The DOI matches its Crossref record; 95 of the 95 references it cites were found in a registry or an index.
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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Cites
Breiman, L. (2001) · Machine Learning · doi:10.1023/a:1010933404324 · Cited by 133,538
ImageNet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017) · Communications of the ACM · doi:10.1145/3065386 · Cited by 112,155
Hochreiter, S., & Schmidhuber, J. (1997) · Neural Computation · doi:10.1162/neco.1997.9.8.1735 · Cited by 102,318
Adam: A Method for Stochastic Optimization
Kingma, D. P., & Ba, J. (2014) · arXiv · doi:10.48550/arxiv.1412.6980 · Cited by 98,946
Scikit-learn: Machine Learning in Python
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Müller, A., Nothman, J., Louppe, G., et al. (2012) · arXiv · doi:10.48550/arxiv.1201.0490 · Cited by 71,016
XGBoost: A Scalable Tree Boosting System
Chen, T., & Guestrin, C. (2016) · Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining · doi:10.1145/2939672.2939785 · Cited by 54,569
Regression shrinkage and selection via the lasso
Tibshirani, R. (1996) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.2517-6161.1996.tb02080.x · Cited by 51,482
(2014) · Cited by 33,419
The Elements of Statistical Learning
Hastie, T., Friedman, J., & Tibshirani, R. (2001) · Springer Series in Statistics · doi:10.1007/978-0-387-21606-5 · Cited by 24,162
Multilayer feedforward networks are universal approximators
Hornik, K., Stinchcombe, M., & White, H. (1989) · Neural Networks · doi:10.1016/0893-6080(89)90020-8 · Cited by 21,728
Research works printed but not linked: 2
- Theano Development Team. Theano: a Python framework for fast computation of mathematical expressions; 2016. Available from: arXiv eprint arXiv:1605.02688. · arXiv
- Andersson J. A general-purpose software framework for dynamic optimization [PhD thesis]. Kasteelpark Arenberg 10, 3001-Heverlee (Belgium): Arenberg Doctoral School, KU Leuven, Department of Electrical Engineering (ESAT/SCD) and Optimization in Engineering Center; October 2013.
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Forecasting: theory and practice
Petropoulos, F., Apiletti, D., Assimakopoulos, V., Babaï, M. Z., Barrow, D. K., Taieb, S. B., Bergmeir, C., Bessa, R. J., Bijak, J., Boylan, J. E., et al. (2022) · International Journal of Forecasting · doi:10.1016/j.ijforecast.2021.11.001 · Cited by 1,187
Energy Forecasting: A Review and Outlook
Hong, T., Pinson, P., Wang, Y., Weron, R., Yang, D., & Zareipour, H. (2020) · IEEE Open Access Journal of Power and Energy · doi:10.1109/oajpe.2020.3029979 · Cited by 695
Lago, J., Marcjasz, G., Schutter, B. D., & Weron, R. (2021) · Applied Energy · doi:10.1016/j.apenergy.2021.116983 · Cited by 551
Self-citationDeep learning framework to forecast electricity demand
Bedi, J., & Toshniwal, D. (2019) · Applied Energy · doi:10.1016/j.apenergy.2019.01.113 · Cited by 507
A review of irregular time series data handling with gated recurrent neural networks
Weerakody, P. B., Wong, K. W., Wang, G., & Ela, W. P. (2021) · Neurocomputing · doi:10.1016/j.neucom.2021.02.046 · Cited by 477
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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