
+    }
+    config = adapter.validate_python(raw)
+    assert isinstance(config, ITransformerModelConfig)
+    assert config.seq_len == 96
+
+
+def test_train_returns_none_on_empty_data(
+    itransformer_config: ITransformerModelConfig,
+) -> None:
+    """train_model returns None when no datapoints are provided."""
+    provider = ModelProviderFactory.create_provider(itransformer_config)
+
+    model = provider.train_model(
+        TrainingDataSet(
+            target=AssetFeatureDatapoints(
+                feature_name=itransformer_config.target.attribute_name,
+                datapoints=[],
+            ),
+        ),
+    )
+    assert model is None
diff --git a/uv.lock b/uv.lock
index 83f7793e..b259a51a 100644
--- a/uv.lock
+++ b/uv.lock
@@ -1,5 +1,5 @@
 version = 1
-revision = 1
+revision = 3
 requires-python = ">=3.13"
 
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+    { url = "https://files.pythonhosted.org/packages/31/08/aa4fdfb71f7de5176385bd9e90852eaf6b5d622735020ad600f2bab54385/typing_inspection-0.4.0-py3-none-any.whl", hash = "sha256:50e72559fcd2a6367a19f7a7e610e6afcb9fac940c650290eed893d61386832f", size = 14125, upload-time = "2025-02-25T17:27:57.754Z" },
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+sdist = { url = "https://files.pythonhosted.org/packages/95/32/1a225d6164441be760d75c2c42e2780dc0873fe382da3e98a2e1e48361e5/tzdata-2025.2.tar.gz", hash = "sha256:b60a638fcc0daffadf82fe0f57e53d06bdec2f36c4df66280ae79bce6bd6f2b9", size = 196380, upload-time = "2025-03-23T13:54:43.652Z" }
 wheels = [
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+    { url = "https://files.pythonhosted.org/packages/5c/23/c7abc0ca0a1526a0774eca151daeb8de62ec457e77262b66b359c3c7679e/tzdata-2025.2-py2.py3-none-any.whl", hash = "sha256:1a403fada01ff9221ca8044d701868fa132215d84beb92242d9acd2147f667a8", size = 347839, upload-time = "2025-03-23T13:54:41.845Z" },
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@@ -1066,9 +1456,9 @@ source = { registry = "https://pypi.org/simple" }
 dependencies = [
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-sdist = { url = "https://files.pythonhosted.org/packages/8b/2e/c14812d3d4d9cd1773c6be938f89e5735a1f11a9f184ac3639b93cef35d5/tzlocal-5.3.1.tar.gz", hash = "sha256:cceffc7edecefea1f595541dbd6e990cb1ea3d19bf01b2809f362a03dd7921fd", size = 30761 }
+sdist = { url = "https://files.pythonhosted.org/packages/8b/2e/c14812d3d4d9cd1773c6be938f89e5735a1f11a9f184ac3639b93cef35d5/tzlocal-5.3.1.tar.gz", hash = "sha256:cceffc7edecefea1f595541dbd6e990cb1ea3d19bf01b2809f362a03dd7921fd", size = 30761, upload-time = "2025-03-05T21:17:41.549Z" }
 wheels = [
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+    { url = "https://files.pythonhosted.org/packages/c2/14/e2a54fabd4f08cd7af1c07030603c3356b74da07f7cc056e600436edfa17/tzlocal-5.3.1-py3-none-any.whl", hash = "sha256:eb1a66c3ef5847adf7a834f1be0800581b683b5608e74f86ecbcef8ab91bb85d", size = 18026, upload-time = "2025-03-05T21:17:39.857Z" },
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@@ -1079,7 +1469,7 @@ dependencies = [
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     { name = "h11" },
 ]
-sdist = { url = "https://files.pythonhosted.org/packages/4b/4d/938bd85e5bf2edeec766267a5015ad969730bb91e31b44021dfe8b22df6c/uvicorn-0.34.0.tar.gz", hash = "sha256:404051050cd7e905de2c9a7e61790943440b3416f49cb409f965d9dcd0fa73e9", size = 76568 }
+sdist = { url = "https://files.pythonhosted.org/packages/4b/4d/938bd85e5bf2edeec766267a5015ad969730bb91e31b44021dfe8b22df6c/uvicorn-0.34.0.tar.gz", hash = "sha256:404051050cd7e905de2c9a7e61790943440b3416f49cb409f965d9dcd0fa73e9", size = 76568, upload-time = "2024-12-15T13:33:30.42Z" }
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+    { url = "https://files.pythonhosted.org/packages/61/14/33a3a1352cfa71812a3a21e8c9bfb83f60b0011f5e36f2b1399d51928209/uvicorn-0.34.0-py3-none-any.whl", hash = "sha256:023dc038422502fa28a09c7a30bf2b6991512da7dcdb8fd35fe57cfc154126f4", size = 62315, upload-time = "2024-12-15T13:33:27.467Z" },
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From f7712dc10acdb5dcb7c9ec6eba0cd476338d3985 Mon Sep 17 00:00:00 2001
From: Nazim <i499773@fontysict.nl>
Date: Tue, 16 Jun 2026 10:09:34 +0200
Subject: [PATCH 2/3] Integrate our pre-trained encoder-decoder transformer
 from HuggingFace

---
 docker/Dockerfile                             |  10 +-
 frontend/src/pages/pages-config-editor.ts     |  57 +++-
 frontend/src/services/models.ts               |  20 +-
 pyproject.toml                                |   1 +
 .../ml/_enc_dec_transformer_net.py            | 119 ++++++++
 .../ml/model_provider_factory.py              |  16 +
 .../ml/nl_energy_forecaster_model_provider.py | 284 ++++++++++++++++++
 .../models/model_config.py                    |  46 ++-
 src/service_ml_forecast/models/model_type.py  |   1 +
 .../services/model_scheduler.py               |  16 +-
 uv.lock                                       | 253 +++++++++++++++-
 11 files changed, 800 insertions(+), 23 deletions(-)
 create mode 100644 src/service_ml_forecast/ml/_enc_dec_transformer_net.py
 create mode 100644 src/service_ml_forecast/ml/nl_energy_forecaster_model_provider.py

diff --git a/docker/Dockerfile b/docker/Dockerfile
index 4e8a8d98..e129adb3 100644
--- a/docker/Dockerfile
+++ b/docker/Dockerfile
@@ -24,9 +24,9 @@ WORKDIR /app
 # Version argument for setuptools-scm
 ARG VERSION=0.0.0
 
-# Optional extras to install (e.g. "itransformer" to include torch).
+# Optional extras to install. Comma-separated (e.g. "itransformer,nl-energy-forecaster").
 # Pass --build-arg ML_EXTRAS="" to build a Prophet-only image without torch.
-ARG ML_EXTRAS=itransformer
+ARG ML_EXTRAS=itransformer,nl-energy-forecaster
 
 ENV PYTHONUNBUFFERED=1 \
     PYTHONDONTWRITEBYTECODE=1 \
@@ -53,7 +53,8 @@ COPY packages/ ./packages/
 RUN echo "Installing Python project dependencies..."
 RUN uv lock --no-cache && \
     if [ -n "${ML_EXTRAS}" ]; then \
-        uv sync --no-cache-dir --extra "${ML_EXTRAS}"; \
+        EXTRA_ARGS=$(echo "${ML_EXTRAS}" | tr ',' '\n' | sed 's/^/--extra /') && \
+        uv sync --no-cache-dir $EXTRA_ARGS; \
     else \
         uv sync --no-cache-dir; \
     fi \
@@ -108,7 +109,8 @@ COPY packages/ ./packages/
 # Install project dependencies using uv (include optional extras if set)
 RUN echo "Installing Python project dependencies..."
 RUN if [ -n "${ML_EXTRAS}" ]; then \
-        uv sync --no-cache-dir --extra "${ML_EXTRAS}"; \
+        EXTRA_ARGS=$(echo "${ML_EXTRAS}" | tr ',' '\n' | sed 's/^/--extra /') && \
+        uv sync --no-cache-dir $EXTRA_ARGS; \
     else \
         uv sync --no-cache-dir; \
     fi
diff --git a/frontend/src/pages/pages-config-editor.ts b/frontend/src/pages/pages-config-editor.ts
index 8e6fce13..59d6beab 100644
--- a/frontend/src/pages/pages-config-editor.ts
+++ b/frontend/src/pages/pages-config-editor.ts
@@ -20,7 +20,7 @@ import { customElement, property, state } from 'lit/decorators.js';
 import { map } from 'lit/directives/map.js';
 import { when } from 'lit/directives/when.js';
 import { ModelTypeEnum, ProphetSeasonalityModeEnum } from '../services/models';
-import type { ITransformerModelConfig, ModelConfig, ProphetModelConfig } from '../services/models';
+import type { ITransformerModelConfig, ModelConfig, NLEnergyForecasterModelConfig, ProphetModelConfig } from '../services/models';
 import { APIService } from '../services/api-service';
 import { Router, RouterLocation } from '@vaadin/router';
 import { InputType, OrInputChangedEvent } from '@openremote/or-mwc-components/or-mwc-input';
@@ -75,6 +75,26 @@ const DEFAULT_ITRANSFORMER_FORM_DATA = (realm: string): ITransformerModelConfig
     val_split: 0.2
 });
 
+const DEFAULT_NL_ENERGY_FORECASTER_FORM_DATA = (realm: string): NLEnergyForecasterModelConfig => ({
+    ...BASE_FORM_DEFAULTS,
+    realm,
+    type: ModelTypeEnum.NL_ENERGY_FORECASTER,
+    forecast_periods: 24,
+    forecast_frequency: '1h',
+    feature_mapping: {
+        temperature_2m: '',
+        cloud_cover: '',
+        wind_speed_10m: '',
+        shortwave_radiation: '',
+        total_load: '',
+        generation_forecast: '',
+        Open: '',
+        High: '',
+        Low: '',
+        'Change %': ''
+    }
+});
+
 @customElement('page-config-editor')
 export class PageConfigEditor extends LitElement {
     @consume({ context: realmContext })
@@ -233,6 +253,8 @@ export class PageConfigEditor extends LitElement {
             const newType = value as ModelTypeEnum;
             if (newType === ModelTypeEnum.ITRANSFORMER) {
                 this.formData = { ...DEFAULT_ITRANSFORMER_FORM_DATA(this.realm), name: this.formData.name, target: this.formData.target };
+            } else if (newType === ModelTypeEnum.NL_ENERGY_FORECASTER) {
+                this.formData = { ...DEFAULT_NL_ENERGY_FORECASTER_FORM_DATA(this.realm), name: this.formData.name, target: this.formData.target };
             } else {
                 this.formData = { ...DEFAULT_PROPHET_FORM_DATA(this.realm), name: this.formData.name, target: this.formData.target };
             }
@@ -682,7 +704,7 @@ export class PageConfigEditor extends LitElement {
                                 @or-mwc-input-changed="${this.handleBasicInput}"
                                 label="Model Type"
                                 type="${InputType.SELECT}"
-                                .options="${[['prophet', 'Prophet'], ['itransformer', 'iTransformer']]}"
+                                .options="${[['prophet', 'Prophet'], ['itransformer', 'iTransformer'], ['nl_energy_forecaster', 'NL Energy Forecaster']]}"
                                 .value="${this.formData.type}"
                             >
                             </or-mwc-input>
@@ -782,6 +804,36 @@ export class PageConfigEditor extends LitElement {
                 <!-- Model parameters — dynamic based on model type -->
                 <or-panel heading="PARAMETERS">
                     <div class="column">
+                        ${this.formData.type === ModelTypeEnum.NL_ENERGY_FORECASTER
+                            ? (() => {
+                                const n = this.formData as NLEnergyForecasterModelConfig;
+                                return html`
+                                    <div class="row">
+                                        <p style="margin:0;opacity:0.7;font-size:0.9em;">
+                                            Pre-trained model (Nazim112/nl-energy-forecaster).
+                                            Map each feature column to a regressor in the format
+                                            <em>asset_id.attribute_name</em>.
+                                        </p>
+                                    </div>
+                                    <div class="row">
+                                        <or-mwc-input
+                                            style="flex:1;max-width:100%;"
+                                            type="${InputType.TEXTAREA}"
+                                            name="feature_mapping"
+                                            @or-mwc-input-changed="${(e: OrInputChangedEvent) => {
+                                                try {
+                                                    this.formData = { ...(this.formData as NLEnergyForecasterModelConfig), feature_mapping: JSON.parse(e.detail?.value ?? '{}') };
+                                                } catch (_) { /* ignore invalid JSON while typing */ }
+                                            }}"
+                                            label="Feature mapping (JSON)"
+                                            .value="${JSON.stringify(n.feature_mapping, null, 2)}"
+                                            rows="14"
+                                            required
+                                        ></or-mwc-input>
+                                    </div>
+                                `;
+                              })()
+                            : ''}
                         ${when(
                             this.formData.type === ModelTypeEnum.PROPHET,
                             () => {
@@ -849,6 +901,7 @@ export class PageConfigEditor extends LitElement {
                                 `;
                             },
                             () => {
+                                if (this.formData.type !== ModelTypeEnum.ITRANSFORMER) return html``;
                                 const t = this.formData as ITransformerModelConfig;
                                 return html`
                                     <div class="row">
diff --git a/frontend/src/services/models.ts b/frontend/src/services/models.ts
index f8680a05..d0028c25 100644
--- a/frontend/src/services/models.ts
+++ b/frontend/src/services/models.ts
@@ -23,7 +23,8 @@
  */
 export enum ModelTypeEnum {
     PROPHET = 'prophet',
-    ITRANSFORMER = 'itransformer'
+    ITRANSFORMER = 'itransformer',
+    NL_ENERGY_FORECASTER = 'nl_energy_forecaster'
 }
 
 /**
@@ -203,8 +204,23 @@ export interface ITransformerModelConfig extends BaseModelConfig {
     val_split?: number;
 }
 
+/**
+ * NL energy price forecaster (pre-trained encoder-decoder transformer from HuggingFace).
+ * Inference-only — no local training. Always produces 24-step hourly forecasts.
+ */
+export interface NLEnergyForecasterModelConfig extends BaseModelConfig {
+    type: ModelTypeEnum.NL_ENERGY_FORECASTER;
+    /**
+     * Maps non-time, non-Price FEATURE_COL names to regressor feature_names
+     * (format: '{asset_id}.{attribute_name}').
+     * Required keys: temperature_2m, cloud_cover, wind_speed_10m, shortwave_radiation,
+     * total_load, generation_forecast, Open, High, Low, 'Change %'.
+     */
+    feature_mapping: Record<string, string>;
+}
+
 /**
  * Represents a model configuration, which can be one of the specific model types.
  * This uses a discriminated union based on the 'type' field.
  */
-export type ModelConfig = ProphetModelConfig | ITransformerModelConfig;
+export type ModelConfig = ProphetModelConfig | ITransformerModelConfig | NLEnergyForecasterModelConfig;
diff --git a/pyproject.toml b/pyproject.toml
index 5dd03e78..d26133e5 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -32,6 +32,7 @@ dependencies = [
 
 [project.optional-dependencies]
 itransformer = ["torch>=2.2.0"]
+nl-energy-forecaster = ["torch>=2.2.0", "huggingface_hub>=0.20.0", "scikit-learn>=1.3.0"]
 
 [tool.uv.workspace]
 members = ["packages/*"]
diff --git a/src/service_ml_forecast/ml/_enc_dec_transformer_net.py b/src/service_ml_forecast/ml/_enc_dec_transformer_net.py
new file mode 100644
index 00000000..080d3a28
--- /dev/null
+++ b/src/service_ml_forecast/ml/_enc_dec_transformer_net.py
@@ -0,0 +1,119 @@
+# Copyright 2025, OpenRemote Inc.
+#
+# This program is free software: you can redistribute it and/or modify
+# it under the terms of the GNU Affero General Public License as
+# published by the Free Software Foundation, either version 3 of the
+# License, or (at your option) any later version.
+#
+# This program is distributed in the hope that it will be useful,
+# but WITHOUT ANY WARRANTY; without even the implied warranty of
+# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+# GNU Affero General Public License for more details.
+#
+# You should have received a copy of the GNU Affero General Public License
+# along with this program. If not, see <https://www.gnu.org/licenses/>.
+#
+# SPDX-License-Identifier: AGPL-3.0-or-later
+
+# Vendored from Nazim112/nl-energy-forecaster (originally CitrusBoy/exp2c-energy-forecaster).
+# Kept local to avoid a hard dependency on the HF repo's src/ layout at runtime.
+
+from __future__ import annotations
+
+import math
+from dataclasses import dataclass, field
+
+import torch
+import torch.nn as nn
+
+
+@dataclass
+class TransformerConfig:
+    d_model: int = 256
+    nhead: int = 8
+    num_layers: int = 4
+    num_decoder_layers: int | None = None
+    dim_feedforward: int = 1024
+    dropout: float = 0.1
+    activation: str = "gelu"
+    pooling: str = "mean"
+    head_hidden_size: int = 128
+
+
+@dataclass
+class EncDecModelConfig:
+    name: str = "encoder_decoder_transformer"
+    transformer: TransformerConfig | None = field(default=None)
+
+
+class SinusoidalPositionalEncoding(nn.Module):
+    def __init__(self, d_model: int, dropout: float = 0.1, max_len: int = 5000) -> None:
+        super().__init__()
+        self.dropout = nn.Dropout(p=dropout)
+        pe = torch.zeros(max_len, d_model)
+        position = torch.arange(0, max_len).unsqueeze(1).float()
+        div_term = torch.exp(torch.arange(0, d_model, 2).float() * (-math.log(10000.0) / d_model))
+        pe[:, 0::2] = torch.sin(position * div_term)
+        pe[:, 1::2] = torch.cos(position * div_term)
+        self.register_buffer("pe", pe.unsqueeze(0))
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        x = x + self.pe[:, : x.size(1)]  # type: ignore[index]
+        return self.dropout(x)
+
+
+class EncoderDecoderTransformerModel(nn.Module):
+    """Encoder consumes lookback; learned query tokens cross-attend to encoder memory → MLP head."""
+
+    def __init__(
+        self,
+        input_dim: int,
+        horizon: int,
+        input_kind: str,
+        model_config: EncDecModelConfig,
+    ) -> None:
+        super().__init__()
+        if model_config.transformer is None:
+            raise ValueError("Transformer configuration required.")
+
+        cfg = model_config.transformer
+        num_dec = cfg.num_decoder_layers if cfg.num_decoder_layers is not None else cfg.num_layers
+
+        self.input_proj = nn.Linear(input_dim, cfg.d_model)
+        self.pos_enc = SinusoidalPositionalEncoding(cfg.d_model, dropout=cfg.dropout)
+
+        encoder_layer = nn.TransformerEncoderLayer(
+            d_model=cfg.d_model,
+            nhead=cfg.nhead,
+            dim_feedforward=cfg.dim_feedforward,
+            dropout=cfg.dropout,
+            batch_first=True,
+            norm_first=True,
+        )
+        self.encoder = nn.TransformerEncoder(encoder_layer, num_layers=cfg.num_layers)
+
+        decoder_layer = nn.TransformerDecoderLayer(
+            d_model=cfg.d_model,
+            nhead=cfg.nhead,
+            dim_feedforward=cfg.dim_feedforward,
+            dropout=cfg.dropout,
+            batch_first=True,
+            norm_first=True,
+        )
+        self.decoder = nn.TransformerDecoder(decoder_layer, num_layers=num_dec)
+
+        self.query_tokens = nn.Parameter(torch.randn(1, horizon, cfg.d_model))
+
+        self.head = nn.Sequential(
+            nn.Linear(cfg.d_model, cfg.head_hidden_size),
+            nn.ReLU(),
+            nn.Dropout(cfg.dropout),
+            nn.Linear(cfg.head_hidden_size, 1),
+        )
+
+    def forward(self, inputs: torch.Tensor) -> torch.Tensor:
+        B = inputs.size(0)
+        memory = self.encoder(self.pos_enc(self.input_proj(inputs)))
+        queries = self.query_tokens.expand(B, -1, -1)
+        out = self.decoder(tgt=queries, memory=memory)
+        return self.head(out).squeeze(-1)  # (B, horizon)
diff --git a/src/service_ml_forecast/ml/model_provider_factory.py b/src/service_ml_forecast/ml/model_provider_factory.py
index 19529086..c56be5f5 100644
--- a/src/service_ml_forecast/ml/model_provider_factory.py
+++ b/src/service_ml_forecast/ml/model_provider_factory.py
@@ -62,4 +62,20 @@ def create_provider(
                     f"Error: {e!s}. Config details: {config.model_dump_json()}"
                 ) from e
 
+        if config.type == ModelTypeEnum.NL_ENERGY_FORECASTER:
+            try:
+                from service_ml_forecast.ml.nl_energy_forecaster_model_provider import NLEnergyForecasterModelProvider  # noqa: PLC0415
+                return cast("ModelProvider[Any]", NLEnergyForecasterModelProvider(config=config))
+            except ImportError as e:
+                raise ImportError(
+                    "torch, huggingface_hub, and scikit-learn are required for NL energy forecaster. "
+                    "Install the 'nl-energy-forecaster' extra: "
+                    "pip install service-ml-forecast[nl-energy-forecaster]"
+                ) from e
+            except Exception as e:
+                raise ValueError(
+                    f"Failed to create NL energy forecaster provider for config {config.id}. "
+                    f"Error: {e!s}. Config details: {config.model_dump_json()}"
+                ) from e
+
         raise ValueError(f"Unsupported model type: {config.type}. Supported types: {[t.value for t in ModelTypeEnum]}")
diff --git a/src/service_ml_forecast/ml/nl_energy_forecaster_model_provider.py b/src/service_ml_forecast/ml/nl_energy_forecaster_model_provider.py
new file mode 100644
index 00000000..8c333b84
--- /dev/null
+++ b/src/service_ml_forecast/ml/nl_energy_forecaster_model_provider.py
@@ -0,0 +1,284 @@
+# Copyright 2025, OpenRemote Inc.
+#
+# This program is free software: you can redistribute it and/or modify
+# it under the terms of the GNU Affero General Public License as
+# published by the Free Software Foundation, either version 3 of the
+# License, or (at your option) any later version.
+#
+# This program is distributed in the hope that it will be useful,
+# but WITHOUT ANY WARRANTY; without even the implied warranty of
+# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+# GNU Affero General Public License for more details.
+#
+# You should have received a copy of the GNU Affero General Public License
+# along with this program. If not, see <https://www.gnu.org/licenses/>.
+#
+# SPDX-License-Identifier: AGPL-3.0-or-later
+
+from __future__ import annotations
+
+import base64
+import io
+import logging
+from datetime import datetime
+from pathlib import Path
+from uuid import UUID
+
+import joblib
+import numpy as np
+import pandas as pd
+import torch
+from openremote_client import AssetDatapoint
+
+from service_ml_forecast.ml._enc_dec_transformer_net import EncDecModelConfig, EncoderDecoderTransformerModel, TransformerConfig
+from service_ml_forecast.ml.model_provider import ModelProvider
+from service_ml_forecast.models.feature_data_wrappers import ForecastDataSet, ForecastResult, TrainingDataSet
+from service_ml_forecast.models.model_config import NLEnergyForecasterModelConfig
+from service_ml_forecast.services.model_storage_service import ModelStorageService
+
+logger = logging.getLogger(__name__)
+
+HF_REPO_ID = "Nazim112/nl-energy-forecaster"
+INPUT_LEN = 168
+HORIZON = 24
+
+FEATURE_COLS = [
+    "temperature_2m",
+    "cloud_cover",
+    "wind_speed_10m",
+    "shortwave_radiation",
+    "total_load",
+    "generation_forecast",
+    "Price",
+    "Open",
+    "High",
+    "Low",
+    "Change %",
+    "day",
+    "hour_sin",
+    "hour_cos",
+    "day_of_week_sin",
+    "day_of_week_cos",
+    "month_sin",
+    "month_cos",
+    "quarter_sin",
+    "quarter_cos",
+    "weekend_sin",
+    "weekend_cos",
+]
+
+_TIME_FEATURE_COLS = frozenset({
+    "day", "hour_sin", "hour_cos", "day_of_week_sin", "day_of_week_cos",
+    "month_sin", "month_cos", "quarter_sin", "quarter_cos", "weekend_sin", "weekend_cos",
+})
+
+_MODEL_FILE_EXT = "pt"
+
+NLEnergyForecasterBundle = dict
+
+
+class NLEnergyForecasterModelProvider(ModelProvider[NLEnergyForecasterBundle]):
+    """Inference-only provider for the pre-trained NL energy price forecaster on HuggingFace.
+
+    Training (sync) job downloads the model from HF and stores the last INPUT_LEN hours of
+    feature data in the bundle. Forecast job loads the bundle and runs inference.
+    Model repo: https://huggingface.co/Nazim112/nl-energy-forecaster
+    """
+
+    def __init__(self, config: NLEnergyForecasterModelConfig) -> None:
+        self.config = config
+        self.model_storage_service = ModelStorageService()
+        self._device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+
+    def train_model(self, training_dataset: TrainingDataSet) -> NLEnergyForecasterBundle | None:
+        """Downloads the pre-trained model from HuggingFace and captures the feature window."""
+        logger.info(f"Syncing NL energy forecaster {self.config.id} from HuggingFace ({HF_REPO_ID})")
+
+        try:
+            from huggingface_hub import snapshot_download  # noqa: PLC0415
+            repo_dir = Path(snapshot_download(HF_REPO_ID))
+        except Exception as e:
+            logger.error(f"Failed to download model from HuggingFace: {e}")
+            return None
+
+        checkpoint = torch.load(repo_dir / "best_model.pt", map_location="cpu", weights_only=False)
+        scalers = joblib.load(repo_dir / "scalers.joblib")
+
+        # Build last_window: col_name → [(ts_ms, value), ...]
+        last_window: dict[str, list[tuple[int, float]]] = {}
+
+        price_pts = sorted(training_dataset.target.datapoints, key=lambda p: p.x)
+        last_window["Price"] = [(int(p.x), float(p.y)) for p in price_pts[-INPUT_LEN:]]
+
+        if training_dataset.regressors:
+            # Reverse map: regressor feature_name → FEATURE_COL name
+            reverse_map = {v: k for k, v in self.config.feature_mapping.items()}
+            for reg in training_dataset.regressors:
+                col_name = reverse_map.get(reg.feature_name)
+                if col_name and col_name in FEATURE_COLS and col_name not in _TIME_FEATURE_COLS:
+                    sorted_pts = sorted(reg.datapoints, key=lambda p: p.x)
+                    last_window[col_name] = [(int(p.x), float(p.y)) for p in sorted_pts[-INPUT_LEN:]]
+
+        missing_cols = [
+            c for c in FEATURE_COLS
+            if c not in _TIME_FEATURE_COLS and c not in last_window
+        ]
+        if missing_cols:
+            logger.warning(
+                f"NL energy forecaster {self.config.id}: missing feature data for {missing_cols}. "
+                "Forecasts will use zeros for these features."
+            )
+
+        logger.info(f"NL energy forecaster {self.config.id} sync complete")
+        return {
+            "model_state_dict": checkpoint["model_state_dict"],
+            "checkpoint_config": checkpoint["config"],
+            "input_dim": checkpoint["input_dim"],
+            "horizon": checkpoint["horizon"],
+            "input_kind": checkpoint.get("input_kind", "sequence"),
+            "target_scaler": scalers["target_scaler"],
+            "feature_scaler": scalers["feature_scaler"],
+            "last_window": last_window,
+        }
+
+    def save_model(self, model: NLEnergyForecasterBundle) -> None:
+        buf = io.BytesIO()
+        torch.save(model, buf)
+        encoded = base64.b64encode(buf.getvalue()).decode("utf-8")
+        self.model_storage_service.save(encoded, self.config.id, model_file_extension=_MODEL_FILE_EXT)
+        logger.info(f"Saved NL energy forecaster bundle -- {self.config.id}")
+
+    def load_model(self, model_config_id: UUID) -> NLEnergyForecasterBundle:
+        encoded = self.model_storage_service.get(model_config_id, model_file_extension=_MODEL_FILE_EXT)
+        buf = io.BytesIO(base64.b64decode(encoded))
+        return torch.load(buf, map_location="cpu", weights_only=False)
+
+    def generate_forecast(self, forecast_dataset: ForecastDataSet | None = None) -> ForecastResult:
+        bundle = self.load_model(self.config.id)
+
+        cfg_dict = bundle["checkpoint_config"]["model"]["transformer"]
+        transformer_cfg = TransformerConfig(
+            d_model=cfg_dict["d_model"],
+            nhead=cfg_dict["nhead"],
+            num_layers=cfg_dict["num_layers"],
+            num_decoder_layers=cfg_dict.get("num_decoder_layers", cfg_dict["num_layers"]),
+            dim_feedforward=cfg_dict["dim_feedforward"],
+            dropout=cfg_dict["dropout"],
+            activation=cfg_dict.get("activation", "gelu"),
+            pooling=cfg_dict.get("pooling") or cfg_dict.get("pool", "mean"),
+            head_hidden_size=cfg_dict["head_hidden_size"],
+        )
+        model_cfg = EncDecModelConfig(name="encoder_decoder_transformer", transformer=transformer_cfg)
+
+        net = EncoderDecoderTransformerModel(
+            input_dim=bundle["input_dim"],
+            horizon=bundle["horizon"],
+            input_kind=bundle["input_kind"],
+            model_config=model_cfg,
+        ).to(self._device)
+        net.load_state_dict(bundle["model_state_dict"])
+        net.eval()
+
+        last_window: dict[str, list[tuple[int, float]]] = bundle["last_window"]
+        X = _build_feature_matrix(last_window)  # (INPUT_LEN, 22)
+
+        fs = bundle["feature_scaler"]
+        X_scaled = fs.transform(X) if fs is not None else X
+
+        tensor = torch.from_numpy(X_scaled).unsqueeze(0).to(self._device)  # (1, INPUT_LEN, 22)
+        with torch.no_grad():
+            out = net(tensor).squeeze(0).cpu().numpy()  # (HORIZON,)
+
+        ts_scaler = bundle["target_scaler"]
+        if ts_scaler is not None:
+            out = ts_scaler.inverse_transform(out.reshape(1, -1))[0]
+
+        price_data = sorted(last_window["Price"], key=lambda p: p[0])
+        last_ts = price_data[-1][0]
+        freq_ms = _freq_to_ms(self.config.forecast_frequency)
+
+        datapoints = [
+            AssetDatapoint(x=last_ts + (i + 1) * freq_ms, y=float(v))
+            for i, v in enumerate(out)
+        ]
+        return ForecastResult(
+            asset_id=self.config.target.asset_id,
+            attribute_name=self.config.target.attribute_name,
+            datapoints=datapoints,
+        )
+
+
+def _build_feature_matrix(last_window: dict[str, list[tuple[int, float]]]) -> np.ndarray:
+    """Assemble (INPUT_LEN, 22) feature matrix in FEATURE_COLS order."""
+    price_data = sorted(last_window.get("Price", []), key=lambda p: p[0])
+    n = min(len(price_data), INPUT_LEN)
+    if n == 0:
+        raise ValueError("No Price data in bundle — model sync must run before forecasting")
+
+    price_data = price_data[-INPUT_LEN:]
+    timestamps_ms = np.array([p[0] for p in price_data], dtype=np.int64)
+
+    result = np.zeros((INPUT_LEN, len(FEATURE_COLS)), dtype=np.float32)
+    pad = INPUT_LEN - n  # zero-pad at start when fewer than INPUT_LEN points
+
+    for col_idx, col_name in enumerate(FEATURE_COLS):
+        if col_name in _TIME_FEATURE_COLS:
+            computed = _compute_time_feature(col_name, timestamps_ms)
+            result[pad:, col_idx] = computed
+        else:
+            series_data = sorted(last_window.get(col_name, []), key=lambda p: p[0])
+            if not series_data:
+                continue
+            data_ts = np.array([p[0] for p in series_data], dtype=np.int64)
+            data_vals = np.array([p[1] for p in series_data], dtype=np.float32)
+            # Nearest past-or-equal lookup for each reference timestamp
+            indices = np.searchsorted(data_ts, timestamps_ms, side="right") - 1
+            indices = np.clip(indices, 0, len(data_vals) - 1)
+            result[pad:, col_idx] = data_vals[indices]
+
+    return result
+
+
+def _compute_time_feature(col_name: str, timestamps_ms: np.ndarray) -> np.ndarray:
+    dts = [datetime.fromtimestamp(int(ts) / 1000) for ts in timestamps_ms]
+    if col_name == "day":
+        return np.array([dt.day for dt in dts], dtype=np.float32)
+    if col_name == "hour_sin":
+        h = np.array([dt.hour for dt in dts], dtype=np.float32)
+        return np.sin(2 * np.pi * h / 24)
+    if col_name == "hour_cos":
+        h = np.array([dt.hour for dt in dts], dtype=np.float32)
+        return np.cos(2 * np.pi * h / 24)
+    if col_name == "day_of_week_sin":
+        d = np.array([dt.weekday() for dt in dts], dtype=np.float32)
+        return np.sin(2 * np.pi * d / 7)
+    if col_name == "day_of_week_cos":
+        d = np.array([dt.weekday() for dt in dts], dtype=np.float32)
+        return np.cos(2 * np.pi * d / 7)
+    if col_name == "month_sin":
+        m = np.array([dt.month for dt in dts], dtype=np.float32)
+        return np.sin(2 * np.pi * m / 12)
+    if col_name == "month_cos":
+        m = np.array([dt.month for dt in dts], dtype=np.float32)
+        return np.cos(2 * np.pi * m / 12)
+    if col_name == "quarter_sin":
+        q = np.array([(dt.month - 1) // 3 + 1 for dt in dts], dtype=np.float32)
+        return np.sin(2 * np.pi * q / 4)
+    if col_name == "quarter_cos":
+        q = np.array([(dt.month - 1) // 3 + 1 for dt in dts], dtype=np.float32)
+        return np.cos(2 * np.pi * q / 4)
+    if col_name == "weekend_sin":
+        w = np.array([1.0 if dt.weekday() >= 5 else 0.0 for dt in dts], dtype=np.float32)
+        return np.sin(np.pi * w)
+    if col_name == "weekend_cos":
+        w = np.array([1.0 if dt.weekday() >= 5 else 0.0 for dt in dts], dtype=np.float32)
+        return np.cos(np.pi * w)
+    raise ValueError(f"Unknown time feature: {col_name}")
+
+
+def _freq_to_ms(frequency: str) -> int:
+    offset = pd.tseries.frequencies.to_offset(frequency)
+    if offset is None:
+        raise ValueError(f"Unrecognised forecast_frequency: {frequency!r}")
+    nanos: int = int(pd.Timedelta(offset).value)  # type: ignore[arg-type]
+    return nanos // 1_000_000
diff --git a/src/service_ml_forecast/models/model_config.py b/src/service_ml_forecast/models/model_config.py
index 49d961a2..1b64cb37 100644
--- a/src/service_ml_forecast/models/model_config.py
+++ b/src/service_ml_forecast/models/model_config.py
@@ -19,7 +19,7 @@
 from typing import Annotated, Literal
 from uuid import UUID, uuid4
 
-from pydantic import BaseModel, Field
+from pydantic import BaseModel, Field, model_validator
 
 from service_ml_forecast.models.model_type import ModelTypeEnum
 
@@ -182,7 +182,49 @@ class ITransformerModelConfig(BaseModelConfig):
     val_split: float = Field(default=0.2, description="Fraction of data held out for validation.", ge=0.0, lt=1.0)
 
 
+_REQUIRED_FEATURE_MAPPING_COLS: frozenset[str] = frozenset({
+    "temperature_2m", "cloud_cover", "wind_speed_10m", "shortwave_radiation",
+    "total_load", "generation_forecast", "Open", "High", "Low", "Change %",
+})
+
+
+class NLEnergyForecasterModelConfig(BaseModelConfig):
+    """Pre-trained NL energy price forecaster (encoder-decoder transformer from HuggingFace).
+
+    Inference-only: no local training. The sync job downloads the model from
+    https://huggingface.co/Nazim112/nl-energy-forecaster and stores the last 168 h of
+    feature data. Produces a fixed 24-step hourly price forecast (EUR/MWh).
+    """
+
+    type: Literal[ModelTypeEnum.NL_ENERGY_FORECASTER] = ModelTypeEnum.NL_ENERGY_FORECASTER
+    feature_mapping: dict[str, str] = Field(
+        description=(
+            "Maps non-time, non-Price FEATURE_COL names to regressor feature_names "
+            "(format: '{asset_id}.{attribute_name}'). "
+            "Required keys: temperature_2m, cloud_cover, wind_speed_10m, shortwave_radiation, "
+            "total_load, generation_forecast, Open, High, Low, 'Change %'. "
+            "Time features (day, hour_sin/cos, day_of_week_sin/cos, month_sin/cos, "
+            "quarter_sin/cos, weekend_sin/cos) are computed automatically. "
+            "'Price' is always taken from the target attribute."
+        )
+    )
+
+    @model_validator(mode="after")
+    def validate_nl_config(self) -> "NLEnergyForecasterModelConfig":
+        if self.forecast_periods != 24:
+            raise ValueError(
+                f"NL energy forecaster always produces exactly 24 forecast periods "
+                f"(HORIZON=24), got forecast_periods={self.forecast_periods}"
+            )
+        missing = _REQUIRED_FEATURE_MAPPING_COLS - set(self.feature_mapping.keys())
+        if missing:
+            raise ValueError(
+                f"feature_mapping is missing required keys: {sorted(missing)}"
+            )
+        return self
+
+
 ModelConfig = Annotated[
-    ProphetModelConfig | ITransformerModelConfig,
+    ProphetModelConfig | ITransformerModelConfig | NLEnergyForecasterModelConfig,
     Field(discriminator="type"),
 ]
diff --git a/src/service_ml_forecast/models/model_type.py b/src/service_ml_forecast/models/model_type.py
index d8d02079..a9ac5a1b 100644
--- a/src/service_ml_forecast/models/model_type.py
+++ b/src/service_ml_forecast/models/model_type.py
@@ -21,3 +21,4 @@
 class ModelTypeEnum(str, Enum):
     PROPHET = "prophet"
     ITRANSFORMER = "itransformer"
+    NL_ENERGY_FORECASTER = "nl_energy_forecaster"
diff --git a/src/service_ml_forecast/services/model_scheduler.py b/src/service_ml_forecast/services/model_scheduler.py
index 9a408a59..865de3c1 100644
--- a/src/service_ml_forecast/services/model_scheduler.py
+++ b/src/service_ml_forecast/services/model_scheduler.py
@@ -27,6 +27,7 @@
 from service_ml_forecast.common.time_util import TimeUtil
 from service_ml_forecast.ml.model_provider_factory import ModelProviderFactory
 from service_ml_forecast.models.model_config import ModelConfig
+from service_ml_forecast.models.model_type import ModelTypeEnum
 from service_ml_forecast.services.model_config_service import ModelConfigService
 from service_ml_forecast.services.openremote_service import OpenRemoteService
 
@@ -230,12 +231,15 @@ def _model_forecast_job(config: ModelConfig, data_service: OpenRemoteService) ->
     forecast_dataset = data_service.get_forecast_dataset(config)
 
     if config.regressors is not None and forecast_dataset is None:
-        logger.error(
-            f"Cannot forecast model {config.id} - config has regressors but no forecast dataset. "
-            f"Asset ID: {config.target.asset_id}, Attribute: {config.target.attribute_name}, "
-            f"Regressors: {', '.join(r.attribute_name for r in config.regressors)}"
-        )
-        return
+        # NL energy forecaster is inference-only and uses its stored bundle — it does not
+        # require live predicted regressor data, so we let it proceed with forecast_dataset=None.
+        if config.type != ModelTypeEnum.NL_ENERGY_FORECASTER:
+            logger.error(
+                f"Cannot forecast model {config.id} - config has regressors but no forecast dataset. "
+                f"Asset ID: {config.target.asset_id}, Attribute: {config.target.attribute_name}, "
+                f"Regressors: {', '.join(r.attribute_name for r in config.regressors)}"
+            )
+            return
 
     # Generate the forecast
     forecast = provider.generate_forecast(forecast_dataset)
diff --git a/uv.lock b/uv.lock
index b259a51a..befe1d9b 100644
--- a/uv.lock
+++ b/uv.lock
@@ -17,6 +17,15 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/37/d7/15d67e05b235d1ed8c3ce61688fe4d84130e72af1657acadfaac3479f4cf/aiocache-0.12.3-py2.py3-none-any.whl", hash = "sha256:889086fc24710f431937b87ad3720a289f7fc31c4fd8b68e9f918b9bacd8270d", size = 28199, upload-time = "2024-09-25T13:20:22.688Z" },
 ]
 
+[[package]]
+name = "annotated-doc"
+version = "0.0.4"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/57/ba/046ceea27344560984e26a590f90bc7f4a75b06701f653222458922b558c/annotated_doc-0.0.4.tar.gz", hash = "sha256:fbcda96e87e9c92ad167c2e53839e57503ecfda18804ea28102353485033faa4", size = 7288, upload-time = "2025-11-10T22:07:42.062Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/1e/d3/26bf1008eb3d2daa8ef4cacc7f3bfdc11818d111f7e2d0201bc6e3b49d45/annotated_doc-0.0.4-py3-none-any.whl", hash = "sha256:571ac1dc6991c450b25a9c2d84a3705e2ae7a53467b5d111c24fa8baabbed320", size = 5303, upload-time = "2025-11-10T22:07:40.673Z" },
+]
+
 [[package]]
 name = "annotated-types"
 version = "0.7.0"
@@ -98,14 +107,14 @@ wheels = [
 
 [[package]]
 name = "click"
-version = "8.1.8"
+version = "8.4.1"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "colorama", marker = "sys_platform == 'win32'" },
 ]
-sdist = { url = "https://files.pythonhosted.org/packages/b9/2e/0090cbf739cee7d23781ad4b89a9894a41538e4fcf4c31dcdd705b78eb8b/click-8.1.8.tar.gz", hash = "sha256:ed53c9d8990d83c2a27deae68e4ee337473f6330c040a31d4225c9574d16096a", size = 226593, upload-time = "2024-12-21T18:38:44.339Z" }
+sdist = { url = "https://files.pythonhosted.org/packages/9b/98/518d8e5081007684232226f475082b30087d0f585e8457db087298259f49/click-8.4.1.tar.gz", hash = "sha256:918b5633eddf6b41c32d4f454bf0de810065c74e3f7dbf8ee5452f8be88d3e96", size = 353007, upload-time = "2026-05-22T04:08:37.769Z" }
 wheels = [
-    { url = "https://files.pythonhosted.org/packages/7e/d4/7ebdbd03970677812aac39c869717059dbb71a4cfc033ca6e5221787892c/click-8.1.8-py3-none-any.whl", hash = "sha256:63c132bbbed01578a06712a2d1f497bb62d9c1c0d329b7903a866228027263b2", size = 98188, upload-time = "2024-12-21T18:38:41.666Z" },
+    { url = "https://files.pythonhosted.org/packages/c7/0d/67e5b4109ea4a837e80daa87c2c696711955e40449a97e8926672534def2/click-8.4.1-py3-none-any.whl", hash = "sha256:482be17c6991b8c19c5429a1e995d9b0efdbb63172824c41f99965dc0ade8ec2", size = 116639, upload-time = "2026-05-22T04:08:35.26Z" },
 ]
 
 [[package]]
@@ -358,6 +367,38 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/95/04/ff642e65ad6b90db43e668d70ffb6736436c7ce41fcc549f4e9472234127/h11-0.14.0-py3-none-any.whl", hash = "sha256:e3fe4ac4b851c468cc8363d500db52c2ead036020723024a109d37346efaa761", size = 58259, upload-time = "2022-09-25T15:39:59.68Z" },
 ]
 
+[[package]]
+name = "hf-xet"
+version = "1.5.0"
+source = { registry = "https://pypi.org/simple" }
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+    { url = "https://files.pythonhosted.org/packages/07/39/338d9219c4e87f3e708f18857ecd24d22a0c3094752393319553096b98af/scipy-1.17.1-cp314-cp314t-win_arm64.whl", hash = "sha256:200e1050faffacc162be6a486a984a0497866ec54149a01270adc8a59b7c7d21", size = 25489165, upload-time = "2026-02-23T00:22:29.563Z" },
+]
+
 [[package]]
 name = "service-ml-forecast"
 source = { editable = "." }
@@ -1199,6 +1397,11 @@ dependencies = [
 itransformer = [
     { name = "torch" },
 ]
+nl-energy-forecaster = [
+    { name = "huggingface-hub" },
+    { name = "scikit-learn" },
+    { name = "torch" },
+]
 
 [package.dev-dependencies]
 dev = [
@@ -1219,6 +1422,7 @@ requires-dist = [
     { name = "cryptography", specifier = ">=44.0.2" },
     { name = "fastapi", specifier = ">=0.115.11" },
     { name = "httpx", specifier = ">=0.28.1" },
+    { name = "huggingface-hub", marker = "extra == 'nl-energy-forecaster'", specifier = ">=0.20.0" },
     { name = "isodate", specifier = ">=0.7.2" },
     { name = "numpy", specifier = ">=2.2.4" },
     { name = "openremote-client", editable = "packages/openremote_client" },
@@ -1230,11 +1434,13 @@ requires-dist = [
     { name = "pyjwt", specifier = ">=2.10.1" },
     { name = "pyyaml", specifier = ">=6.0.2" },
     { name = "pyzmq", specifier = ">=26.3.0" },
+    { name = "scikit-learn", marker = "extra == 'nl-energy-forecaster'", specifier = ">=1.3.0" },
     { name = "tomli", specifier = ">=2.2.1" },
     { name = "torch", marker = "extra == 'itransformer'", specifier = ">=2.2.0" },
+    { name = "torch", marker = "extra == 'nl-energy-forecaster'", specifier = ">=2.2.0" },
     { name = "uvicorn", specifier = ">=0.34.0" },
 ]
-provides-extras = ["itransformer"]
+provides-extras = ["itransformer", "nl-energy-forecaster"]
 
 [package.metadata.requires-dev]
 dev = [
@@ -1270,6 +1476,15 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/f7/14/dd3a6053325e882fe191fb4b42289bbdfabf5f44307c302903a8a3236a0a/setuptools_scm-9.2.0-py3-none-any.whl", hash = "sha256:c551ef54e2270727ee17067881c9687ca2aedf179fa5b8f3fab9e8d73bdc421f", size = 62099, upload-time = "2025-08-16T12:56:37.912Z" },
 ]
 
+[[package]]
+name = "shellingham"
+version = "1.5.4"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/58/15/8b3609fd3830ef7b27b655beb4b4e9c62313a4e8da8c676e142cc210d58e/shellingham-1.5.4.tar.gz", hash = "sha256:8dbca0739d487e5bd35ab3ca4b36e11c4078f3a234bfce294b0a0291363404de", size = 10310, upload-time = "2023-10-24T04:13:40.426Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/e0/f9/0595336914c5619e5f28a1fb793285925a8cd4b432c9da0a987836c7f822/shellingham-1.5.4-py2.py3-none-any.whl", hash = "sha256:7ecfff8f2fd72616f7481040475a65b2bf8af90a56c89140852d1120324e8686", size = 9755, upload-time = "2023-10-24T04:13:38.866Z" },
+]
+
 [[package]]
 name = "six"
 version = "1.17.0"
@@ -1324,6 +1539,15 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/a2/09/77d55d46fd61b4a135c444fc97158ef34a095e5681d0a6c10b75bf356191/sympy-1.14.0-py3-none-any.whl", hash = "sha256:e091cc3e99d2141a0ba2847328f5479b05d94a6635cb96148ccb3f34671bd8f5", size = 6299353, upload-time = "2025-04-27T18:04:59.103Z" },
 ]
 
+[[package]]
+name = "threadpoolctl"
+version = "3.6.0"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/b7/4d/08c89e34946fce2aec4fbb45c9016efd5f4d7f24af8e5d93296e935631d8/threadpoolctl-3.6.0.tar.gz", hash = "sha256:8ab8b4aa3491d812b623328249fab5302a68d2d71745c8a4c719a2fcaba9f44e", size = 21274, upload-time = "2025-03-13T13:49:23.031Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/32/d5/f9a850d79b0851d1d4ef6456097579a9005b31fea68726a4ae5f2d82ddd9/threadpoolctl-3.6.0-py3-none-any.whl", hash = "sha256:43a0b8fd5a2928500110039e43a5eed8480b918967083ea48dc3ab9f13c4a7fb", size = 18638, upload-time = "2025-03-13T13:49:21.846Z" },
+]
+
 [[package]]
 name = "tomli"
 version = "2.2.1"
@@ -1410,6 +1634,21 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/c1/68/fa86e5a39608000f645535b2c124920126327ab731f8c4fafd5b07ff8d4b/triton-3.7.0-cp314-cp314t-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:ce061073102714b725f3660ec6939d94a1da7984b3aa99c921417cae273672f5", size = 201546766, upload-time = "2026-05-07T18:46:42.088Z" },
 ]
 
+[[package]]
+name = "typer"
+version = "0.25.1"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "annotated-doc" },
+    { name = "click" },
+    { name = "rich" },
+    { name = "shellingham" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/e4/51/9aed62104cea109b820bbd6c14245af756112017d309da813ef107d42e7e/typer-0.25.1.tar.gz", hash = "sha256:9616eb8853a09ffeabab1698952f33c6f29ffdbceb4eaeecf571880e8d7664cc", size = 122276, upload-time = "2026-04-30T19:32:16.964Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/3f/f9/2b3ff4e56e5fa7debfaf9eb135d0da96f3e9a1d5b27222223c7296336e5f/typer-0.25.1-py3-none-any.whl", hash = "sha256:75caa44ed46a03fb2dab8808753ffacdbfea88495e74c85a28c5eefcf5f39c89", size = 58409, upload-time = "2026-04-30T19:32:18.271Z" },
+]
+
 [[package]]
 name = "types-pytz"
 version = "2025.2.0.20250326"

From ab525634e4a49ab0e6c43f5873f7168927d4472a Mon Sep 17 00:00:00 2001
From: Nazim <i499773@fontysict.nl>
Date: Sat, 20 Jun 2026 21:23:00 +0200
Subject: [PATCH 3/3] added documentation to changes

---
 docker/docker-compose.yml | 148 +++++++++++++++++++++++++++++---------
 1 file changed, 114 insertions(+), 34 deletions(-)

diff --git a/docker/docker-compose.yml b/docker/docker-compose.yml
index 6c31376b..71b263ad 100644
--- a/docker/docker-compose.yml
+++ b/docker/docker-compose.yml
@@ -1,40 +1,120 @@
-name: service-ml-forecast
+# OpenRemote v3
+#
+# Profile that runs the stack by default on https://localhost using a self-signed SSL certificate,
+# but optionally on https://$OR_HOSTNAME with an auto generated SSL certificate from Letsencrypt.
+#
+# It is configured to use the AWS logging driver.
+#
+volumes:
+  proxy-data:
+  manager-data:
+  postgresql-data:
+  service-ml-forecast-data:
+  energy-price-forecast-data:
 
 services:
-  service-ml-forecast:
-    build:
-      context: ..
-      dockerfile: docker/Dockerfile
-    container_name: ml-forecast
+
+  proxy:
+    image: openremote/proxy:${PROXY_VERSION:-latest}
+    restart: always
+    depends_on:
+      manager:
+        condition: service_healthy
     ports:
-      - "8000:8000"
-    env_file:
-      - ../.env
+      - "80:80" # Needed for SSL generation using letsencrypt
+      - "${OR_SSL_PORT:-443}:443"
+      - "8883:8883"
+      - "127.0.0.1:8404:8404" # Localhost metrics access
+      - "${PRIVATE_IP:-127.0.0.1}:8404:8404" # Allows to also expose metrics on a given IP address,
+                                             # intended to be IP of the interface of the private subnet of the EC2 VM
+    volumes:
+      - proxy-data:/deployment
+      - ./deployment:/data
     environment:
-      # These override .env values with Docker-internal hostnames
-      - ML_OR_URL=https://energy-forecasting-dataset-proxy-1
-      - ML_OR_KEYCLOAK_URL=https://energy-forecasting-dataset-proxy-1/auth
-      - ML_OR_SERVICE_URL=https://localhost
-      - ML_OR_PUBLIC_URL=https://localhost
-      - ML_OR_PUBLIC_KEYCLOAK_URL=https://localhost/auth
-      - ML_VERIFY_SSL=false
-      - ML_API_MIDDLEWARE_KEYCLOAK=false
-      - ML_WEBSERVER_ORIGINS=["https://localhost"]
-      - ML_OR_REALM=master
-      - ML_LOG_LEVEL=${ML_LOG_LEVEL:-INFO}
+      LE_EMAIL: ${OR_EMAIL_ADMIN:-}
+      DOMAINNAME: ${OR_HOSTNAME:-localhost}
+      DOMAINNAMES: ${OR_ADDITIONAL_HOSTNAMES:-}
+      # USE A CUSTOM PROXY CONFIG - COPY FROM https://raw.githubusercontent.com/openremote/proxy/main/haproxy.cfg
+      HAPROXY_CONFIG: '/data/proxy/haproxy.cfg'
+
+  postgresql:
+    restart: always
+    image: openremote/postgresql:${POSTGRESQL_VERSION:-latest-slim}
+    shm_size: 128mb
     volumes:
-      # Model storage
-      - ../deployment/data/models:/app/deployment/data/models
-      # Config storage
-      - ../deployment/data/configs:/app/deployment/data/configs
-    restart: unless-stopped
-    networks:
-      - ml-network
-      - openremote-network
+      - postgresql-data:/var/lib/postgresql/data
+      - manager-data:/storage
 
-networks:
-  ml-network:
-    driver: bridge
-  openremote-network:
-    external: true
-    name: energy-forecasting-dataset_default
+  keycloak:
+    restart: always
+    image: openremote/keycloak:${KEYCLOAK_VERSION:-latest}
+    depends_on:
+      postgresql:
+        condition: service_healthy
+    volumes:
+      - ./deployment:/deployment
+    environment:
+      KEYCLOAK_ADMIN_PASSWORD: ${OR_ADMIN_PASSWORD:-secret}
+      KC_HOSTNAME: ${OR_HOSTNAME:-localhost}
+      KC_HOSTNAME_PORT: ${OR_SSL_PORT:--1}
+      # KEYCLOAK_ISSUER_BASE_URI: 'https://${OR_HOSTNAME:-localhost}/auth'
+
+  ml-forecast:
+    image: nazim112/service-ml-forecast:latest
+    restart: always
+    environment:
+      ML_LOG_LEVEL: INFO
+      ML_ENVIRONMENT: production
+      ML_WEBSERVER_ORIGINS: '["https://localhost"]'
+      ML_OR_URL: http://manager:8080
+      ML_OR_KEYCLOAK_URL: http://keycloak:8080/auth
+      ML_OR_PUBLIC_URL: https://localhost
+      ML_OR_PUBLIC_KEYCLOAK_URL: https://localhost/auth
+      ML_VERIFY_SSL: "false"
+      ML_API_MIDDLEWARE_KEYCLOAK: "false"
+      ML_OR_SERVICE_USER: YOUR_SERVICE_USER
+      ML_OR_SERVICE_USER_SECRET: YOUR_SERVICE_USER_SECRET
+      ML_OR_SERVICE_URL: https://localhost
+      ML_OR_KEYCLOAK_VALID_ISSUERS: "[\"https://localhost/auth/realms/master\"]"
+      OR_HOSTNAME: localhost
+      OR_SSL_PORT: -1
+    depends_on:
+      keycloak:
+        condition: service_healthy
+    volumes:
+      - service-ml-forecast-data:/app/deployment/data
+
+  manager:
+#    privileged: true
+    restart: always
+    image: openremote/manager:${MANAGER_VERSION:-latest}
+    depends_on:
+      keycloak:
+        condition: service_healthy
+    ports:
+      - "127.0.0.1:8405:8405" # Localhost metrics access
+      - "${PRIVATE_IP:-127.0.0.1}:8405:8405" # Allows to also expose metrics on a given IP address,
+                                             # intended to be IP of the interface of the private subnet of the EC2 VM
+    environment:
+      OR_SETUP_TYPE:
+      OR_ADMIN_PASSWORD:
+      OR_SETUP_RUN_ON_RESTART:
+      OR_EMAIL_HOST:
+      OR_EMAIL_USER:
+      OR_EMAIL_PASSWORD:
+      OR_EMAIL_X_HEADERS:
+      OR_EMAIL_FROM:
+      OR_EMAIL_ADMIN:
+      OR_METRICS_ENABLED: ${OR_METRICS_ENABLED:-true}
+      OR_HOSTNAME: ${OR_HOSTNAME:-localhost}
+      OR_ADDITIONAL_HOSTNAMES:
+      OR_SSL_PORT: ${OR_SSL_PORT:--1}
+      OR_DEV_MODE: ${OR_DEV_MODE:-false}
+
+      # The following variables will configure the demo
+      OR_FORECAST_SOLAR_API_KEY:
+      OR_OPEN_WEATHER_API_APP_ID:
+      OR_SETUP_IMPORT_DEMO_AGENT_KNX:
+      OR_SETUP_IMPORT_DEMO_AGENT_VELBUS:
+    volumes:
+      - manager-data:/storage