Holiday load forecasting using fuzzy polynomial regression with weather feature selection and adjustment

Young Min Wi, Sung Kwan Joo, Kyung Bin Song

Research output: Contribution to journalArticlepeer-review

100 Citations (Scopus)


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.

Original languageEnglish
Article number6107608
Pages (from-to)596-603
Number of pages8
JournalIEEE Transactions on Power Systems
Issue number2
Publication statusPublished - 2012 May

Bibliographical note

Funding Information:
Manuscript received May 28, 2010; revised November 29, 2010, May 11, 2011, and October 12, 2011; accepted October 12, 2011. Date of publication December 16, 2011; date of current version April 18, 2012. This work was supported by the Human Resources Development of the Korea Institute of Energy Technology Evaluation and Planning (KETEP) grant funded by the Korea government Ministry of Knowledge Economy (No. 20114010203110 and No. 20114010203010). Paper no. TPWRS-00424-2010.


  • Fuzzy polynomial regression
  • Mahalanobis distance
  • load forecasting
  • mutual information

ASJC Scopus subject areas

  • Energy Engineering and Power Technology
  • Electrical and Electronic Engineering


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