基于神经网络的电力负荷快速预测

Proceedings of the 43rd IEEE Midwest Symposium on Circuits and Systems (Cat.No.CH37144) Pub Date : 2000-12-01 DOI:10.1109/MWSCAS.2000.952840

M. Lopes, C. R. Minussi, A. Lotufo

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引用次数: 9

摘要

这项工作的目的是发展一种基于神经网络的电力负荷预测方法。本文采用了基于模糊逻辑的自适应反向传播算法。与传统的反向传播算法相比，这种方法的结果是快速训练。使用巴西电力公司的数据给出了结果，性能非常好，符合提案的目标。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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A fast electric load forecasting using neural networks

The objective of this work is the development of a methodology for electric load forecasting based on a neural network. Here, the backpropagation algorithm with an adaptive process based on fuzzy logic is used. This methodology results in fast training, when compared to the conventional formulation of the backpropagation algorithm. Results are presented using data from a Brazilian electric company and the performance is very good for the proposal objective.

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Proceedings of the 43rd IEEE Midwest Symposium on Circuits and Systems (Cat.No.CH37144)

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