Improved solar photovoltaic energy generation forecast using deep learning-based ensemble stacking approach

An accurate solar energy forecast is of utmost importance to allow a higher level of integration of renewable energy into the controls of the existing electricity grid. With the availability of data in unprecedented granularities, there is an opportunity to use data-driven algorithms for improved prediction of solar generation. In this paper, an improved generally applicable stacked ensemble algorithm (DSE-XGB) is proposed utilizing two deep learning algorithms namely artificial neural network (ANN) and long short-term memory (LSTM) as base models for solar energy forecast. The predictions from the base models are integrated using an extreme gradient boosting algorithm to enhance the accuracy of the solar PV generation forecast. The proposed model was evaluated on four different solar generation datasets to provide a comprehensive assessment. Additionally, the shapely additive explanation framework was utilized in this study to provide a deeper insight into the learning mechanism of the algorithm. The performance of the proposed model was evaluated by comparing the prediction results with individual ANN, LSTM, and Bagging. The proposed DSE-XGB method exhibits the best combination of consistency and stability on different case studies irrespective of the weather variations and demonstrates an improvement in R2 value of 10%–12% to other models.