RESEARCH ON GROUPING MODEL OF HOURLY GLOBAL SOLAR RADIATION BASED ON GA-BP NEURAL NETWORK

Yu Ying; Chen Xiao; Jia Xiaoyu; Yang Liu

doi:10.19912/j.0254-0096.tynxb.2021-0015

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Acta Energiae Solaris Sinica ›› 2022, Vol. 43 ›› Issue (8) : 157-163. DOI: 10.19912/j.0254-0096.tynxb.2021-0015

RESEARCH ON GROUPING MODEL OF HOURLY GLOBAL SOLAR RADIATION BASED ON GA-BP NEURAL NETWORK

Yu Ying¹, Chen Xiao¹, Jia Xiaoyu¹, Yang Liu²

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Abstract

By analyzing the main factors affecting solar radiation, this paper proposes a grouping modeling method, which uses the solar altitude angle, season and clearness index as the indicators of data classification. The hourly meteorological data and radiation data for Lhasa and Xi'an are selected as examples. The hourly global radiation grouping models for solar altitude, seasons and weather are established respectively through BP neural network optimized by genetic algorithm （GA）. The error variation rules of grouping model are revealed. The error of grouping models are compared with that of the AllData model further. The results show that the estimation error of the grouping model is reduced compared with the AllData model, and the estimation error of the weather grouping model is the smallest. The results of Xi'an's weather grouping model are better than those of Lhasa. Compared with the AllData model, the mean absolute percentage error （MAPE） and relative root mean square error （rRMSE） of Xi'an's weather grouping model is decreased by 3.96% and 4.18%, respectively. The results show that the grouping model can reduce the estimation error of hourly global solar radiation, and provide a reference for neural network as a method for estimating hourly global solar radiation.

Key words

solar energy / hourly global solar radiation / GA-BP neural network / grouping model / error analysis

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Yu Ying, Chen Xiao, Jia Xiaoyu, Yang Liu. RESEARCH ON GROUPING MODEL OF HOURLY GLOBAL SOLAR RADIATION BASED ON GA-BP NEURAL NETWORK[J]. Acta Energiae Solaris Sinica. 2022, 43(8): 157-163 https://doi.org/10.19912/j.0254-0096.tynxb.2021-0015

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