Forecasting of short-term photovoltaic power generation using combined interval type-2 Takagi-Sugeno-Kang fuzzy systems

Abstract

For the purpose of environmental protection and economic development, photovoltaic (PV) power generation is becoming increasingly popular, but the intermittence and uncertainty in PV power generation make grid-connected PV systems face severe challenges, accurate forecast is an effective way to solve this problem. Motived by the expectancy to process uncertainty and improve the prediction accuracy, in this study, a new prediction model combines interval type-2 Takagi-Sugeno-Kang (TSK) fuzzy neural network (type-2 TSKFNN) model optimized by extended Kalman filter (EKF) and SOM is proposed. A possible clustering interval is first determined according to the distribution of PV power generation, then the meteorological data are clustered using SOM, and the optimal size of categories is determined by Davies-Bouldin index (DBI). In each data partition, the decoupled extended Kalman filter (DKEF) is used to optimize the parameters of type-2 TSKFNN, including two Gaussian models with uncertain mean and uncertain standard deviation, and the independent fuzzy prediction models are established. The actual prediction analysis verifies the performance of proposed SOM-EKTSK model by using the data collected from Yulara Solar System in Australia, and the comparisons with other fuzzy logic system (FLS) models are also completed. The results show that the proposed SOM-EKTSK model has the highest prediction accuracy. Compared with other contrast models, the root mean square error (RMSE) of forecasts is reduced by 16.47% in spring, 4.75% in summer, 23.15% in autumn and 44.36% in winter, on average.© 2017 Elsevier Inc. All rights reserved.