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Electricity price forecasting: A review of the state-of-the-art with a look into the future
Open accessAbstract
A variety of methods and ideas have been tried for electricity price forecasting (EPF) over the last 15 years, with varying degrees of success. This review article aims to explain the complexity of available solutions, their strengths and weaknesses, and the opportunities and threats that the forecasting tools offer or that may be encountered. The paper also looks ahead and speculates on the directions EPF will or should take in the next decade or so. In particular, it postulates the need for objective comparative EPF studies involving (i) the same datasets, (ii) the same robust error evaluation procedures, and (iii) statistical testing of the significance of one model’s outperformance of another.
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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 [838]
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Retracted
Metadata
- Authors
- Weron, R.
- Affiliations
- Wrocław University of Science and Technology (PL)
- Corresponding author
- Weron, Rafał
- ORCID
- Weron, Rafał
- Venue
- International Journal of Forecasting
- Publisher
- Elsevier BV
- Published
- 2014-10
- Volume
- 30
- Issue
- 4
- Pages
- 1030-1081
- ISSN
- 0169-2070 (ISSN-L)
- OpenAlex
- W1568559004
- Cited by
- 1,646
- References
- 316
- Citations by year
- 2026: 188 · 2025: 150 · 2024: 128 · 2023: 121 · 2022: 148 · 2021: 158 · 2020: 176 · 2019: 159 · 2018: 153 · 2017: 110 · 2016: 97 · 2015: 49 · 2014: 7
- FWCI
- 46.59
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- International Journal of Forecasting (publishedVersion, cc-by-nc-nd)
- RePEc: Research Papers in Economics (submittedVersion)
- Funders
- National Science Centre (NCN, Poland) (2011/01/B/HS4/01077)
- Topics
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Energy Load and Power Forecasting
- Social Sciences › Decision Sciences › Management Science and Operations Research › Forecasting Techniques and Applications
- Social Sciences › Decision Sciences › Management Science and Operations Research › Stock Market Forecasting Methods
- Keywords
- electricity price forecasting, forecasting methods, forecast evaluation, model comparison, significance testing, future research directions
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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