Application of Discrete-Interval Moving Seasonalities to Spanish Electricity Demand Forecasting during Easter

Forecasting electricity demand through time series is a tool used by transmission system operators to establish future operating conditions. The accuracy of these forecasts is essential for the precise development of activity. However, the accuracy of the forecasts is enormously subject to the calen...

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Bibliographic Details
Published in:Energies (Basel) 2019-03, Vol.12 (6), p.1083
Main Authors: Trull, Óscar, García-Díaz, J. Carlos, Troncoso, Alicia
Format: Article
Language:English
Subjects:
Calendars
Coexistence
Decomposition
Economic forecasting
Economic models
Electric power demand
Electricity
Electricity consumption
electricity demand
Electricity distribution
Energy industry
exponential smoothing
Forecasting
Holidays & special occasions
Neural networks
Seasonal variations
Short term
Supply-demand forecasting
Time series
Values
Variables
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Summary:Forecasting electricity demand through time series is a tool used by transmission system operators to establish future operating conditions. The accuracy of these forecasts is essential for the precise development of activity. However, the accuracy of the forecasts is enormously subject to the calendar effect. The multiple seasonal Holt–Winters models are widely used due to the great precision and simplicity that they offer. Usually, these models relate this calendar effect to external variables that contribute to modification of their forecasts a posteriori. In this work, a new point of view is presented, where the calendar effect constitutes a built-in part of the Holt–Winters model. In particular, the proposed model incorporates discrete-interval moving seasonalities. Moreover, a clear example of the application of this methodology to situations that are difficult to treat, such as the days of Easter, is presented. The results show that the proposed model performs well, outperforming the regular Holt–Winters model and other methods such as artificial neural networks and Exponential Smoothing State Space Model with Box-Cox Transformation, ARMA Errors, Trend and Seasonal Components (TBATS) methods.
ISSN:1996-1073
1996-1073
DOI:10.3390/en12061083