| Project/Area Number |
09650331
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| Research Category |
Grant-in-Aid for Scientific Research (C)
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| Allocation Type | Single-year Grants |
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| Section | 一般 |
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| Research Field |
電力工学・電気機器工学
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| Research Institution | Meiji University |
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Principal Investigator |
MORI Hiroyuki Meiji University, School of Science & Technology, Electrical & Electronics, Professor, 理工学部, 教授 (70174381)
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| Project Period (FY) |
1997 – 1999
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| Project Status |
Completed (Fiscal Year 1999)
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| Budget Amount *help |
¥2,700,000 (Direct Cost: ¥2,700,000)
Fiscal Year 1999: ¥600,000 (Direct Cost: ¥600,000)
Fiscal Year 1998: ¥600,000 (Direct Cost: ¥600,000)
Fiscal Year 1997: ¥1,500,000 (Direct Cost: ¥1,500,000)
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| Keywords | load forecasting / time series analysis / fuzzy inference / tabu search / meta-heuristics / parallel computation / global optimization / learning system / 時系列予測 |
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| Research Abstract |
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.
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