RESEARCH OF COMBINED FORECASTING METHOD OF SHORT-TERM PHOTOVOLTAIC POWER ON TRANSITIVE CLOSURE BASED

Li Wei; Wang Xinpeng; Xu Ye; Wang Xu; Ma Wenjing

doi:10.19912/j.0254-0096.tynxb.2022-0158

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Acta Energiae Solaris Sinica ›› 2023, Vol. 44 ›› Issue (6) : 265-274. DOI: 10.19912/j.0254-0096.tynxb.2022-0158

RESEARCH OF COMBINED FORECASTING METHOD OF SHORT-TERM PHOTOVOLTAIC POWER ON TRANSITIVE CLOSURE BASED

Li Wei¹, Wang Xinpeng¹, Xu Ye¹, Wang Xu¹, Ma Wenjing²

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Abstract

In order to effectively improve the prediction accuracy of short-term photovoltaic power generation and ensure the stable operation of the power grid, this study uses the Pearson Correlation Coefficient （PCC） method to extract relevant meteorological elements and adopts the transitive closure method to determine the similar days. Combining ARIMA time series and BP neural network, two combined prediction models of PCC-transitive closure-ARIMA and PCC-transitive closure-BP are constructed, and used to solve the output prediction problem of a photovoltaic power station in Binchuan, Yunnan. The comparison results demonstrate that, （i） compared with single model, the prediction accuracy of two combination models is improved significantly, where the combined model based on BP neural network has the better performance, with an average prediction accuracy of 91.19%; （ii） ARIMA model is suitable to describe the fluctuation and variation characteristics of photovoltaic output in the period of high-level electricity output ; correspondingly, BP model owns more adjust and correct capability under unfavorable meteorological condition.

Key words

photovoltaic power / prediction model / factor analysis / Pearson correlation coefficient / transitive closure / ARIMA / BP neural network

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Li Wei, Wang Xinpeng, Xu Ye, Wang Xu, Ma Wenjing. RESEARCH OF COMBINED FORECASTING METHOD OF SHORT-TERM PHOTOVOLTAIC POWER ON TRANSITIVE CLOSURE BASED[J]. Acta Energiae Solaris Sinica. 2023, 44(6): 265-274 https://doi.org/10.19912/j.0254-0096.tynxb.2022-0158

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