I built a weather station that runs machine learning to forecast weather (github.com)

A tech enthusiast has developed a weather station powered by machine learning, capable of forecasting weather up to 24 hours in advance. The system comprises sensor data logging, dual machine learning models for different forecasting time horizons—one for one-hour predictions and another for 24-hour forecasts—along with a web interface for real-time data monitoring and alert management. The solution employs K-Nearest Neighbors (KNN) to facilitate real-time learning without complete retraining of the neural network, ensuring timely and accurate updates based on locally gathered data. This project is significant for the AI/ML community as it embodies a practical implementation of deep learning techniques in real-world applications, specifically in meteorology. The neural network leverages 30 years of historical weather data and integrates various data types, including temperature, humidity, and pressure, to improve prediction accuracy. The architecture features two parallel 1D convolutional networks, enhanced by LSTM units and an attention layer, optimizing the processing of past weather conditions for accurate forecasting. This innovative approach exemplifies how AI can transform environmental data analysis, making advanced predictive capabilities accessible for individual users.