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.

Highlights

  • Solar energy is regarded as a clean, renewable, sustainable and environmentally-friendly energy source for life on Earth [1]

  • Qin [7] developed an efficient physically-based parameterization scheme to derive surface solar irradiance in China and the USA; the results showed that the model can effectively retrieve surface solar irradiance, with a root mean square error (RMSE) of 35 Wm−2, on a daily basis

  • N represent the number of samples collected; Vest and Vobs represent the estimated and observed direct horizontal irradiance (DHI), respectively; Vest represents the mean value of the estimated DHI; and Vobs represent the mean value of the observed DHI

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Summary

Introduction

Solar energy is regarded as a clean, renewable, sustainable and environmentally-friendly energy source for life on Earth [1]. Solar radiation is measured mainly using four methods: solar radiation retrieval from satellite observations, reanalysis data, simulations based on general circulation models, and direct measurements at the surface [7]. It can be concluded that there is an increasing need for accurate and reliable daily direct radiation data for solar energy applications using various methods within different climatic zones in China. The model with the highest degree of accuracy was used to demonstrate the temporal variation of annual and monthly mean direct radiation, and the spatial distribution of annual mean sum of potential CSP electricity production in different climatic zones across China. Our study is the first assessment of various direct radiation models in different ecosystems in China

Observation Data
Temperature
Back Propagation Neural Network
Statistical Measures of Model Accuracy
Data Quality Control
Validation of Estimated DHI
Analysis of Spatial-Temporal Variations of DHI Values across China
Spatial ofm annual
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