Execution of synthetic Bayesian model average for solar energy forecasting

Abstract

Accurate photovoltaic (PV) forecasting is quite crucial in planning and in the regular operation of power system. Stochastic habit along with the high risks in PV signal uncertainty and a probabilistic forecasting model is required to address the numerical weather prediction (NWP) underdispersion. In this study, a new synthetic prediction process based on Bayesian model averaging (BMA) and Ensemble Learning is developed. The proposed model is initiated by the improved self-organizing map (ISOM) clustering K-fold cross-validation for the training process. To provide desirable learning model for different input samples, three learners including long short-term memory (LSTM) network, general regression neural network (GRNN), and non-linear auto-regressive eXogenous NN (NARXNN) are employed. The proposed BMA approach is combined with the output of the learners to obtain accurate and desirable outcomes. Different models are precisely compared with the obtained numerical results over real-world engineering test site, that is, Arta-Solar case study. The numerical analysis and recorded results validate the performance and superiority of the proposed model.