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Energies 2019, 12(1), 150; https://doi.org/10.3390/en12010150

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

1
School of Resource and Environmental Science, Wuhan University, Wuhan 430079, China
2
School of Geographic Sciences, Xinyang Normal University, Xinyang 464000, China
3
Laboratory of Critical Zone Evolution, School of Earth Sciences, China University of Geosciences, Wuhan 430074, China
*
Author to whom correspondence should be addressed.
Received: 19 November 2018 / Revised: 18 December 2018 / Accepted: 27 December 2018 / Published: 2 January 2019
(This article belongs to the Special Issue Solar Thermal Energy Utilization Technologies in Buildings)
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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−2day−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−2during 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. View Full-Text
Keywords: direct horizontal irradiance; physically-based model; machine learning method; estimation; China direct horizontal irradiance; physically-based model; machine learning method; estimation; China
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Chen, F.; Zhou, Z.; Lin, A.; Niu, J.; Qin, W.; Yang, Z. Evaluation of Direct Horizontal Irradiance in China Using a Physically-Based Model and Machine Learning Methods. Energies 2019, 12, 150.

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