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Starting with a data-selection algorithm, a multi-objective genetic algorithm is then executed, performing topology and feature selection, as well as parameter estimation. From the set of non-dominated or preferential models, a smaller sub-set is chosen to form the ensemble. Prediction intervals for the ensemble are obtained using the covariance method. This procedure is illustrated in the design of four different models, required for energy management systems. Excellent results were obtained by this methodology, superseding the existing alternatives. Further research will incorporate a robustness criterion in MOGA, and will incorporate the prediction intervals in predictive control techniques.<\/jats:p>","DOI":"10.3390\/inventions8040096","type":"journal-article","created":{"date-parts":[[2023,7,26]],"date-time":"2023-07-26T01:11:26Z","timestamp":1690333886000},"page":"96","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Designing Robust Forecasting Ensembles of Data-Driven Models with a Multi-Objective Formulation: An Application to Home Energy Management Systems"],"prefix":"10.3390","volume":"8","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6308-8666","authenticated-orcid":false,"given":"Antonio","family":"Ruano","sequence":"first","affiliation":[{"name":"Faculty of Science & Technology, University of Algarve, 8005-294 Faro, Portugal"},{"name":"IDMEC, Instituto Superior T\u00e9cnico, Universidade de Lisboa, 1049-001 Lisbon, 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":[[2023,7,25]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"102010","DOI":"10.1016\/j.scs.2019.102010","article-title":"A review on machine learning forecasting growth trends and their real-time applications in different energy systems","volume":"54","author":"Ahmad","year":"2020","journal-title":"Sustain. 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