MetroClimate is an innovative Internet of Things (IoT) project aimed at enhancing weather forecasting accuracy through a city-wide network of environmental sensors located primarily in residential areas. This project utilizes ESP microcontrollers along with various sensors to measure critical environmental factors such as humidity, temperature, and light levels. The collected data is sent to a cloud-based platform for processing.
- Real-Time Data Collection: Leveraging ESP microcontrollers for their WiFi capabilities, compact size, and cost-effectiveness, enabling efficient data collection from widespread urban locations.
- Machine Learning Integration: Data is used to train a machine learning model that continuously improves its predictive capabilities, eventually aiming to surpass existing weather forecasting APIs.
- API Comparison: The project includes integration with free weather forecasting APIs to compare real-time sensor data against API predictions, enhancing the accuracy and reliability of weather forecasts.
- Community Impact: Designed to benefit a diverse range of users, from daily commuters wondering if they need an umbrella, to professionals like firefighters and disaster management specialists who depend on precise weather information.
The ultimate goal of MetroClimate is to go beyond traditional weather forecasting, providing a robust tool that helps cities adapt to their unique microclimates and offering the community more reliable and precise weather predictions.