Forecasting Day-Ahead Electricity Prices: A Review of State-of-the-Art Algorithms, Best Practices and an Open-Access Benchmark

Reference

J. Lago, G. Marcjasz, B. De Schutter, and R. Weron, "Forecasting day-ahead electricity prices: A review of state-of-the-art algorithms, best practices and an open-access benchmark," Applied Energy, vol. 293, July 2021. Article 116983.

Abstract

While the field of electricity price forecasting has benefited from plenty of contributions in the last two decades, it arguably lacks a rigorous approach to evaluating new predictive algorithms. The latter are often compared using unique, not publicly available datasets and across too short and limited to one market test samples. The proposed new methods are rarely benchmarked against well established and well performing simpler models, the accuracy metrics are sometimes inadequate and testing the significance of differences in predictive performance is seldom conducted. Consequently, it is not clear which methods perform well nor what are the best practices when forecasting electricity prices. In this paper, we tackle these issues by performing a literature survey of state-of-the-art models, comparing state-of-the-art statistical and deep learning methods across multiple years and markets, and by putting forward a set of best practices. In addition, we make available the considered datasets, forecasts of the state-of-the-art models, and a specifically designed python toolbox, so that new algorithms can be rigorously evaluated in future studies.

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Erratum

• J. Lago, G. Marcjasz, B. De Schutter, and R. Weron, "Erratum to "Forecasting day-ahead electricity prices: A review of state-of-the-art algorithms, best practices and an open-access benchmark" [Appl. Energy 293 (2021) 116983]," WORking papers in Management Science (WORMS) WORMS/21/12, Department of Operations Research and Business Intelligence, Wroclaw University of Science and Technology, Wroclaw, Poland, 2021.  (online version ,  abstract,  bibtex,  tech. report (pdf))

Software toolbox

• J. Lago, G. Marcjasz, B. De Schutter, and R. Weron, "EPFTOOLBOX: The first open-access PYTHON library for driving research in electricity price forecasting (EPF)," WORMS Software (WORking papers in Management Science Software) WORMS/C/21/01, Department of Operations Research and Business Intelligence, Wroclaw University of Science and Technology, Wroclaw, Poland, 2021.  (online version ,  abstract,  bibtex)

BibTeX

@article{LagMar:21-011,
   author  = {Lago, Jesus and Marcjasz, Grzegorz and De Schutter, Bart and
              Weron, Rafa{\l}},
   title   = {Forecasting Day-Ahead Electricity Prices: {A} Review of
              State-of-the-Art Algorithms, Best Practices and an Open-Access
              Benchmark},
   journal = {Applied Energy},
   volume  = {293},
   month   = jul,
   year    = {2021},
   note    = {Article 116983}
   }

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