An improved RBF neural network for short-term load forecast in smart grids

Yun Lu, Tiankui Zhang, Zhimin Zeng, J. Loo
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引用次数: 18

Abstract

With the rapid development of smart grids, a large volume of smart meter data has been collected. The analysis of these big data can be leveraged to channel the data from individual meters into knowledge valuable to electric power utilities and end-consumers. Short-term load forecasting is essential for ensuring both efficient and reliable operations of smart grids. This paper proposes an improved Radial Basis Function neural network model (RBF-PCA-WFCM) for short-term load forecast, which use the weighted Fuzzy C-Means (FCM) clustering algorithm based on Principal Component Analysis (PCA) to determine the basis function centers, and use the gradient descent algorithm to train the output layer weights. The proposed model is implemented on real smart meter data and simulation results show that better forecasting accuracy could be achieved by using the proposed model comparing with the conventional RBF neural network model based on K-Means (RBF-KM).
基于改进RBF神经网络的智能电网短期负荷预测
随着智能电网的快速发展,大量的智能电表数据被收集起来。对这些大数据的分析可以将单个电表的数据转化为对电力公司和最终消费者有价值的知识。短期负荷预测是保证智能电网高效、可靠运行的关键。提出了一种改进的径向基函数神经网络(RBF-PCA-WFCM)短期负荷预测模型,该模型采用基于主成分分析(PCA)的加权模糊c均值(FCM)聚类算法确定基函数中心,并采用梯度下降算法训练输出层权值。仿真结果表明,与基于K-Means的传统RBF神经网络模型(RBF- km)相比,该模型具有更好的预测精度。
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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