The DOI matches its Crossref record; 314 of the 316 references it cites were found in a registry or an index.
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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Cites
Generalized autoregressive conditional heteroskedasticity
Bollerslev, T. (1986) · Journal of Econometrics · doi:10.1016/0304-4076(86)90063-1 · Cited by 22,346
Engle, R. F. (1982) · Econometrica · doi:10.2307/1912773 · Cited by 20,795
A New Approach to the Economic Analysis of Nonstationary Time Series and the Business Cycle
Hamilton, J. D. (1989) · Econometrica · doi:10.2307/1912559 · Cited by 9,696
A Theory of the Term Structure of Interest Rates
Cox, J. C., Ingersoll, J. E., & Ross, S. A. (1985) · Econometrica · doi:10.2307/1911242 · Cited by 8,591
An equilibrium characterization of the term structure
Vasicek, O. A. (1977) · Journal of Financial Economics · doi:10.1016/0304-405x(77)90016-2 · Cited by 6,109
Strictly Proper Scoring Rules, Prediction, and Estimation
Gneiting, T., & Raftery, A. E. (2007) · Journal of the American Statistical Association · doi:10.1198/016214506000001437 · Cited by 5,793
Another look at measures of forecast accuracy
Hyndman, R. J., & Koehler, A. B. (2006) · International Journal of Forecasting · doi:10.1016/j.ijforecast.2006.03.001 · Cited by 5,757
Diebold, F. X., & Mariano, R. S. (1995) · Journal of Business and Economic Statistics · doi:10.1080/07350015.1995.10524599 · Cited by 5,240
Artificial neural networks: a tutorial
Jain, A. K., Mao, J., & Mohiuddin, K. M. (1996) · Computer · doi:10.1109/2.485891 · Cited by 5,095
Forecasting principles and practice
Hyndman, R. J., & Athanasopoulos, G. (2018) · Cited by 4,405
Research works printed but not linked: 7
- Czapaj, 2009, On the possibility of short-term electricity prices forecasting on Polish parquets considering the German EEX AG exchange, Przeglad Elektrotechniczny, 85, 140
- Garcia-Alcalde, A., Ventosa, M., Rivier, M., Ramos, A., & Relanõ, G. (2002). Fitting electricity market models. A conjectural variations approach. Proceedings of the 14th PSCC conference, Seville.
- Huisman, 2003, Option pricing for power prices with spikes, Energy Power Risk Management, 7, 12
- Huisman, 2007, Hourly electricity prices in day-ahead markets, Energy Economics, 29, 240, 10.1016/j.eneco.2006.08.005 · DOI
- Koop, 2004, Forecasting in dynamic factor models using Bayesian model averaging, The Econometrics Journal, 7, 550, 10.1111/j.1368-423X.2004.00143.x · DOI
- Maciejowska, K., Nowotarski, J., & Weron, R. (2014). Probabilistic forecasting of electricity spot prices using Factor Quantile Regression Averaging, International Journal of Forecasting (submitted for publication). Working paper version available from RePEc: http://ideas.repec.org/p/wuu/wpaper/hsc1409.html.
- Schmutz, A., & Elkuch, P. (2004). Electricity price forecasting: application and experience in the European power markets. In Proceedings of the 6th IAEE European Conference, Zürich.
Cited by
Sorted by citations
Probabilistic electric load forecasting: A tutorial review
Hong, T., & Shu, F. (2016) · International Journal of Forecasting · doi:10.1016/j.ijforecast.2015.11.011 · Cited by 1,460
Probabilistic energy forecasting: Global Energy Forecasting Competition 2014 and beyond
Hong, T., Pinson, P., Shu, F., Zareipour, H., Troccoli, A., & Hyndman, R. J. (2016) · International Journal of Forecasting · doi:10.1016/j.ijforecast.2016.02.001 · Cited by 1,068
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
Bouktif, S., Fiaz, A., Ouni, A., & Serhani, M. A. (2018) · Energies · doi:10.3390/en11071636 · Cited by 941
A hybrid method of exponential smoothing and recurrent neural networks for time series forecasting
Smyl, S. (2019) · International Journal of Forecasting · doi:10.1016/j.ijforecast.2019.03.017 · Cited by 790
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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