SHORT-TERM PHOTOVOLTAIC POWER FORECASTING METHOD UNDER NON-CLEAR SKY CONDITION

Wang Qingliang, Yang Bo, Ying Xinfeng, Song Xi, Gao Mei

Acta Energiae Solaris Sinica ›› 2022, Vol. 43 ›› Issue (3) : 188-196.

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Acta Energiae Solaris Sinica ›› 2022, Vol. 43 ›› Issue (3) : 188-196. DOI: 10.19912/j.0254-0096.tynxb.2020-0582

SHORT-TERM PHOTOVOLTAIC POWER FORECASTING METHOD UNDER NON-CLEAR SKY CONDITION

  • Wang Qingliang, Yang Bo, Ying Xinfeng, Song Xi, Gao Mei
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Abstract

To solve the problem that the short-term power forecasting accuracy of a photovoltaic generator is not high under non-clear sky conditions, a new short-term power forecasting method is proposed based on relevance vector machine model with an adaptive hybrid kernel. By constructing hybrid kernel function and adaptive optimization kernel parameters, the generalization and learning abilities of the forecasting model are enhanced. Then, the mapping relation of multi-scale and multi-mode data is established to realize the machine learning and effective capture of the random fluctuation rules of photovoltaic power generation. The correlation coefficient was used to screen the historical similarity days, and the optimal forecasting model was automatically determined by the data of historical similarity days. Finally, the short-term power forecasting accuracy of the proposed method and other forecasting methods is compared with the measured data from a photovoltaic power station in Oregon, USA. The results show that the proposed method has the highest accuracy for short-term power forecasting of the photovoltaic generator under non-clear sky conditions.

Key words

photovoltaic generators / power forecasting / machine learning / non-clear sky / kernel function

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Wang Qingliang, Yang Bo, Ying Xinfeng, Song Xi, Gao Mei. SHORT-TERM PHOTOVOLTAIC POWER FORECASTING METHOD UNDER NON-CLEAR SKY CONDITION[J]. Acta Energiae Solaris Sinica. 2022, 43(3): 188-196 https://doi.org/10.19912/j.0254-0096.tynxb.2020-0582

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