A novel hybrid ensemble LSTM‐FFNN forecasting model for very short‐term and short‐term PV generation forecasting
Despoina Kothona, Ioannis P. Panapakidis, Georgios C. Christoforidis
Abstract
Abstract The increasing penetration of photovoltaic (PV) systems into the electrical energy systems brings forward several technical and economic issues that mostly relate to their unpredictable nature. A promising solution to many of these is the implementation of robust PV generation forecasting models. In this paper a novel hybrid Ensemble Long Short‐Term Memory‐Feed Forward Neural Network (ELSTM‐FFNN) model is proposed, that is able to perform both very‐short and short‐term forecasting. The performance of the proposed model is compared with individual LSTM models, and its forecasting accuracy is assessed in two different forecasting horizons: (a) 15‐min ahead and (b) 1‐h ahead. Moreover, in order to fully examine the contribution of the utilized data to the performance of the model, several scenarios have been formulated for each forecasting horizon. The results indicate that the proposed ELSTM‐FFNN model can increase the forecasting accuracy in both horizons between 3–11.9% and 0.2–17.8%, respectively, considering the Mean Absolute Range Normalized Error (MARNE).