Improving short term load forecast accuracy via combining sister forecasts

Jakub Nowotarski, Bidong Liu, Rafał Weron and Tao Hong

No HSC/15/05, HSC Research Reports from Hugo Steinhaus Center, Wroclaw University of Science and Technology

Abstract: Although combining forecasts is well-known to be an effective approach to improving forecast accuracy, the literature and case studies on combining load forecasts are very limited. In this paper, we investigate the performance of combining so-called sister load forecasts with eight methods: three variants of arithmetic averaging, four regression based and one performance based method. Through comprehensive analysis of two case studies developed from public data (Global Energy Forecasting Competition 2014 and ISO New England), we demonstrate that combing sister forecasts outperforms the benchmark methods significantly in terms of forecasting accuracy measured by Mean Absolute Percentage Error. With the power to improve accuracy of individual forecasts and the advantage of easy generation, combining sister load forecasts has a high academic and practical value for researchers and practitioners.

Keywords: Electric load forecasting; Forecast combination; Sister forecast (search for similar items in EconPapers)
JEL-codes: C22 C32 C53 Q47 (search for similar items in EconPapers)
Pages: 17 pages
Date: 2015-05-03
New Economics Papers: this item is included in nep-ene and nep-ets
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Citations: View citations in EconPapers (1)

Published as J. Nowotarski, B. Liu, R. Weron, T. Hong (2016) Improving short term load forecast accuracy via combining sister forecasts, Energy 98, 40-49 (doi: 10.1016/j.energy.2015.12.142).

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http://www.im.pwr.wroc.pl/~hugo/RePEc/wuu/wpaper/HSC_15_05.pdf Revised version, 2015-07-19 (application/pdf)

Related works:
Journal Article: Improving short term load forecast accuracy via combining sister forecasts (2016)
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