{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,22]],"date-time":"2026-01-22T08:39:45Z","timestamp":1769071185433,"version":"3.49.0"},"reference-count":31,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2021,6,20]],"date-time":"2021-06-20T00:00:00Z","timestamp":1624147200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["JSAN"],"abstract":"<jats:p>The Internet of Things (IoT) is revolutionising how energy is delivered from energy producers and used throughout residential households. Optimising the residential energy consumption is a crucial step toward having greener and sustainable energy production. Such optimisation requires a household-centric energy management system as opposed to a one-rule-fits all approach. In this paper, we propose a data-driven multi-layer digital twin of the energy system that aims to mirror households\u2019 actual energy consumption in the form of a household digital twin (HDT). When linked to the energy production digital twin (EDT), HDT empowers the household-centric energy optimisation model to achieve the desired efficiency in energy use. The model intends to improve the efficiency of energy production by flattening the daily energy demand levels. This is done by collaboratively reorganising the energy consumption patterns of residential homes to avoid peak demands whilst accommodating the resident needs and reducing their energy costs. Indeed, our system incorporates the first HDT model to gauge the impact of various modifications on the household energy bill and, subsequently, on energy production. The proposed energy system is applied to a real-world IoT dataset that spans over two years and covers seventeen households. Our conducted experiments show that the model effectively flattened the collective energy demand by 20.9% on synthetic data and 20.4% on a real dataset. At the same time, the average energy cost per household was reduced by 10.7% for the synthetic data and 17.7% for the real dataset.<\/jats:p>","DOI":"10.3390\/jsan10020037","type":"journal-article","created":{"date-parts":[[2021,6,20]],"date-time":"2021-06-20T21:50:15Z","timestamp":1624225815000},"page":"37","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":81,"title":["Digital Twin-Driven Decision Making and Planning for Energy Consumption"],"prefix":"10.3390","volume":"10","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7398-5283","authenticated-orcid":false,"given":"Yasmin","family":"Fathy","sequence":"first","affiliation":[{"name":"Department of Engineering, University of Cambridge, Cambridge CB3 0FA, UK"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0908-3207","authenticated-orcid":false,"given":"Mona","family":"Jaber","sequence":"additional","affiliation":[{"name":"Electronic Engineering and Computer Science School, Queen Mary University of London, London E1 4FZ, UK"}]},{"given":"Zunaira","family":"Nadeem","sequence":"additional","affiliation":[{"name":"Electronic Engineering and Computer Science School, Queen Mary University of London, London E1 4FZ, UK"}]}],"member":"1968","published-online":{"date-parts":[[2021,6,20]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"123369","DOI":"10.1109\/ACCESS.2019.2963045","article-title":"Short-Term Prediction of Residential Power Energy Consumption via CNN and Multi-Layer Bi-Directional LSTM Networks","volume":"8","author":"Ullah","year":"2020","journal-title":"IEEE Access"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"25521","DOI":"10.1109\/ACCESS.2020.2969728","article-title":"Setting the Time-of-Use Tariff Rates with NoSQL and Machine Learning to a Sustainable Environment","volume":"8","author":"Oprea","year":"2020","journal-title":"IEEE Access"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Chen, X., Li, J., Yang, A., and Zhang, Q. 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