期刊
IEEE TRANSACTIONS ON POWER SYSTEMS
卷 27, 期 2, 页码 596-603出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TPWRS.2011.2174659
关键词
Fuzzy polynomial regression; load forecasting; Mahalanobis distance; mutual information
资金
- Human Resources Development of the Korea Institute of Energy Technology Evaluation and Planning (KETEP)
- Korea government Ministry of Knowledge Economy [20114010203110, 20114010203010]
- Korea Evaluation Institute of Industrial Technology (KEIT) [20114010203110] Funding Source: Korea Institute of Science & Technology Information (KISTI), National Science & Technology Information Service (NTIS)
The load forecasting problem is a complex nonlinear problem linked with social considerations, economic factors, and weather variations. In particular, load forecasting for holidays is a challenging task as only a small number of historical data is available for holidays compared with what is available for normal weekdays and weekends. This paper presents a fuzzy polynomial regression method with data selection based on Mahalanobis distance incorporating a dominant weather feature for holiday load forecasting. Selection of past weekday data relevant to a given holiday is critical for improvement of the accuracy of holiday load forecasting. In the paper, a data selection process incorporating a dominant weather feature is also proposed in order to improve the accuracy of the fuzzy polynomial regression method. The dominant weather feature for selection of historical data is identified by evaluating mutual information between various weather features and loads from season to season. The results of case studies are presented to show the effectiveness of the proposed method.
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