Solar Power Prediction with an Hour-based Ensemble Machine Learning Method
In recent years, the share of solar power in total energy production has gained a rapid increase. Therefore, prediction of solar power production has become increasingly important for energy regulations. In this study we proposed an ensemble method which gives competitive prediction performance for solar power production. This method firstly decomposes the nonlinear power production data into components with a multi-scale decomposition technique such as Empirical Mode Decomposition (EMD). These components are then enriched with the explanatory exogenous feature set. Finally, each component is separately modeled by nonlinear machine learning methods and their results are aggregated as final prediction. We use two different training approaches such as Hour-based and Day-based, for predicting the power production at each hour in a day. Experimental results show that our ensemble method with Hour-based approach outperform the examined machine learning methods.
Solar power, Time series forecasting, Machine learning, Ensemble methods, Empirical mode decomposition.
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