{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T02:32:39Z","timestamp":1760236359019,"version":"build-2065373602"},"reference-count":119,"publisher":"MDPI AG","issue":"22","license":[{"start":{"date-parts":[[2021,11,16]],"date-time":"2021-11-16T00:00:00Z","timestamp":1637020800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Programa Operacional Portugal 2020 and Operational Program CRESC Algarve 2020","award":["72581\/2020","39578\/2018"],"award-info":[{"award-number":["72581\/2020","39578\/2018"]}]},{"name":"Funda\u00e7\u00e3o para a Ci\u00eancia e Tecnologia","award":["UID\/EMS\/50022\/2020, through IDMEC, under LAETA"],"award-info":[{"award-number":["UID\/EMS\/50022\/2020, through IDMEC, under LAETA"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Energies"],"abstract":"<jats:p>The increasing levels of energy consumption worldwide is raising issues with respect to surpassing supply limits, causing severe effects on the environment, and the exhaustion of energy resources. Buildings are one of the most relevant sectors in terms of energy consumption; as such, efficient Home or Building Management Systems are an important topic of research. This study discusses the use of ensemble techniques in order to improve the performance of artificial neural networks models used for energy forecasting in residential houses. The case study is a residential house, located in Portugal, that is equipped with PV generation and battery storage and controlled by a Home Energy Management System (HEMS). It has been shown that the ensemble forecasting results are superior to single selected models, which were already excellent. A simple procedure was proposed for selecting the models to be used in the ensemble, together with a heuristic to determine the number of models.<\/jats:p>","DOI":"10.3390\/en14227664","type":"journal-article","created":{"date-parts":[[2021,11,16]],"date-time":"2021-11-16T11:32:03Z","timestamp":1637062323000},"page":"7664","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":19,"title":["Design of Ensemble Forecasting Models for Home Energy Management Systems"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5904-2166","authenticated-orcid":false,"given":"Karol","family":"Bot","sequence":"first","affiliation":[{"name":"Faculty of Science & Technology, University of Algarve, 8005-294 Faro, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8543-8831","authenticated-orcid":false,"given":"Samira","family":"Santos","sequence":"additional","affiliation":[{"name":"Faculty of Science & Technology, University of Algarve, 8005-294 Faro, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6078-6813","authenticated-orcid":false,"given":"Inoussa","family":"Laouali","sequence":"additional","affiliation":[{"name":"Faculty of Science & Technology, University of Algarve, 8005-294 Faro, Portugal"},{"name":"SIGER, Faculty of Sciences and Technology, Sidi Mohamed Ben Abdellah University, Fez 1049-001, Morocco"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6308-8666","authenticated-orcid":false,"given":"Antonio","family":"Ruano","sequence":"additional","affiliation":[{"name":"Faculty of Science & Technology, University of Algarve, 8005-294 Faro, Portugal"},{"name":"IDMEC, Instituto Superior T\u00e9cnico, Universidade de Lisboa, 1950-044 Lisboa, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0014-9257","authenticated-orcid":false,"given":"Maria da Gra\u00e7a","family":"Ruano","sequence":"additional","affiliation":[{"name":"Faculty of Science & Technology, University of Algarve, 8005-294 Faro, Portugal"},{"name":"CISUC, University of Coimbra, 3030-290 Coimbra, Portugal"}]}],"member":"1968","published-online":{"date-parts":[[2021,11,16]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"709","DOI":"10.1016\/j.energy.2018.09.144","article-title":"Forecasting energy consumption time series using machine learning techniques based on usage patterns of residential householders","volume":"165","author":"Chou","year":"2018","journal-title":"Energy"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"556","DOI":"10.1016\/j.energy.2017.05.123","article-title":"Smart energy and smart energy systems","volume":"137","author":"Lund","year":"2017","journal-title":"Energy"},{"key":"ref_3","unstructured":"Lund, H. 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