Prediction of photovoltaic power output based on similar day analysis using RBF neural network with adaptive black widow optimization algorithm and K-means clustering

Abstract

Solar photovoltaic power generation has become the focus of the world energy market. However, weak continuity and variability of solar power data severely increase grid operating pressure. Therefore, it is necessary to propose a new refined and targeted forecasting method to broaden the forecasting channels. In this paper, a hybrid model (KM-SDA-ABWO-RBF) based on radial basis function neural networks (RBFNNs), adaptive black widow optimization algorithm (ABWO), similar day analysis (SDA) and K-means clustering (KM) has been developed. The ABWO algorithm develops adaptive factors to optimize the parameters of RBFNNs and avoid getting trapped in local optima. SDA and K-means clustering determine the similarity days and the optimal similarity day through meteorological factors and historical datasets. Nine models compared forecast accuracy and stability over four seasons. Experiments show that compared with other well-known models on the four indicators, the proposed KM-SDA-ABWO-RBF model has the highest prediction accuracy and is more stable.