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Forecasting spot electricity prices: Deep learning approaches and empirical comparison of traditional algorithms
Open accessAbstract
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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“ML can also help control batteries located at solar and wind farms to increase these farms' profits, for instance by storing their electricity when prices are low and then selling it when prices are high; prior work has used ML to forecast electricity prices [460,838] or RL to control batteries based on current and historical prices [825].”
From Tackling climate change with machine learning (2023) · cites this paper as [460]
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Metadata
- 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)
- 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
- 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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