Cited by 1,646Energy Load and Power ForecastingForecasting Techniques and ApplicationsStock Market Forecasting Methods

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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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Weron, Rafał·2014·International Journal of Forecasting

DOI 10.1016/j.ijforecast.2014.08.008

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Abstract

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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Generalized autoregressive conditional heteroskedasticity
Bollerslev, T. (1986) · Journal of Econometrics · doi:10.1016/0304-4076(86)90063-1 · Cited by 22,346
Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation
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
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Another look at measures of forecast accuracy
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Comparing Predictive Accuracy
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

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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.

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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)
DOI: 10.1016/j.ijforecast.2014.08.008
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: cc-by-nc-nd
www.elsevier.com/tdm/userlicense/1.0/ (tdm)
creativecommons.org/licenses/by-nc-nd/3.0/ (vor)
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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Electricity price forecasting: A review of the state-of-the-art with a look into the future

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