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

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. (c) 2021 Elsevier Ltd. All rights reserved.

Automated summary

Transmission System Operators provide forecasts of electricity demand, which are crucial for producers and sellers in planning and pricing. A new forecasting method based on Holt-Winters modelling is applied to predict holidays, showing improved accuracy compared to regular methods. The new proposal reduces forecasting error during holidays from 9.5% to under 5%.