Next Article in Journal
A Novel Data-Driven Magnetic Resonance Spectroscopy Signal Analysis Framework to Quantify Metabolite Concentration
Next Article in Special Issue
EEG Feature Extraction Using Genetic Programming for the Classification of Mental States
Previous Article in Journal
Distributional Reinforcement Learning with Ensembles
 
 
Article

Forecasting Electricity Prices: A Machine Learning Approach

1
NOVA Information Management School (NOVA IMS), Universidade Nova de Lisboa, Campus de Campolide, 1070-312 Lisboa, Portugal
2
University of Ljubljana, School of Economics and Business, Kardeljeva ploščad, 17 SI-1000 Ljubljana, Slovenia
*
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
Show Figures

Figure 1

MDPI and ACS Style

Castelli, M.; Groznik, A.; Popovič, A. Forecasting Electricity Prices: A Machine Learning Approach. Algorithms 2020, 13, 119. https://doi.org/10.3390/a13050119

AMA Style

Castelli M, Groznik A, Popovič A. Forecasting Electricity Prices: A Machine Learning Approach. Algorithms. 2020; 13(5):119. https://doi.org/10.3390/a13050119

Chicago/Turabian Style

Castelli, Mauro, Aleš Groznik, and Aleš Popovič. 2020. "Forecasting Electricity Prices: A Machine Learning Approach" Algorithms 13, no. 5: 119. https://doi.org/10.3390/a13050119

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop