POWER FORECASTING OF ULTRA-SHORT-TERM PHOTOVOLTAIC STATION BASED ON NWP SIMILARITY ANALYSIS

Zhang Shan; Dong Lei; Ji Deyang; Hao Ying; Zhang Xiaofeng

doi:10.19912/j.0254-0096.tynxb.2020-0717

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Acta Energiae Solaris Sinica ›› 2022, Vol. 43 ›› Issue (4) : 142-147. DOI: 10.19912/j.0254-0096.tynxb.2020-0717

Topics on Key Technologies for Safety of Electrochemical Energy Storage Systems and Echelon Utilization of Decommissioned Power Batteries

POWER FORECASTING OF ULTRA-SHORT-TERM PHOTOVOLTAIC STATION BASED ON NWP SIMILARITY ANALYSIS

Zhang Shan¹, Dong Lei¹, Ji Deyang¹, Hao Ying¹, Zhang Xiaofeng²

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Abstract

According to the fact that photovoltaic plants have similar generation power under similar weather conditions, an Ultra-short-term power forecasting method based on NWP similarity analysis is proposed. The proposed method uses the Pearson correlation coefficient to find weather forecast data similar to the predicted time, and estimates the power in the predicted time based on the actual power of the similar time. The proposed method can efficiently forecast the generated power based on the weather forecast data. Compared with the neural network, the proposed method has a better effect, especially in the period of large data fluctuations, which has higher reliability.

Key words

photovoltaic station / Pearson correlation coefficient / power forecasting / numerical weather prediction / similarity analysis

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Zhang Shan, Dong Lei, Ji Deyang, Hao Ying, Zhang Xiaofeng. POWER FORECASTING OF ULTRA-SHORT-TERM PHOTOVOLTAIC STATION BASED ON NWP SIMILARITY ANALYSIS[J]. Acta Energiae Solaris Sinica. 2022, 43(4): 142-147 https://doi.org/10.19912/j.0254-0096.tynxb.2020-0717

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