Predicting Power Output Based on Weather Conditions

This project presents a weather-driven power generation prediction system utilizing two machine learning models: a Random Forest (RF) and a Recurrent Neural Network (RNN). The system aims to forecast the energy output of a power generation system based on real-time weather data, including wind speed, temperature, humidity, and pressure. By leveraging data from the OpenWeather API, the system processes and scales the weather parameters, which are then input into the models for accurate predictions. The Random Forest model, known for its robustness and ability to handle complex datasets, provides an interpretable prediction of power output. On the other hand, the RNN, which is particularly effective for sequential data, learns the temporal dependencies in weather patterns, improving the forecasting accuracy for time series data. Both models were trained on historical weather and power generation data, achieving high accuracy in predicting future power production. The system enables real-time power generation predictions, making it an invaluable tool for optimizing energy production strategies. The use of weather data for forecasting ensures more efficient resource planning, leading to enhanced energy management in power generation systems. This integration of machine learning with weather data presents a scalable solution for future energy systems, providing a foundation for the development of predictive models in various energy sectors.