EXECUTION OF SYNTHETIC BAYESIAN MODEL AVERAGE FOR SOLAR ENERGY FORECASTING

Abstract

Accurate photovoltaic (PV) forecasting is quite crucial in planning and in the regular oper ation 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 pre diction (NWP) underdispersion. In this study, a new synthetic prediction process based on Bayesian model averaging (BMA) and Ensemble Learning is developed. The pro posed 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, gen eral 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.