School of Electronics and Computer Science, University of Westminster, 115 New Cavendish Street, London W1W 6UW, UK.
Int J Neural Syst. 2013 Oct;23(5):1350024. doi: 10.1142/S012906571350024X. Epub 2013 Jul 18.
Load forecasting is a critical element of power system operation, involving prediction of the future level of demand to serve as the basis for supply and demand planning. This paper presents the development of a novel clustering-based fuzzy wavelet neural network (CB-FWNN) model and validates its prediction on the short-term electric load forecasting of the Power System of the Greek Island of Crete. The proposed model is obtained from the traditional Takagi-Sugeno-Kang fuzzy system by replacing the THEN part of fuzzy rules with a "multiplication" wavelet neural network (MWNN). Multidimensional Gaussian type of activation functions have been used in the IF part of the fuzzyrules. A Fuzzy Subtractive Clustering scheme is employed as a pre-processing technique to find out the initial set and adequate number of clusters and ultimately the number of multiplication nodes in MWNN, while Gaussian Mixture Models with the Expectation Maximization algorithm are utilized for the definition of the multidimensional Gaussians. The results corresponding to the minimum and maximum power load indicate that the proposed load forecasting model provides significantly accurate forecasts, compared to conventional neural networks models.
负荷预测是电力系统运行的关键环节，涉及对未来需求水平的预测，作为供需规划的基础。本文提出了一种基于聚类的模糊小波神经网络（CB-FWNN）模型的发展，并验证了其在希腊克里特岛电力系统短期电力负荷预测中的预测能力。所提出的模型是通过用“乘法”小波神经网络（MWNN）替换模糊规则的 THEN 部分从传统的 Takagi-Sugeno-Kang 模糊系统获得的。在模糊规则的 IF 部分使用了多维高斯类型的激活函数。采用模糊减法聚类方案作为预处理技术，找出初始集合和适当数量的聚类，最终确定 MWNN 中的乘法节点数，而多维高斯的定义则采用期望最大化算法的高斯混合模型。对应于最小和最大功率负荷的结果表明，与传统的神经网络模型相比，所提出的负荷预测模型提供了非常准确的预测。
