A support vector machine approach to estimate global solar radiation with the influence of fog and haze

Wanxiang Yao, Chunxiao Zhang, Haodong Hao, Xiao Wang and Xianli Li

Renewable Energy, 2018, vol. 128, issue PA, 155-162

Abstract: In recent years, fog and haze occurred frequently, due to energy crisis and environmental pollution. Fog and haze have significant scattering-weakening effect on solar radiation, resulting in a severe weaken to solar radiation received on a horizontal surface. In this paper, air quality index (AQI) is taken as an additional input parameter, and some new models for estimating global solar radiation on a horizontal surface are proposed based on a support vector machine (SVM). The accuracy of SVM-1 and SVM-2 models are compared and analyzed, and the results show that the performance of SVM-2 models with an extra input parameter AQI are generally improved, for which the R value is promoted from 0.848 to 0.876, the NSE value is lifted from 0.682 to 0.740, the RMSE value is reduced from 0.114 to 0.102, and the MAPE value is decreased from 9.257 to 8.214. Comparing with existing models, SVM models proposed in this paper can improve the accuracy of global solar radiation models. If AQI is used as an additional input parameter to estimate global solar radiation, the accuracy will be further improved.

Keywords: Support vector machines; Global solar radiation; Fog and haze; Air quality index (search for similar items in EconPapers)
Date: 2018
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (16)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:renene:v:128:y:2018:i:pa:p:155-162

DOI: 10.1016/j.renene.2018.05.069

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