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<p>Accurate solar power generation forecasting is essential for grid stability and renewable energy integration. This paper presents an enhanced solar power forecasting system achieving 94.95% accuracy (<InlineEquation ID="IEq1"> <EquationSource Format="TEX">\(\hbox {R}^{2}\)</EquationSource> </InlineEquation>) using a voting ensemble approach combined with physics-informed feature engineering. The methodology transforms 21 meteorological variables from the Kaggle Solar Energy Power Generation Dataset into 41 engineered features incorporating solar geometry, atmospheric physics, and temporal dynamics. The proposed voting ensemble combines Gradient Boosting Regressor, LightGBM, and XGBoost through simple averaging, achieving <InlineEquation ID="IEq2"> <EquationSource Format="TEX">\(\hbox {R}^{2}\)</EquationSource> </InlineEquation> = 0.949, RMSE = 214.8 kW, and MAE = 127.7 kW with only 142.4 seconds training time. Experimental validation on 4,213 observations demonstrates superior performance compared to individual models, positioning the system within 3.05% of the target 98% accuracy threshold while maintaining exceptional computational efficiency for real-time deployment.</p>

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Physics-informed voting ensemble for solar power generation forecasting: integrating domain knowledge with machine learning

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年1月2日

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  • Manimaran Naghapushanam,
  • Baskaran Jeevarathinam,
  • C. Sankari

摘要

Accurate solar power generation forecasting is essential for grid stability and renewable energy integration. This paper presents an enhanced solar power forecasting system achieving 94.95% accuracy ( \(\hbox {R}^{2}\) ) using a voting ensemble approach combined with physics-informed feature engineering. The methodology transforms 21 meteorological variables from the Kaggle Solar Energy Power Generation Dataset into 41 engineered features incorporating solar geometry, atmospheric physics, and temporal dynamics. The proposed voting ensemble combines Gradient Boosting Regressor, LightGBM, and XGBoost through simple averaging, achieving \(\hbox {R}^{2}\) = 0.949, RMSE = 214.8 kW, and MAE = 127.7 kW with only 142.4 seconds training time. Experimental validation on 4,213 observations demonstrates superior performance compared to individual models, positioning the system within 3.05% of the target 98% accuracy threshold while maintaining exceptional computational efficiency for real-time deployment.