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 journalArticle

45 Citations (Scopus)

Abstract

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
Volume27
Issue number2
DOIs
Publication statusPublished - 2012 May 1

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Feature extraction
Polynomials
Economics

Keywords

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

ASJC Scopus subject areas

  • Electrical and Electronic Engineering
  • Energy Engineering and Power Technology

Cite this

Holiday load forecasting using fuzzy polynomial regression with weather feature selection and adjustment. / Wi, Young Min; Joo, Sung-Kwan; Song, Kyung Bin.

In: IEEE Transactions on Power Systems, Vol. 27, No. 2, 6107608, 01.05.2012, p. 596-603.

Research output: Contribution to journalArticle

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