Short term electric load forecasting using an adaptively trained layered perceptron

M. El-Sharkawi, S. Oh, R. Marks, M. Damborg, C.M. Brace
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引用次数: 40

Abstract

The authors address electric load forecasting using artificial neural network (NN) technology. They summarize research for Puget Sound Power and Light Company. In this study, several structures for NNs are proposed and tested. Features extraction is implemented to capture strongly correlated variables to electric loads. The NN is compared to several forecasting models. Most of them are commercial codes. The NN performed as well as the best and most sophisticated commercial forecasting systems.<>
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基于自适应训练分层感知器的短期电力负荷预测
利用人工神经网络(NN)技术对电力负荷进行预测。他们总结了普吉特声光公司的研究。在本研究中,提出并测试了几种神经网络结构。实现特征提取以捕获与电力负荷强相关的变量。将神经网络与几种预测模型进行了比较。其中大部分是商业代码。神经网络的表现与最好和最复杂的商业预测系统一样好
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Finite precision error analysis for neural network learning Hybrid expert system neural network hierarchical architecture for classifying power system contingencies Neural network application to state estimation computation Short term electric load forecasting using an adaptively trained layered perceptron Neural networks for topology determination of power systems
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