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Electrical load forecasting by exponential smoothing with covariates

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  • Rainer Göb
  • Kristina Lurz
  • Antonio Pievatolo

Abstract

In the past, studies in short‐term electrical load forecasting have been rather sceptical on the use of meteorological covariates like temperature for short‐term forecasting purposes. The main reasons were time delays in data provision and the poor precision of meteorological forecasts. Both arguments have lost their impact, as new recent studies have shown. We explore the use of meteorological covariates in short‐term load forecasting based on the rather new method of exponential smoothing with covariates (ESCov). The existing ESCov model is refined by including multiple seasonalities. The method is empirically explored in the hourly prediction of the electrical consumption of customers from provinces of an Italian region. Copyright © 2013 John Wiley & Sons, Ltd.

Suggested Citation

  • Rainer Göb & Kristina Lurz & Antonio Pievatolo, 2013. "Electrical load forecasting by exponential smoothing with covariates," Applied Stochastic Models in Business and Industry, John Wiley & Sons, vol. 29(6), pages 629-645, November.
    • Handle: RePEc:wly:apsmbi:v:29:y:2013:i:6:p:629-645
    DOI: 10.1002/asmb.2008
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    Cited by:

    1. Mauro Bernardi & Francesco Lisi, 2020. "Point and Interval Forecasting of Zonal Electricity Prices and Demand Using Heteroscedastic Models: The IPEX Case," Energies, MDPI, vol. 13(23), pages 1-34, November.
    2. Wang, Jianguo & Han, Lincheng & Zhang, Xiuyu & Wang, Yingzhou & Zhang, Shude, 2023. "Electrical load forecasting based on variable T-distribution and dual attention mechanism," Energy, Elsevier, vol. 283(C).
    3. Brusaferri, Alessandro & Matteucci, Matteo & Spinelli, Stefano & Vitali, Andrea, 2022. "Probabilistic electric load forecasting through Bayesian Mixture Density Networks," Applied Energy, Elsevier, vol. 309(C).
    4. Ivana Kiprijanovska & Simon Stankoski & Igor Ilievski & Slobodan Jovanovski & Matjaž Gams & Hristijan Gjoreski, 2020. "HousEEC: Day-Ahead Household Electrical Energy Consumption Forecasting Using Deep Learning," Energies, MDPI, vol. 13(10), pages 1-29, May.
    5. Óscar Trull & J. Carlos García-Díaz & Alicia Troncoso, 2019. "Application of Discrete-Interval Moving Seasonalities to Spanish Electricity Demand Forecasting during Easter," Energies, MDPI, vol. 12(6), pages 1-16, March.
    6. Mengran Zhou & Tianyu Hu & Kai Bian & Wenhao Lai & Feng Hu & Oumaima Hamrani & Ziwei Zhu, 2021. "Short-Term Electric Load Forecasting Based on Variational Mode Decomposition and Grey Wolf Optimization," Energies, MDPI, vol. 14(16), pages 1-17, August.
    7. Kottath, Rahul & Singh, Priyanka, 2023. "Influencer buddy optimization: Algorithm and its application to electricity load and price forecasting problem," Energy, Elsevier, vol. 263(PC).
    8. Leonardo Brain García Fernández & Anna Diva Plasencia Lotufo & Carlos Roberto Minussi, 2023. "Development of a Short-Term Electrical Load Forecasting in Disaggregated Levels Using a Hybrid Modified Fuzzy-ARTMAP Strategy," Energies, MDPI, vol. 16(10), pages 1-30, May.

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