Electric load forecasting with recency effect: A big data approach

Pu Wang, Bidong Liu and Tao Hong

No HSC/15/08, HSC Research Reports from Hugo Steinhaus Center, Wroclaw University of Science and Technology

Abstract: Temperature plays a key role in driving electricity demand. We adopt "recency effect", a term originated from psychology, to denote the fact that electricity demand is affected by the temperatures of preceding hours. In the load forecasting literature, the temperature variables are often constructed in the form of lagged hourly temperatures and moving average temperatures. Over the past decades, computing power has been limiting the amount of temperature variables that can be used in a load forecasting model. In this paper, we present a comprehensive study on modeling recency effect through a big data approach. We take advantage of the modern computing power to answer a fundamental question: how many lagged hourly temperatures and/or moving average temperatures are needed in a regression model to fully capture recency effect without compromising the forecasting accuracy? Using the case study based on data from the load forecasting track of the Global Energy Forecasting Competition 2012, we first demonstrate that a model with recency effect outperforms its counterpart (a.k.a., Tao’s Vanilla Benchmark Model) in forecasting the load series at the top (aggregated) level by 18% to 21%. We then apply recency effect modeling to customize load forecasting models at low level of a geographic hierarchy, again showing the superiority over the benchmark model by 12% to 15% on average. Finally, we discuss four different implementations of the recency effect modeling by hour of a day.

Keywords: Electric load forecasting; Regression; Recency effect; Big data approach; Global Energy Forecasting Competition (search for similar items in EconPapers)
JEL-codes: C22 C32 C53 Q47 (search for similar items in EconPapers)
Pages: 30 pages
Date: 2015-10-03
New Economics Papers: this item is included in nep-ene and nep-for
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (5)

Forthcoming in International Journal of Forecasting (2016).

Downloads: (external link)
http://www.im.pwr.wroc.pl/~hugo/RePEc/wuu/wpaper/HSC_15_08.pdf Final version, 2015 (application/pdf)

Related works:
Journal Article: Electric load forecasting with recency effect: A big data approach (2016)
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:wuu:wpaper:hsc1508

Access Statistics for this paper

More papers in HSC Research Reports from Hugo Steinhaus Center, Wroclaw University of Science and Technology Contact information at EDIRC.
Bibliographic data for series maintained by Rafal Weron ().