{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,14]],"date-time":"2026-05-14T09:11:32Z","timestamp":1778749892578,"version":"3.51.4"},"reference-count":33,"publisher":"Wiley","issue":"6","license":[{"start":{"date-parts":[[2026,4,22]],"date-time":"2026-04-22T00:00:00Z","timestamp":1776816000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2026,4,22]],"date-time":"2026-04-22T00:00:00Z","timestamp":1776816000000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Expert Systems"],"published-print":{"date-parts":[[2026,6]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:p>Accurately assessing the energy consumption associated with public buildings and the services they provide is essential for supporting sustainable infrastructure planning. This work presents an integrated modelling framework that combines machine learning prediction of building energy use with a graph based representation of user mobility, allowing a comprehensive estimation of total energy demand. The approach includes building operations, service related energy, travel by users and staff and supply chain logistics. Building energy consumption is predicted through supervised models trained on a curated subset of commercial facilities selected for their similarity to public service environments. Mobility is modelled using a synthetic geographical network that encodes population distribution, available transportation modes, behavioural tendencies and relocation dynamics. The framework is applied to a university reorganization scenario, exploring alternative facility configurations and varying degrees of remote activity. Results indicate that user travel is generally the dominant contributor to total energy demand, while the importance of building energy increases as in person attendance decreases. Sensitivity analyses confirm the robustness of the optimal configurations under different behavioural assumptions. We stress that this is not actually an optimization algorithm, but a parameter sweep; in real situations, constraints may be applied, for instance for building availability, costs, opportunity of sharing services or specific goals. The methodology is further demonstrated in a real healthcare application, where the predictive model enables reliable estimation of building energy use in the absence of direct measurements. Overall, the proposed framework illustrates how data driven modelling and intelligent system techniques can support sustainable decision making for complex public service infrastructures.<\/jats:p>","DOI":"10.1111\/exsy.70266","type":"journal-article","created":{"date-parts":[[2026,4,23]],"date-time":"2026-04-23T01:20:42Z","timestamp":1776907242000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Predictive Modelling of Service Building and Users Mobility Related Energy Consumption Through Machine Learning and Graph Based Analysis"],"prefix":"10.1111","volume":"43","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4473-6258","authenticated-orcid":false,"given":"Valerio","family":"Bellandi","sequence":"first","affiliation":[{"name":"Computer Science Department Universit\u00e0 Degli Studi di Milano  Milano Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6477-3876","authenticated-orcid":false,"given":"Stefano","family":"Siccardi","sequence":"additional","affiliation":[{"name":"Computer Science Department Universit\u00e0 Degli Studi di Milano  Milano Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7830-3149","authenticated-orcid":false,"given":"Maria Giulia","family":"Vincini","sequence":"additional","affiliation":[{"name":"Division of Radiation Oncology Milan, Italy IEO European Institute of Oncology IRCCS  Milano Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Federico","family":"Mastroleo","sequence":"additional","affiliation":[{"name":"Division of Radiation Oncology Milan, Italy IEO European Institute of Oncology IRCCS  Milano Italy"},{"name":"Department of Oncology and Hemato\u2010Oncology Universit\u00e0 Degli Studi di Milano  Milano Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5339-8038","authenticated-orcid":false,"given":"Giulia","family":"Marvaso","sequence":"additional","affiliation":[{"name":"Division of Radiation Oncology Milan, Italy IEO European Institute of Oncology IRCCS  Milano Italy"},{"name":"Department of Oncology and Hemato\u2010Oncology Universit\u00e0 Degli Studi di Milano  Milano Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8151-3673","authenticated-orcid":false,"given":"Barbara Alicja","family":"Jereczek\u2010Fossa","sequence":"additional","affiliation":[{"name":"Division of Radiation Oncology Milan, Italy IEO European Institute of Oncology IRCCS  Milano Italy"},{"name":"Department of Oncology and Hemato\u2010Oncology Universit\u00e0 Degli Studi di Milano  Milano Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9557-6496","authenticated-orcid":false,"given":"Ernesto","family":"Damiani","sequence":"additional","affiliation":[{"name":"Computer Science Department Universit\u00e0 Degli Studi di Milano  Milano Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","published-online":{"date-parts":[[2026,4,22]]},"reference":[{"key":"e_1_2_10_2_1","unstructured":"Administration U.E.I.2018.\u201cCommercial Buildings Energy Consumption Survey (CBECS).\u201dhttps:\/\/www.eia.gov\/consumption\/commercial\/data\/2018\/index.php?view=microdata."},{"key":"e_1_2_10_3_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jobe.2022.104577"},{"key":"e_1_2_10_4_1","unstructured":"Beliajeva A.2024.\u201cApplication of Change Management Towards Green Business Process Management Within the Banking Sector.\u201dMaster Thesis Vilniaus Universitetas Includes 14 Figures 11 Tables 84 References."},{"key":"e_1_2_10_5_1","first-page":"327","article-title":"Il consumo energetico di treni in esercizio: simulazione, metodologia di analisi ed influenza dello stile di condotta","volume":"4","author":"Bruno F.","year":"2015","journal-title":"Ingegneria Ferroviaria"},{"key":"e_1_2_10_6_1","doi-asserted-by":"publisher","DOI":"10.3390\/en15030806"},{"key":"e_1_2_10_7_1","doi-asserted-by":"publisher","DOI":"10.3390\/su15031920"},{"key":"e_1_2_10_8_1","unstructured":"Deru M. K.Field D.Studer et\u00a0al.2014.\u201cCommercial Reference Building: Hospital. [Data Set].\u201dOpen Energy Data Initiative (OEDI).http:\/\/data.openei.org\/submissions\/157."},{"key":"e_1_2_10_9_1","doi-asserted-by":"publisher","DOI":"10.1007\/s00066\u2010025\u201002419\u20107"},{"key":"e_1_2_10_10_1","unstructured":"Grassi W. D.Testi E.Menchetti et\u00a0al.2009.\u201cValutazione dei consumi nell'edilizia esistente e benchmark mediante codici semplificati: Analisi di edifici ospedalieri. In Italian.\u201dhttps:\/\/www.ricercasistemaelettrico.enea.it\/archivio\u2010documenti.html?task=download.send&id=1608:valutazione\u2010dei\u2010consumi\u2010nelledilizia\u2010esistente\u2010e\u2010benchmark\u2010mediante\u2010codici\u2010semplificati\u2010analisi\u2010di\u2010edifici\u2010ospedalieri&catid=355."},{"key":"e_1_2_10_11_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.enbuild.2016.11.037"},{"key":"e_1_2_10_12_1","doi-asserted-by":"publisher","DOI":"10.1007\/s42421\u2010024\u201000102\u20104"},{"key":"e_1_2_10_13_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.rser.2022.113045"},{"key":"e_1_2_10_14_1","doi-asserted-by":"publisher","DOI":"10.1051\/bioconf\/202414505017"},{"key":"e_1_2_10_15_1","doi-asserted-by":"publisher","DOI":"10.1016\/S1470\u20102045(24)00148\u20107"},{"key":"e_1_2_10_16_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-94-007-5922-0_13"},{"key":"e_1_2_10_17_1","unstructured":"Modin K. andF.Sj\u00f6str\u00f6m.2022.\u201cThe Digital Carbon Footprint Associated to the Way of Working in an Organization.\u201dSupervised by Per Lundqvist."},{"key":"e_1_2_10_18_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.rser.2019.109541"},{"key":"e_1_2_10_19_1","doi-asserted-by":"publisher","DOI":"10.5435\/JAAOSGlobal\u2010D\u201025\u201000195"},{"key":"e_1_2_10_20_1","doi-asserted-by":"publisher","DOI":"10.1001\/jamanetworkopen.2022.53788"},{"key":"e_1_2_10_21_1","unstructured":"Piano Nazionale Integrato per l'Energia e il Clima.2019.\u201cPiano Nazionale Integrato per l'Energia e il Clima. 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