利用Elman和FIR神经网络进行短期电力负荷预测

A. I. Galarniotis, A. Tsakoumis, P. Fessas, S. Vladov, V. Mladenov
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引用次数: 6

摘要

比较了有限脉冲响应(FIR)神经网络和Elman神经网络在电力负荷预测中的应用。用一种时间反向传播学习算法对FIR神经网络进行了训练,结果表明,该算法的有效性比应用的网络模型更重要。将这两种网络与基于多层感知器(MLP)网络的标准方法进行了比较,证明了FIR网络的有效性。它比Elman网络性能更好。两种网络的性能都优于MLP网络。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Using Elman and FIR neural networks for short term electric load forecasting
Finite impulse response (FIR) neural network and Elman neural network have been compared in electric load prediction. An FIR neural network has been trained with a temporal back-propagation learning algorithm and the results obtained showed that the effectiveness of the algorithm is more important than the applied network model. The comparison between both networks and the standard approach with Multilayer perceptron (MLP) network, demonstrates that the FIR network acts adequately. It performs better than the Elman network. Both networks perform better than the MLP network.
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