Artificial neural network versus linear regression for predicting Grid-Connected Photovoltaic system output
Artificial neural network versus linear regression for predicting Grid-Connected Photovoltaic system output
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人工神经网络与线性回归预测并网光伏系统输出
DOI：
10.1109/cyber.2012.6392548
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发表时间：
2012
期刊：
影响因子：
--
通讯作者：
S. Shaari
中科院分区：
文献类型：
--
作者：
S. Sulaiman;Titik Khawa Abdul Rahman;I. Musirin;S. Shaari
提出了一种经典训练的多层前馈神经网络(MLFNN)技术，用于预测光伏并网发电(GCPV)系统的输出功率。在拟议的MLFNN中，训练参数的选择是使用一系列规定的步骤进行的。MLFNN以太阳辐照度(SI)和组件温度(MT)为输入，交流千瓦时能量为输出。与线性回归方法相比，MLFNN具有更低的预测误差，具有更好的性能。
This paper presents a classically trained Multi-Layer Feedforward Neural Network (MLFNN) technique for predicting the output from a Grid-Connected Photovoltaic (GCPV) system. In the proposed MLFNN, the selection of the training parameters was conducted using a series of prescribed steps. The MLFNN utilized solar irradiance (SI) and module temperature (MT) as its inputs and AC kWh energy as its output. When compared with the linear regression method, the MLFNN offered superior performance by producing lower prediction error.