One-day-ahead electricity demand forecasting in holidays using discrete-interval moving seasonalities

Oscar Trull, J. Carlos García-Díaz and Alicia Troncoso

Energy, 2021, vol. 231, issue C

Abstract: Transmission System Operators provide forecasts of electricity demand to the electricity system. The producers and sellers use this information to establish the next day production units planning and prices. The results obtained are very accurate. However, they have a great deal with special events forecasting. Special events produce anomalous load conditions, and the models used to provide predictions must react properly against these situations. In this article, a new forecasting method based on multiple seasonal Holt-Winters modelling including discrete-interval moving seasonalities is applied to the Spanish hourly electricity demand to predict holidays with a 24-h prediction horizon. It allows the model to integrate the anomalous load within the model. The main results show how the new proposal outperforms regular methods and reduces the forecasting error from 9.5% to under 5% during holidays.

Keywords: Time series; Forecasting; Electricity demand; Anomalous load (search for similar items in EconPapers)
Date: 2021
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Citations: View citations in EconPapers (5)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:231:y:2021:i:c:s0360544221012147

DOI: 10.1016/j.energy.2021.120966

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