{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,31]],"date-time":"2025-12-31T12:22:47Z","timestamp":1767183767474,"version":"build-2065373602"},"reference-count":33,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2022,3,24]],"date-time":"2022-03-24T00:00:00Z","timestamp":1648080000000},"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 Tecnologia","doi-asserted-by":"publisher","award":["760"],"award-info":[{"award-number":["760"]}],"id":[{"id":"10.13039\/501100001871","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Energies"],"abstract":"<jats:p>In the energy sector, prosumers are becoming relevant entities for energy management systems since they can share energy with their citizen energy community (CEC). Thus, this paper proposes a novel methodology based on demand response (DR) participation in a CEC context, where unsupervised learning algorithms such as convolutional neural networks and k-means are used. This novel methodology can analyze future events on the grid and balance the consumption and generation using end-user flexibility. The end-users\u2019 invitations to the DR event were according to their ranking obtained through three metrics. These metrics were energy flexibility, participation ratio, and flexibility history of the end-users. During the DR event, a continuous balancing assessment is performed to allow the invitation of additional end-users. Real data from a CEC with 50 buildings were used, where the results demonstrated that the end-users\u2019 participation in two DR events allows reduction of energy costs by EUR 1.31, balancing the CEC energy resources.<\/jats:p>","DOI":"10.3390\/en15072380","type":"journal-article","created":{"date-parts":[[2022,3,25]],"date-time":"2022-03-25T00:05:18Z","timestamp":1648166718000},"page":"2380","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":13,"title":["Evaluation Metrics to Assess the Most Suitable Energy Community End-Users to Participate in Demand Response"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5574-6972","authenticated-orcid":false,"given":"Ruben","family":"Barreto","sequence":"first","affiliation":[{"name":"GECAD\u2014Research Group on Intelligent Engineering and Computing for Advanced Innovation and Development, Polytechnic of Porto (P.PORTO), P-4200-072 Porto, Portugal"}]},{"given":"Calvin","family":"Gon\u00e7alves","sequence":"additional","affiliation":[{"name":"GECAD\u2014Research Group on Intelligent Engineering and Computing for Advanced Innovation and Development, Polytechnic of Porto (P.PORTO), P-4200-072 Porto, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8597-3383","authenticated-orcid":false,"given":"Luis","family":"Gomes","sequence":"additional","affiliation":[{"name":"GECAD\u2014Research Group on Intelligent Engineering and Computing for Advanced Innovation and Development, Polytechnic of Porto (P.PORTO), P-4200-072 Porto, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5982-8342","authenticated-orcid":false,"given":"Pedro","family":"Faria","sequence":"additional","affiliation":[{"name":"GECAD\u2014Research Group on Intelligent Engineering and Computing for Advanced Innovation and Development, Polytechnic of Porto (P.PORTO), P-4200-072 Porto, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4560-9544","authenticated-orcid":false,"given":"Zita","family":"Vale","sequence":"additional","affiliation":[{"name":"GECAD\u2014Research Group on Intelligent Engineering and Computing for Advanced Innovation and Development, Polytechnic of Porto (P.PORTO), P-4200-072 Porto, Portugal"}]}],"member":"1968","published-online":{"date-parts":[[2022,3,24]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"50","DOI":"10.1016\/j.enpol.2015.05.010","article-title":"The European renewable energy target for 2030\u2014An impact assessment of the electricity sector","volume":"85","author":"Knopf","year":"2015","journal-title":"Energy Policy"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"131911","DOI":"10.1109\/ACCESS.2019.2940751","article-title":"Peer-to-Peer Energy Trading in Micro\/Mini-Grids for Local Energy Communities: A Review and Case Study of Nepal","volume":"7","author":"Shrestha","year":"2019","journal-title":"IEEE Access"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"100678","DOI":"10.1016\/j.esr.2021.100678","article-title":"Smart energy community: A systematic review with metanalysis","volume":"36","author":"Faria","year":"2021","journal-title":"Energy Strategy Rev."},{"key":"ref_4","unstructured":"US Department of Energy (2022, January 31). 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