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DOI:10.35833/MPCE.2020.000004
A Hybrid Model for Short-term PV Output Forecasting Based on PCA-GWO-GRNN
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Author: Leijiao Ge1,Yiming Xian2,Jun Yan3,Bo Wang4,Zhongguan Wang1

Author Affiliation: 1.Key Laboratory of Smart Grid of Ministry of Education, Tianjin University, Tianjin 300072, China;2.State Key Laboratory of Reliability and Intelligence of Electrical Equipment, Hebei University of Technology, Tianjin 300132, China;3.Concordia Institute for Information Systems Engineering, Concordia University, Montreal, QC H3G 1M8, Canada;4.School of Electrical and Automation, Wuhan University, Wuhan 430072, China

Foundation:
This work was supported by the National Key Research and Development Program of China (No. 2018YFB1500800) and the National Natural Science Foundation of China (No. 51807134).
Abstract: High-precision day-ahead short-term photovoltaic (PV) output forecasting is essential in PV integration to the smart distribution networks and multi-energy system, and provides the foundation for the security, stability, and economic operation of PV systems. This paper proposes a hybrid model based on principal component analysis, grey wolf optimization and generalized regression neural network (PCA-GWO-GRNN) for day-ahead short-term PV output forecasting, considering the features of multiple influencing factors and strong uncertainty. This paper first uses the PCA to reduce the dimension of meteorological features. Then, the high-precision day-ahead short-term PV output forecasting based on GWO-GRNN model is realized. GRNN is used to regressively analyze the input features after dimension reduction, and the parameter of GRNN is optimized by using GWO, which has strong global searching ability and fast convergence. The proposed PCA-GWO-GRNN model effectively achieves a high precision in day-ahead short-term PV output forecasting, which is demonstrated in a case study on a real PV plant in Jiangsu province, China. The results have validated the accuracy and applicability of the proposed model in real scenarios.

Keywords:
Photovoltaic output forecasting ; principal component analysis (PCA) ; grey wolf optimization (GWO) ; generalized regression neural network (GRNN).
Received:January 03, 2020               Online Time:2020/12/03
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