{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T11:35:54Z","timestamp":1785584154365,"version":"3.56.0"},"reference-count":189,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2020,12,15]],"date-time":"2020-12-15T00:00:00Z","timestamp":1607990400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100007776","name":"Pontificia Universidad Cat\u00f3lica de Valpara\u00edso","doi-asserted-by":"publisher","award":["PhD Grant"],"award-info":[{"award-number":["PhD Grant"]}],"id":[{"id":"10.13039\/501100007776","id-type":"DOI","asserted-by":"publisher"}]},{"name":"VRIEA PUCV","award":["Project 039.457\/2020"],"award-info":[{"award-number":["Project 039.457\/2020"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Entropy"],"abstract":"<jats:p>Electric power forecasting plays a substantial role in the administration and balance of current power systems. For this reason, accurate predictions of service demands are needed to develop better programming for the generation and distribution of power and to reduce the risk of vulnerabilities in the integration of an electric power system. For the purposes of the current study, a systematic literature review was applied to identify the type of model that has the highest propensity to show precision in the context of electric power forecasting. The state-of-the-art model in accurate electric power forecasting was determined from the results reported in 257 accuracy tests from five geographic regions. Two classes of forecasting models were compared: classical statistical or mathematical (MSC) and machine learning (ML) models. Furthermore, the use of hybrid models that have made significant contributions to electric power forecasting is identified, and a case of study is applied to demonstrate its good performance when compared with traditional models. Among our main findings, we conclude that forecasting errors are minimized by reducing the time horizon, that ML models that consider various sources of exogenous variability tend to have better forecast accuracy, and finally, that the accuracy of the forecasting models has significantly increased over the last five years.<\/jats:p>","DOI":"10.3390\/e22121412","type":"journal-article","created":{"date-parts":[[2020,12,15]],"date-time":"2020-12-15T09:12:57Z","timestamp":1608023577000},"page":"1412","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":134,"title":["A Systematic Review of Statistical and Machine Learning Methods for Electrical Power Forecasting with Reported MAPE Score"],"prefix":"10.3390","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1593-8109","authenticated-orcid":false,"given":"Eliana","family":"Vivas","sequence":"first","affiliation":[{"name":"Escuela de Ingenier\u00eda Inform\u00e1tica, Pontificia Universidad Cat\u00f3lica de Valpara\u00edso, Brasil 2950, Valpara\u00edso, Chile"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3047-8817","authenticated-orcid":false,"given":"H\u00e9ctor","family":"Allende-Cid","sequence":"additional","affiliation":[{"name":"Escuela de Ingenier\u00eda Inform\u00e1tica, Pontificia Universidad Cat\u00f3lica de Valpara\u00edso, Brasil 2950, Valpara\u00edso, Chile"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0350-6811","authenticated-orcid":false,"given":"Rodrigo","family":"Salas","sequence":"additional","affiliation":[{"name":"Escuela de Ingenier\u00eda C. Biom\u00e9dica, Universidad de Valpara\u00edso, Chacabuco 2092-2220, Valpara\u00edso, Chile"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2020,12,15]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.ijforecast.2005.06.006","article-title":"A comparison of univariate methods for forecasting electricity demand up to a day ahead","volume":"22","author":"Taylor","year":"2006","journal-title":"Int. J. Forecast."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1753","DOI":"10.1016\/j.rser.2010.12.008","article-title":"Optimization methods applied to renewable and sustainable energy: A review","volume":"15","author":"Banos","year":"2011","journal-title":"Renew. Sustain. 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