Comparison of artificial intelligence and empirical models for estimation of daily diffuse solar radiation in North China Plain

文献类型： 外文期刊

第一作者： Feng, Yu

作者： Feng, Yu;Cui, Ningbo;Zhang, Qingwen;Zhao, Lu;Feng, Yu;Cui, Ningbo;Zhang, Qingwen;Zhao, Lu;Feng, Yu;Gong, Daozhi

作者机构：

关键词： Diffuse solar radiation;Extreme learning machine;Backpropagation neural networks;Random forests;Generalized regression neural networks;North China Plain

期刊名称：INTERNATIONAL JOURNAL OF HYDROGEN ENERGY （ 影响因子：5.816； 五年影响因子：5.242 ）

ISSN：

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收录情况： SCI

摘要： Accurate diffuse solar radiation (Ha) data is highly crucial for the development and utilization of solar energy technologies. However, due to expensive cost and technology requirements, measurements of Ha are not available in many regions of North China Plain (NCP), where the diffuse and direct solar radiation are affected by severe particulate pollution. Thus, development of models for precisely estimating H-d is indeed essential in NCP. On this account, the present studies proposed four artificial intelligence models, including the extreme learning machine (ELM), backpropagation neural networks optimized by genetic algorithm (GANN), random forests (RF), and generalized regression neural networks (GRNN), for estimating daily Hd at two meteorological stations of NCP. Daily global solar radiation and sunshine duration along with the estimated extraterrestrial radiation and maximum possible sunshine duration were selected as model inputs to train the models. Meanwhile, the proposed AI models were compared with the empirical Iqbal model to test their performance using measured Hd data. The results indicated that the ELM, GANN, RF, and GRNN models all performed much better than the empirical Iqbal model for estimating daily Ha. All the models underestimated Hd for both stations, with average relative error ranging from -5.8% to -5.4% for AI models and 19.1% for Iqbal model in Beijing, -5.9% to -4.3% and -26.9% in Zhengzhou, respectively. Generally, GANN model had the best accuracy, and ELM ranked next, followed by RF and GRNN models. The ELM model had a slightly poorer performance but the highest computation speed, and both the GANN and ELM models could be highly recommended to estimate daily Ha in NCP of China. (C) 2017 Hydrogen Energy Publications LLC. Published by Elsevier Ltd. All rights reserved.

分类号： TK

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