Shengwei-Peng/PV-Power-Generation-Forecasting
A project focused on forecasting solar photovoltaic (PV) power generation using regional microclimate data. Implements machine learning models like CatBoost, LightGBM, and XGBoost for predictions, leveraging environmental features like temperature, humidity, wind speed, and solar radiation.
This project helps solar farm operators and energy planners accurately forecast the power output of photovoltaic (PV) installations. By taking regional weather data, including temperature, humidity, wind, and solar radiation, it predicts future PV power generation. This tool is designed for energy managers, grid operators, and renewable energy analysts who need reliable predictions for operational planning and grid stability.
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Use this if you need to predict solar panel power generation based on local weather conditions for better energy management and resource allocation.
Not ideal if you need real-time, ultra-high-frequency predictions or if you lack access to comprehensive microclimate data for your PV sites.
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Jupyter Notebook
License
Apache-2.0
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Last pushed
Mar 10, 2025
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