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Electricity price forecasting: A review of the state-of-the-art with a look into the future
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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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Heavy-tails and regime-switching in electricity prices
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Efficient estimation of Markov regime-switching models: An application to electricity spot prices
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Turning green: Agent-based modeling of the adoption of dynamic electricity tariffs
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Robust estimation and forecasting of the long-term seasonal component of electricity spot prices
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Self-citationModeling highly volatile and seasonal markets: evidence from the Nord Pool electricity market
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Self-citationA neural network-GARCH-based method for construction of Prediction Intervals
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When Supply Meets Demand: The Case of Hourly Spot Electricity Prices
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Day‐ahead price forecasting of electricity markets by a hybrid intelligent system
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Demand, generation and price in the Norwegian market for electric power
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Self-adaptive radial basis function neural network for short-term electricity price forecasting
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Prediction of system marginal price of electricity using wavelet transform analysis
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Revisiting the relationship between spot and futures prices in the Nord Pool electricity market
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Self-citationForecasting the Mean and the Variance of Electricity Prices in Deregulated Markets
Ruibal, C., & Mazumdar, M. (2008) · IEEE Transactions on Power Systems · doi:10.1109/tpwrs.2007.913195 · Cited by 71
FORECASTING SPOT ELECTRICITY PRICES WITH TIME SERIES MODELSPreprint
Weron, R., & Misiorek, A. (2005) · RePEc: Research Papers in Economics · Cited by 70
Self-citation
Flagged first, then by citations
2 of the 316 references couldn’t be matched to a known paper.
Research works printed but not linked: 7
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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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