{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,20]],"date-time":"2026-06-20T11:33:45Z","timestamp":1781955225418,"version":"3.54.5"},"reference-count":57,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2020,5,8]],"date-time":"2020-05-08T00:00:00Z","timestamp":1588896000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001871","name":"Funda\u00e7\u00e3o para a Ci\u00eancia e a Tecnologia","doi-asserted-by":"publisher","award":["DSAIPA\/DS\/0022\/2018"],"award-info":[{"award-number":["DSAIPA\/DS\/0022\/2018"]}],"id":[{"id":"10.13039\/501100001871","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004329","name":"Javna Agencija za Raziskovalno Dejavnost RS","doi-asserted-by":"publisher","award":["P5-0410"],"award-info":[{"award-number":["P5-0410"]}],"id":[{"id":"10.13039\/501100004329","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Algorithms"],"abstract":"<jats:p>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\u2014namely, 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.<\/jats:p>","DOI":"10.3390\/a13050119","type":"journal-article","created":{"date-parts":[[2020,5,8]],"date-time":"2020-05-08T11:26:00Z","timestamp":1588937160000},"page":"119","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":16,"title":["Forecasting Electricity Prices: A Machine Learning Approach"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8793-1451","authenticated-orcid":false,"given":"Mauro","family":"Castelli","sequence":"first","affiliation":[{"name":"NOVA Information Management School (NOVA IMS), Universidade Nova de Lisboa, Campus de Campolide, 1070-312 Lisboa, Portugal"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ale\u0161","family":"Groznik","sequence":"additional","affiliation":[{"name":"University of Ljubljana, School of Economics and Business, Kardeljeva plo\u0161\u010dad, 17 SI-1000 Ljubljana, Slovenia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ale\u0161","family":"Popovi\u010d","sequence":"additional","affiliation":[{"name":"University of Ljubljana, School of Economics and Business, Kardeljeva plo\u0161\u010dad, 17 SI-1000 Ljubljana, Slovenia"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2020,5,8]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.dss.2015.07.011","article-title":"Six thinking hats: A novel metalearner for intelligent decision support in electricity markets","volume":"79","author":"Pinto","year":"2015","journal-title":"Decis. 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