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Forecasting spot electricity prices: Deep learning approaches and empirical comparison of traditional algorithms

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Lago, Jesus, De Ridder, Fjo, De Schutter, Bart·2018·Applied Energy

DOI 10.1016/j.apenergy.2018.02.069

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Abstract

In this paper, a novel modeling framework for forecasting electricity prices is proposed. While many predictive models have been already proposed to perform this task, the area of deep learning algorithms remains yet unexplored. To fill this scientific gap, we propose four different deep learning models for predicting electricity prices and we show how they lead to improvements in predictive accuracy. In addition, we also consider that, despite the large number of proposed methods for predicting electricity prices, an extensive benchmark is still missing. To tackle that, we compare and analyze the accuracy of 27 common approaches for electricity price forecasting. Based on the benchmark results, we show how the proposed deep learning models outperform the state-of-the-art methods and obtain results that are statistically significant. Finally, using the same results, we also show that: (i) machine learning methods yield, in general, a better accuracy than statistical models; (ii) moving average terms do not improve the predictive accuracy; (iii) hybrid models do not outperform their simpler counterparts.

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Authors: Lago, J., De Ridder, F., & De Schutter, B.
Affiliations: Flemish Institute for Technological Research (BE)
Corresponding author: Lago, Jesus
ORCID: Lago, Jesus
Venue: Applied Energy
Publisher: Elsevier BV
Published: 2018-07
Volume: 221
Pages: 386-405
ISSN: 0306-2619 (ISSN-L)
DOI: 10.1016/j.apenergy.2018.02.069
OpenAlex: W2799827709
Cited by: 642
References: 93
Citations by year: 2026: 64 · 2025: 80 · 2024: 74 · 2023: 75 · 2022: 83 · 2021: 84 · 2020: 100 · 2019: 65 · 2018: 16
FWCI: 25.45
Type: journal article
Language: en
Update notices: erratum (2018-11-01)
Open access: hybrid
doi.org
Licence: cc-by-nc-nd
www.elsevier.com/tdm/userlicense/1.0/ (tdm)
www.elsevier.com/legal/tdmrep-license (tdm)
creativecommons.org/licenses/by-nc-nd/4.0/ (vor)
Locations: Applied Energy (publishedVersion, cc-by-nc-nd)
RePEc: Research Papers in Economics (submittedVersion)
Research Repository (Delft University of Technology) (submittedVersion)
Data Archiving and Networked Services (DANS) (submittedVersion, cc-by-nc-nd)
Greater South Information System
Funders: European Union’s Horizon 2020 (675318)
Topics: Physical Sciences › Engineering › Electrical and Electronic Engineering › Energy Load and Power Forecasting
Social Sciences › Decision Sciences › Management Science and Operations Research › Stock Market Forecasting Methods
Social Sciences › Decision Sciences › Management Science and Operations Research › Forecasting Techniques and Applications
Keywords: deep learning, hybrid forecasting model, prediction accuracy

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Forecasting spot electricity prices: Deep learning approaches and empirical comparison of traditional algorithms

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