Short-Term Power Load Forecasting Based on Fuzzy-RBF Neutral Network

Jia Zheng-yuan, Tian Li
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引用次数: 6

Abstract

The paper proposes short-term power load forecasting model based on fuzzy RBF neural network, it has overcome the BP algorithm's disadvantage of slow convergence rate and it fall into partially the smallest insufficiency easily. RBF network model in the use of the latest neighborhood clustering algorithm, and the network structure and the parameters are double-adjusted and the training speed and forecast accuracy are improved. The examples also show that the model can improve forecast accuracy effectively, reducing the error of load forecasting, and the inherent defects of BP neural network have been avoid.
基于模糊rbf神经网络的短期电力负荷预测
本文提出了基于模糊RBF神经网络的短期电力负荷预测模型,克服了BP算法收敛速度慢和容易陷入局部最小不足的缺点。RBF网络模型中采用了最新的邻域聚类算法,并对网络结构和参数进行了双重调整,提高了训练速度和预测精度。实例表明,该模型能有效提高预测精度,减小负荷预测误差,避免了BP神经网络的固有缺陷。
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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