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Solar Radiation Prediction Using Radial Basis Function Models

机译:基于径向基函数模型的太阳辐射预测

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Accurate weather information is essential for developing an efficient solar power generation system. In this study one year hourly meteorological data for Kaitaia, New Zealand has been obtained from The National Climate Database of New Zealand to predict solar radiation. Twelve models with different combinations of input variables were formed. Three artificial neural networks (ANN), Multilayer Perceptron (MLP), Nonlinear Autoregressive Network with Exogenous Inputs (NARX), and Radial Basis Function (RBF) using Levenberg-Marquardt (LM) back propagation learning algorithm were trained and tested for the twelve models. The performance of each approach was assessed by calculating mean square error (MSE) and regression values. The results shows that models with a higher number of input variables irrespective of the number of neurons and delays provide better accuracy and improved results for regression values. In addition, the RBF network outperforms the NARX and MLP approaches. Furthermore, the 24-hour and 4-day ahead predicted solar radiation values of the RBF, NARX and MLP approaches are presented and, the results shows that the RBF network performs better than NARX and MLP approaches.
机译:准确的天气信息对于开发高效的太阳能发电系统至关重要。在这项研究中,已从新西兰国家气候数据库获得了新西兰凯塔亚的一年每小时气象数据,以预测太阳辐射。形成了具有不同输入变量组合的十二个模型。使用Levenberg-Marquardt(LM)反向传播学习算法对三个人工神经网络(ANN),多层感知器(MLP),具有外来输入的非线性自回归网络(NARX)和径向基函数(RBF)进行了训练,并针对这十二个模型进行了测试。通过计算均方误差(MSE)和回归值来评估每种方法的性能。结果表明,无论神经元数量和延迟量如何,输入变量数量更多的模型都可以提供更好的准确性和改进的回归值结果。此外,RBF网络的性能优于NARX和MLP方法。此外,提出了RBF,NARX和MLP方法的24小时和4天提前的太阳辐射预测值,结果表明,RBF网络的性能优于NARX和MLP方法。

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