Deterministic Annealing Clustering for ANN-Based Short-Term Load Forecasting
Deterministic Annealing Clustering for ANN-Based Short-Term Load Forecasting
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DOI：
10.1109/mper.2001.4311558
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发表时间：
2001-05
期刊：
影响因子：
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通讯作者：
H. Mori;A. Yuihara
中科院分区：
文献类型：
--
作者：
H. Mori;A. Yuihara
本文提出了一种聚类方法，用于电力系统短期负荷预测的输入数据预处理。在用人工神经网络（ANN）进行预测之前对输入数据进行聚类，减少了观察到的预测误差。在本文中，一个MLP人工神经网络处理一步前进的日最大负荷预测，并采用确定性退火（DA）聚类分类的输入数据到集群。DA聚类是基于统计力学中的最大熵原理来评价全局最优分类。所提出的方法被成功地应用于真实的数据。在平均和最大预测误差方面，对所提出的方法和传统方法进行了比较。通过真实的负荷数据与短期预测值的比较，验证了该方法的有效性。
This paper presents a clustering method for preprocessing input data of short-term load forecasting in power systems. Clustering the input data prior to forecasting with the artificial neural network(ANN) decreases the prediction errors observed. In this paper, an MLP ANN is used to deal with one-step-ahead daily maximum load forecasting, and the deterministic annealing (DA) clustering is employed to classify input data into clusters. The DA clustering is based on the principle of maximum entropy in statistical mechanics to evaluate globally optimal classification. The proposed method is successfully applied to real data. A comparison is made between the proposed and the conventional methods in terms of the average and the maximum prediction errors. The effectiveness of the proposed method is demonstrated through comparison of the real load data with short-term forecasted values.