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Forecasting Electricity Prices: A Machine Learning Approach

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NOVA Information Management School (NOVA IMS), Universidade Nova de Lisboa, Campus de Campolide, 1070-312 Lisboa, Portugal
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University of Ljubljana, School of Economics and Business, Kardeljeva ploščad, 17 SI-1000 Ljubljana, Slovenia
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Authors to whom correspondence should be addressed.
Algorithms 2020, 13(5), 119; https://doi.org/10.3390/a13050119
Received: 22 April 2020 / Revised: 5 May 2020 / Accepted: 7 May 2020 / Published: 8 May 2020
(This article belongs to the Special Issue Genetic Programming)
The electricity market is a complex, evolutionary, and dynamic environment. Forecasting electricity prices is an important issue for all electricity market participants. In this study, we shed light on how to improve electricity price forecasting accuracy through the use of a machine learning technique—namely, a novel genetic programming approach. Drawing on empirical data from the largest EU energy markets, we propose a forecasting model that considers variables related to weather conditions, oil prices, and CO2 coupons and predicts energy prices 24 h ahead. We show that the proposed model provides more accurate predictions of future electricity prices than existing prediction methods. Our important findings will assist the electricity market participants in forecasting future price movements. View Full-Text
Keywords: energy sector; electricity prices; forecasting; machine learning; geometric semantic; based programming energy sector; electricity prices; forecasting; machine learning; geometric semantic; based programming
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Castelli, M.; Groznik, A.; Popovič, A. Forecasting Electricity Prices: A Machine Learning Approach. Algorithms 2020, 13, 119.

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