A Hybrid Probabilistic Estimation Method for Photovoltaic Power Generation Forecasting. (February 2019)

• Record Type: Journal Article
• Authors: Cheng, Ze; Liu, Qi; Xing, Yuhan
• Is Part Of: Energy procedia. Volume 158(2019)
• Page Start: 173
• Page End: 178
• Publication Date: 2019-02
• Subjects: Probability prediction -- PV power forecast -- Spare Gaussian process regression -- Improved grey wolf optimizer algorithm; Power resources -- Congresses; Power resources -- Periodicals; Power resources; Conference proceedings; Periodicals; 333.7905
• DOI: 10.1016/j.egypro.2019.01.066
• Languages: English
• ISSNs: 1876-6102

Abstract

Abstract: Because of stochastic nature of weather conditions, the predictability of photovoltaic (PV) power generation is poor. Compared with the point prediction, the probabilistic prediction of PV power generation can provide more information about the underlying uncertainties, which is beneficial to the stability and safety of grid dispatching and power system. Based on random forest (RF), fuzzy C-means (FCM), sparse Gaussian process (SPGP), improved grey wolf optimizer (IMGWO) algorithm, a hybrid probabilistic estimation method, in this paper, is proposed to predict the probability of PV power generation for every hour in one day. RF algorithm is firstly used to reduce multidimensional input variables. And according to the weather patterns, FCM method is adopted to divide data and get the similar samples. Finally, a hybrid forecasting method combines SPGP and IMGWO is applied to forecast the test data. With the simulation and experimental results, the validity and reliability of the proposed model (IMGWOSP) is verified. The results show that the proposed model has improved both accuracy and practicability, so the stability and safety of grid dispatching and power system can be improved.