基于神经元数优化的人工神经网络模型预测光伏组件输出功率

IF 2.4 Q3 ENERGY & FUELS
A. Mohammad, Hasanen M. Hussen, Hussein J. Akeiber
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引用次数: 1

摘要

人工神经网络是一种自适应系统,能够在不定义物理和操作条件的情况下预测输入和输出参数之间的关系。在本研究中,简单地澄清了关于使用神经网络方法的一些问题,特别是关于神经元数量及其与输入和输出参数的关系。此外,使用MATLAB代码开发了两个ANN模型,以预测伊拉克真实天气条件下多晶光伏组件的发电量。然后使用神经网络模型来优化隐藏层中的神经元数量。人工神经网络模型的能力已经在几个天气和操作参数的影响下进行了测试。在这方面,六个变量被用作输入参数,包括环境温度、太阳辐照度和风速(天气条件),以及模块温度、短路电流和开路电压(光伏模块的特性)。根据神经网络模型的性能分析,最优神经元数为单隐层15个,最小均方根误差(RMSE)为2.76%,双隐层10个,最小RMSE为1.97%。此外,人工神经网络模型已证明其在预测光伏组件电流和电压方面的准确性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Prediction the output power of photovoltaic module using artificial neural networks model with optimizing the neurons number
Artificial neural networks (ANNs) is an adaptive system that has the ability to predict the relationship between the input and output parameters without defining the physical and operation conditions. In this study, some queries about using ANN methodology are simply clarified especially about the neurons number and their relationship with input and output parameters. In addition, two ANN models are developed using MATLAB code to predict the power production of a polycrystalline PV module in the real weather conditions of Iraq. The ANN models are then used to optimize the neurons number in the hidden layers. The capability of ANN models has been tested under the impact of several weather and operational parameters. In this regard, six variables are used as input parameters including ambient temperature, solar irradiance and wind speed (the weather conditions), and module temperature, short circuit current and open circuit voltage (the characteristics of PV module). According to the performance analysis of ANN models, the optimal neurons number is 15 neurons in single hidden layer with minimum Root Mean Squared Error (RMSE) of 2.76% and 10 neurons in double hidden layers with RMSE of 1.97%.  Accordingly, it can be concluded that the double hidden layers introduce a higher accuracy than the single hidden layer. Moreover, the ANN model has proven its accuracy in predicting the current and voltage of PV module. 
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来源期刊
CiteScore
4.50
自引率
16.00%
发文量
83
审稿时长
8 weeks
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