Solar radiation estimation using artificial neural networks
Solar radiation estimation using artificial neural networks
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DOI：
10.1016/s0306-2619(02)00016-8
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发表时间：
2002-04-01
期刊：
影响因子：
11.2
通讯作者：
Al-Lawati, A
中科院分区：
文献类型：
--
作者：
Dorvlo, ASS;Jervase, JA;Al-Lawati, A
讨论了利用人工神经网络对太阳辐射进行预估的方法。径向基函数。RBF和多层感知器。利用阿曼8个站点的长期数据对MLP模型进行了调查。结果表明，基于观测到的太阳辐射与估计的均方根误差，RBF和MLP模型都具有良好的性能。然而，RBF模型是首选，因为它们需要较少的计算能力。RBF模型是用来自Masirah、Salalah、Seeb、Sur、Fahud和Sohar气象站的数据进行训练，并用来自Buraimi和Marmul的气象站进行测试而获得的。是最好的。该模型可用于估算阿曼任何地点的太阳辐射。(C) 2002 Elsevier Science Ltd.版权所有。
Artificial Neural Network Methods are discussed for estimating solar radiation by first estimating the clearness index. Radial Basis Functions. RBF, and Multilayer Perceptron. MLP, models have been investigated using long-term data from eight stations in Oman. It is shown that both the RBF and MLP models performed well based on the root-mean-square error between the observed and estimated solar radiations. However, the RBF models are preferred since they require less computing power. The RBF model, obtained by training with data from the meteorological stations at Masirah, Salalah, Seeb, Sur, Fahud and Sohar, and testing with those from Buraimi and Marmul. was the best. This model can be used to estimate the solar radiation at any location in Oman. (C) 2002 Elsevier Science Ltd. All rights reserved.