A stacked ensemble forecast for photovoltaic power plants combining deterministic and stochastic methods

Abstract

Feature engineering (or creation of new features) that strengthens the importance of fundamental variables (e.g., cloud cover, partial shading, solar irradiance) is essential in gaining an accurate PV forecast for different use cases. On-grid and off-grid PV systems for residential prosumers, and larger scale PV systems are investigated from the PV forecast point of view. Therefore, in this paper, we propose a robust and scalable method designed for PV systems with various sizes and connectivity that provides accurate predictions. The originality of our research consists in feature engineering and combining the deterministic and stochastic models into a meta-learning Stacked Ensemble Forecast (SEF) method that is properly adjusted considering the connectivity of the PV system. The predictions of several standout Machine Learning (ML) algorithms are stacked and scaled to create an accurate forecast model. The proposed methodology is tested on three case studies that cover different types of PV systems: small (residential) on-grid, off-grid, and industrial PV power plants. In all cases, the SEF model provides accurate predictions with an R2 between 0.91 and 0.99 and MAPE between 0.1 and 0.29, followed closely by the stochastic models that have an average R2 of 0.85 and MAPE of 0.3. The deterministic model provides less accurate predictions with an average R2 of 0.8 and MAPE of 0.37. Furthermore, RMSE and MAE improved with more than 16% using the SEF model compared to the stochastic models and with more than 45% compared to the deterministic model.