{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,8]],"date-time":"2026-05-08T17:54:02Z","timestamp":1778262842780,"version":"3.51.4"},"reference-count":54,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2022,4,27]],"date-time":"2022-04-27T00:00:00Z","timestamp":1651017600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100004071","name":"Khon Kaen University","doi-asserted-by":"publisher","award":["Ph.D.Ee-1\/2564"],"award-info":[{"award-number":["Ph.D.Ee-1\/2564"]}],"id":[{"id":"10.13039\/501100004071","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Computers"],"abstract":"<jats:p>Electric energy demand forecasting is very important for electric utilities to procure and supply electric energy for consumers sufficiently, safely, reliably, and continuously. Consequently, the processing time and accuracy of the forecast system are essential to consider when applying in real power system operations. Nowadays, the Extreme Learning Machine (ELM) is significant for forecasting as it provides an acceptable value of forecasting and consumes less computation time when compared with the state-of-the-art forecasting models. However, the result of electric energy demand forecasting from the ELM was unstable and its accuracy was increased by reducing overfitting of the ELM model. In this research, metaheuristic optimization combined with the ELM is proposed to increase accuracy and reduce the cause of overfitting of three forecasting models, composed of the Jellyfish Search Extreme Learning Machine (JS-ELM), the Harris Hawk Extreme Learning Machine (HH-ELM), and the Flower Pollination Extreme Learning Machine (FP-ELM). The actual electric energy demand datasets in Thailand were collected from 2018 to 2020 and used to test and compare the performance of the proposed and state-of-the-art forecasting models. The overall results show that the JS-ELM provides the best minimum root mean square error compared with the state-of-the-art forecasting models. Moreover, the JS-ELM consumes the appropriate processing time in this experiment.<\/jats:p>","DOI":"10.3390\/computers11050066","type":"journal-article","created":{"date-parts":[[2022,4,27]],"date-time":"2022-04-27T13:40:57Z","timestamp":1651066857000},"page":"66","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":21,"title":["Metaheuristic Extreme Learning Machine for Improving Performance of Electric Energy Demand Forecasting"],"prefix":"10.3390","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7463-8993","authenticated-orcid":false,"given":"Sarunyoo","family":"Boriratrit","sequence":"first","affiliation":[{"name":"Department of Electrical Engineering, Faculty of Engineering, Khon Kaen University, Khon Kaen 40002, Thailand"},{"name":"Provincial Electricity Authority of Thailand (PEA), Bangkok 10900, Thailand"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9138-414X","authenticated-orcid":false,"given":"Chitchai","family":"Srithapon","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering, KTH Royal Institute of Technology, 11428 Stockholm, Sweden"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Pradit","family":"Fuangfoo","sequence":"additional","affiliation":[{"name":"Provincial Electricity Authority of Thailand (PEA), Bangkok 10900, Thailand"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9258-7141","authenticated-orcid":false,"given":"Rongrit","family":"Chatthaworn","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering, Faculty of Engineering, Khon Kaen University, Khon Kaen 40002, Thailand"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2022,4,27]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Pritima, D., Krishnan, P.G., Padmanabhan, P., and Stalin, B. 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