Evaluation of Direct Horizontal Irradiance in China Using a Physically-Based Model and Machine Learning Methods

Feiyan Chen, Zhigao Zhou, Aiwen Lin, Jiqiang Niu, Wenmin Qin and Zhong Yang
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Feiyan Chen: School of Resource and Environmental Science, Wuhan University, Wuhan 430079, China
Zhigao Zhou: School of Resource and Environmental Science, Wuhan University, Wuhan 430079, China
Aiwen Lin: School of Resource and Environmental Science, Wuhan University, Wuhan 430079, China
Jiqiang Niu: School of Geographic Sciences, Xinyang Normal University, Xinyang 464000, China
Wenmin Qin: Laboratory of Critical Zone Evolution, School of Earth Sciences, China University of Geosciences, Wuhan 430074, China
Zhong Yang: School of Resource and Environmental Science, Wuhan University, Wuhan 430079, China

Energies, 2019, vol. 12, issue 1, 1-19

Abstract: Accurate estimation of direct horizontal irradiance (DHI) is a prerequisite for the design and location of concentrated solar power thermal systems. Previous studies have shown that DHI observation stations are too sparsely distributed to meet requirements, as a result of the high construction and maintenance costs of observation platforms. Satellite retrieval and reanalysis have been widely used for estimating DHI, but their accuracy needs to be further improved. In addition, numerous modelling techniques have been used for this purpose worldwide. In this study, we apply five machine learning methods: back propagation neural networks (BP), general regression neural networks (GRNN), genetic algorithm (Genetic), M5 model tree (M5Tree), multivariate adaptive regression splines (MARS); and a physically based model, Yang’s hybrid model (YHM). Daily meteorological variables, including air temperature ( T ), relative humidity ( RH ), surface pressure ( SP ), and sunshine duration ( SD ) were obtained from 839 China Meteorological Administration (CMA) stations in different climatic zones across China and were used as data inputs for the six models. DHI observations at 16 CMA radiation stations were used to validate their accuracy. The results indicate that the capability of M5Tree was superior to BP, GRNN, Genetic, MARS and YHM, with the lowest values of daily root mean square ( RMSE) of 1.989 MJ m −2 day −1 , and the highest correlation coefficient ( R = 0.956), respectively. Then, monthly and annual mean DHI during 1960–2016 were calculated to reveal the spatiotemporal variation of DHI across China, using daily meteorological data based on the M5tree model. The results indicated a significantly decreasing trend with a rate of −0.019 MJ m −2 during 1960–2016, and the monthly and annual DHI values of the Tibetan Plateau are the highest, while whereas the lowest values occur in the southeastern part of the Yunnan−Guizhou Plateau, the Sichuan Basin and most of the southern Yangtze River Basin. The possible causes for spatiotemporal variation of DHI across China were investigated by discussing cloud and aerosol loading.

Keywords: direct horizontal irradiance; physically-based model; machine learning method; estimation; China (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (5)

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