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Electric load forecasting with recency effect: A big data approach

Listed author(s):
  • Pu Wang
  • Bidong Liu
  • Tao Hong
Registered author(s):

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.

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File URL: http://www.im.pwr.wroc.pl/~hugo/RePEc/wuu/wpaper/HSC_15_08.pdf
File Function: Final version, 2015
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Paper provided by Hugo Steinhaus Center, Wroclaw University of Technology in its series HSC Research Reports with number HSC/15/08.

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Length: 30 pages
Date of creation: 03 Oct 2015
Publication status: Forthcoming in International Journal of Forecasting (2016).
Handle: RePEc:wuu:wpaper:hsc1508
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  1. Tao Hong, 2014. "Energy Forecasting: Past, Present, and Future," Foresight: The International Journal of Applied Forecasting, International Institute of Forecasters, issue 32, pages 43-48, Winter.
  2. Hong, Tao & Wang, Pu & White, Laura, 2015. "Weather station selection for electric load forecasting," International Journal of Forecasting, Elsevier, vol. 31(2), pages 286-295.
  3. Nedellec, Raphael & Cugliari, Jairo & Goude, Yannig, 2014. "GEFCom2012: Electric load forecasting and backcasting with semi-parametric models," International Journal of Forecasting, Elsevier, vol. 30(2), pages 375-381.
  4. Hong, Tao & Pinson, Pierre & Fan, Shu, 2014. "Global Energy Forecasting Competition 2012," International Journal of Forecasting, Elsevier, vol. 30(2), pages 357-363.
  5. Ramanathan, Ramu & Engle, Robert & Granger, Clive W. J. & Vahid-Araghi, Farshid & Brace, Casey, 1997. "Shorte-run forecasts of electricity loads and peaks," International Journal of Forecasting, Elsevier, vol. 13(2), pages 161-174, June.
  6. Ben Taieb, Souhaib & Hyndman, Rob J., 2014. "A gradient boosting approach to the Kaggle load forecasting competition," International Journal of Forecasting, Elsevier, vol. 30(2), pages 382-394.
  7. Tao Hong & Jason Wilson & Jingrui Xie, 2013. "Long term probabilistic load forecasting and normalization with hourly information," HSC Research Reports HSC/13/13, Hugo Steinhaus Center, Wroclaw University of Technology.
  8. Hong, Tao & Fan, Shu, 2016. "Probabilistic electric load forecasting: A tutorial review," International Journal of Forecasting, Elsevier, vol. 32(3), pages 914-938.
  9. Bidong Liu & Jakub Nowotarski & Tao Hong & Rafal Weron, 2015. "Probabilistic load forecasting via Quantile Regression Averaging on sister forecasts," HSC Research Reports HSC/15/01, Hugo Steinhaus Center, Wroclaw University of Technology.
  10. Rafal Weron, 2006. "Modeling and Forecasting Electricity Loads and Prices: A Statistical Approach," HSC Books, Hugo Steinhaus Center, Wroclaw University of Technology, number hsbook0601.
  11. Weron, Rafał, 2014. "Electricity price forecasting: A review of the state-of-the-art with a look into the future," International Journal of Forecasting, Elsevier, vol. 30(4), pages 1030-1081.
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