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A Novel Hybrid Algorithm to Forecast Functional Time Series Based on Pattern Sequence Similarity with Application to Electricity Demand
Open AccessArticle

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

1
Department of Applied Statistics, Operational Research and Quality, Universitat Politècnica de València, 46022 Valencia, Spain
2
Division of Computer Science, Universidad Pablo de Olavide, ES-41013 Seville, Spain
*
Author to whom correspondence should be addressed.
Energies 2019, 12(6), 1083; https://doi.org/10.3390/en12061083
Received: 7 March 2019 / Revised: 18 March 2019 / Accepted: 19 March 2019 / Published: 21 March 2019
(This article belongs to the Special Issue Data Science and Big Data in Energy Forecasting with Applications)
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. View Full-Text
Keywords: time series; forecasting; exponential smoothing; electricity demand time series; forecasting; exponential smoothing; electricity demand
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Trull, Ó.; García-Díaz, J.C.; Troncoso, A. Application of Discrete-Interval Moving Seasonalities to Spanish Electricity Demand Forecasting during Easter. Energies 2019, 12, 1083.

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