https://github.com/kamal-shirupa/solar-power-forecasting
This project predicts solar panel energy output based on weather conditions and irradiance levels using a hybrid model of Gradient Boosting (for feature importance) and LSTM (for time-series forecasting). By leveraging real-world irradiance, weather, and panel data, the model enhances solar energy utilization and power grid efficiency.
https://github.com/kamal-shirupa/solar-power-forecasting
deep-learning gradient-boosting knn-imputation lstm machine-learning pca-analysis shap-values
Last synced: about 1 month ago
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This project predicts solar panel energy output based on weather conditions and irradiance levels using a hybrid model of Gradient Boosting (for feature importance) and LSTM (for time-series forecasting). By leveraging real-world irradiance, weather, and panel data, the model enhances solar energy utilization and power grid efficiency.
- Host: GitHub
- URL: https://github.com/kamal-shirupa/solar-power-forecasting
- Owner: Kamal-Shirupa
- Created: 2025-03-30T06:58:25.000Z (about 2 months ago)
- Default Branch: main
- Last Pushed: 2025-03-30T07:59:24.000Z (about 2 months ago)
- Last Synced: 2025-03-30T08:23:55.311Z (about 2 months ago)
- Topics: deep-learning, gradient-boosting, knn-imputation, lstm, machine-learning, pca-analysis, shap-values
- Homepage:
- Size: 648 KB
- Stars: 1
- Watchers: 1
- Forks: 0
- Open Issues: 0
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Metadata Files: