Studies on a Load Forecasting Method with the Evolutionary Parallel Algorithm

一种进化并行算法的负荷预测方法研究

• 批准号: 09650331
• 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: 1997
• 资助国家: 日本
• 起止时间: 1997 至 1999
• 项目状态: 已结题
• 来源: https://kaken.nii.ac.jp/grant/KAKENHI-PROJECT-09650331/
• 关键词: load forecasting; time series analysis; fuzzy inference; tabu search; meta-heuristics; parallel computation; global optimization; learning system; 時系列予測

This project has proposed an efficient method for determining the location and number of fuzzy membership functions in simplified fuzzy inference with parallel tabu search(PTS). PTS is an improved algorithm of tabu search (TS) for solving a combinatorial optimization problem and has a couple of strategies. One is to decompose the neighborhood of TS into several subneighborhoods so that computational effort is reduced. The other is to introduce multiple tabu lengths into the TS algorithm and make solution candidates more diverse to obtain a better solution. As a result, PTS allows to improve the conventional TS in terms of solution accuracy and computational time. Specifically, PTS plays a key role to optimize the structure of simplified fuzzy inference for load forecasting in power systems. The PTS-based simplified fuzzy inference was successfully applied to real data of one-step ahead daily maximum load forecasting. A comparison between PTS and the conventional methods such as SA, GA and TS was made to demonstrate the effectiveness of PTS. The simulation results have shown the following:1. PTS is capable of evaluating a highly approximate solution of a global minimum efficiently.2. The proposed method allows to minimizes the maximum prediction error with the information criterion.3. The use of simplified fuzzy inference gives useful information of input variables through the membership functions.

该项目提出了一种在并行禁忌搜索（PTS）的简化模糊推理中确定模糊隶属函数的位置和数量的有效方法。 PTS 是禁忌搜索 (TS) 的改进算法，用于解决组合优化问题，并具有多种策略。一是将TS的邻域分解为多个子邻域，以减少计算量。另一种是在TS算法中引入多种禁忌长度，使候选解更加多样化，以获得更好的解。因此，PTS 可以在求解精度和计算时间方面改进传统的 TS。具体而言，PTS 在优化电力系统负荷预测的简化模糊推理结构方面发挥着关键作用。基于PTS的简化模糊推理已成功应用于一步步日最大负荷预测的实际数据。将PTS与SA、GA、TS等传统方法进行比较，证明PTS的有效性。仿真结果表明： 1． PTS能够有效地评估全局最小值的高度近似解。2．所提出的方法允许利用信息准则最小化最大预测误差。 3．使用简化的模糊推理通过隶属函数给出输入变量的有用信息。

项目成果

H.Mori: "K.Tomsovic and M.Y.Chow(eds.),"Tutorial on Fuzzy Logic Applications in Power Systems"IEEE Special Publication Catalog Number 99 TP 140-0. 6 (1999)

H.Mori：“K.Tomsovic 和 M.Y.Chow（编辑），“电力系统中模糊逻辑应用教程”IEEE 特别出版物目录号 99 TP 140-0.6 (1999)

H.Mori, et al.: "Accumulative Effect of Discomfort Index for Fuzzy Short-term Load Forecasting"Int'l J. of Engineering Intelligent Systems. 7. 233-238 (1999)

H.Mori 等人：“模糊短期负荷预测的不适指数累积效应”国际工程智能系统杂志。

H.Mori,et.al.,: "Accumulation Effect of Discomfort Index for Fuzzy Short-term Load Forecasting"Int'l J.of Engineering Intelligent Systems. Vol.7,No.4. 233-238 (1999)

H.Mori 等人：“模糊短期负荷预测的不适指数累积效应”国际工程智能系统杂志。

H.Mori: "Fuzzy Neural Network Applications to Power Systems"Proc. of 2000 IEEE PES Winter Meeting. 2. 1284-1288 (2000)

H.Mori：“模糊神经网络在电力系统中的应用”Proc。

森: "遺伝アルゴリズムとニューラルネット"コロナ社. 272 (1998)

森：《遗传算法和神经网络》Corona Publishing 272（1998）。