Short‐term photovoltaic power forecasting with adaptive stochastic configuration network ensemble

Abstract

The volatility and intermittency of solar energy seriously restrict the development of the photovoltaic (PV) industry. Accurate forecast of short‐term PV power generation is essential for the optimal balance and dispatch of power plants in the smart grid. This article presents a machine learning approach for analyzing the volt‐ampere characteristics and influential factors on PV data. A correlation analysis is employed to discover some hidden characteristic variables. Then, an adaptive ensemble method with stochastic configuration networks as base models (AE‐SCN) is proposed to construct the PV prediction model, which integrates bagging and adaptive weighted data fusion algorithms. Compared with the original SCN, SCN ensemble (SCNE) and random vector functional‐link network (RVFLN), linear regression model, random forest model and autoregressive integrated moving average (ARMA) model, AE‐SCN performs favorably in the terms of the prediction accuracy.