Studies on Highly accurate Short-term Electric Power Load Forecasting with Fuzzy Data Mining

模糊数据挖掘高精度短期电力负荷预测研究

基本信息

批准号：
13650319
负责人：
MORI Hiroyuki
金额：
$ 1.73万
依托单位：
Meiji University
依托单位国家：
日本
项目类别：
Grant-in-Aid for Scientific Research (C)
财政年份：
2001
资助国家：
日本
起止时间：
2001 至 2003
项目状态：
已结题

项目摘要

In this project, a new hybrid intelligent system has been proposed for short-term load forecasting in power systems. The proposed method is based on the regression tree of data mining, Simplified Fuzzy Inference and Tabu Search of Meta-heuristics. The regression tree works to extract rules from data base though the decision tree so that if-then rules are obtained. Simplified Fuzzy Inference (SFI) is a good nonlinear approximation technique for nonlinear systems that is equivalent to the multi-layered perceptron (MLP) of artificial neural network. The use of Tabu Search allows SFI to construct the globally optimal rules in terms of the number of fuzzy membership functions and their location. As a result, the proposed model is superior to MLP in terms of the prediction error. At the same time, SFI is applied to the regression tree to improve the boundary conditions of the splitting conditions. The fuzzy rules contributed to the classification of data on load forecasting.Also, the use of TS is easier to determine the forecasting model from a standpoint of minimizing the maximum errors of load forecasting model through the learning process due to the advantage without any constraints. Therefore, power system operators have flexibility to give priority to the maximum or the average squared errors.In addition, the developed model contributed to the reduction of the reserves of generation so that it plays an important role as the decision making system of selling and buying the electricity and make power system operation and control more effective.

在该项目中，已经提出了一种新的混合智能系统，以用于电源系统中的短期负载预测。所提出的方法基于数据挖掘的回归树，简化的模糊推理和对元映射的禁忌搜索。回归树可以通过决策树从数据库中提取规则，以便如果获得当时的规则。简化的模糊推理（SFI）是一种非线性系统的良好非线性近似技术，相当于人工神经网络的多层感知器（MLP）。 Tabu搜索的使用使SFI可以根据模糊成员功能及其位置来构建全球最佳规则。结果，就预测误差而言，提出的模型优于MLP。同时，将SFI应用于回归树，以改善分裂条件的边界条件。模糊规则有助于对负载预测的数据分类。此外，从最小化最大值的预测模型的情况下，TS的使用更易于确定预测模型，这是由于在没有任何约束的情况下，通过学习过程来最大程度地减少学习过程的最大预测模型误差。因此，电力系统运营商具有灵活的优先级，以优先考虑最大或平均平方错误。此外，开发的模型有助于减少发电的储量，因此它是销售和购买电力并使电力系统运营和使电力系统运行和控制更有效的决策系统起重要作用。