[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"detail-sidebar-cat-0-en-105":3,"doc-seo-290797-105":59,"doc-detail-290797-en":131},{"code":4,"msg":5,"data":6},0,"success",[7,13,18,23,28,33,38,43,48,51,55],{"id":8,"doc_module":4,"doc_module_name":9,"category_name":10,"show_sort_weight":11,"slug":12},1,"Document","Story & Novel",90,"story-novel",{"id":14,"doc_module":4,"doc_module_name":9,"category_name":15,"show_sort_weight":16,"slug":17},2,"Literature",80,"literature",{"id":19,"doc_module":4,"doc_module_name":9,"category_name":20,"show_sort_weight":21,"slug":22},4,"Exam",70,"exam",{"id":24,"doc_module":4,"doc_module_name":9,"category_name":25,"show_sort_weight":26,"slug":27},5,"Comic",60,"comic",{"id":29,"doc_module":4,"doc_module_name":9,"category_name":30,"show_sort_weight":31,"slug":32},6,"Technology",50,"technology",{"id":34,"doc_module":4,"doc_module_name":9,"category_name":35,"show_sort_weight":36,"slug":37},7,"Healthcare",40,"healthcare",{"id":39,"doc_module":4,"doc_module_name":9,"category_name":40,"show_sort_weight":41,"slug":42},8,"Research & Report",30,"research-report",{"id":44,"doc_module":4,"doc_module_name":9,"category_name":45,"show_sort_weight":46,"slug":47},9,"Religion & Spirituality",20,"religion-spirituality",{"id":46,"doc_module":4,"doc_module_name":9,"category_name":49,"show_sort_weight":46,"slug":50},"World Cup","world-cup",{"id":52,"doc_module":4,"doc_module_name":9,"category_name":53,"show_sort_weight":52,"slug":54},10,"Lifestyle","lifestyle",{"id":56,"doc_module":4,"doc_module_name":9,"category_name":57,"show_sort_weight":24,"slug":58},19,"General","general",{"code":4,"msg":60,"data":61},"ok",{"site_id":62,"language":63,"slug":64,"title":65,"keywords":66,"description":67,"schema_data":68,"social_meta":124,"head_meta":126,"extra_data":128,"updated_unix":130},105,"en","evaluation-of-smart-building-integration-into-a-smart-city-applying-machine-learning-techniques","Evaluation of Smart Building Integration into a Smart City - Applying Machine Learning Techniques","","Smart buildings’ role is crucial for advancing smart cities’ performance in achieving environmental sustainability, resiliency, and efficiency. Integration barriers persist due to misalignments across technology, infrastructure, and operations, amplified by weak assessment frameworks and classification systems. This study proposes a machine learning model that predicts smart building integration levels and evaluates their impact on smart city performance using data from 147 buildings across 13 regions. Six algorithms were trained; SVR achieved R-squared 0.81, RMSE 0.33, and MAE 0.27. Case studies show differing marginal benefits across building types.",{"@graph":69,"@context":123},[70,84,106],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":40,"@type":76,"position":81},"https://docshare.wps.com/document/research-report/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/evaluation-of-smart-building-integration-into-a-smart-city-applying-machine-learning-techniques/290797/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/evaluation-of-smart-building-integration-into-a-smart-city-applying-machine-learning-techniques/290797.png","ImageObject",300,407,{"name":92,"@type":93},"River Wang","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-10-30","2026-09-17",true,{"@type":102,"interactionType":103,"userInteractionCount":105},"InteractionCounter",{"@type":104},"ViewAction",12,{"@type":107,"mainEntity":108},"FAQPage",[109,115,119],{"name":110,"@type":111,"acceptedAnswer":112},"What problem does the study address regarding smart buildings and smart cities?","Question",{"text":113,"@type":114},"It addresses integration barriers caused by misalignments among technology, infrastructure, and operations, which are worsened by inadequate assessment frameworks and inconsistent metrics.","Answer",{"name":116,"@type":111,"acceptedAnswer":117},"How is the machine learning model built and evaluated?",{"text":118,"@type":114},"The model is trained on data from 147 smart buildings across 13 regions using six optimized algorithms, with feature engineering and permutation importance analysis.",{"name":120,"@type":111,"acceptedAnswer":121},"Which algorithm performs best and what results does it achieve?",{"text":122,"@type":114},"The SVR-trained model outperforms others, reaching R-squared 0.81, RMSE 0.33, and MAE 0.27 for integration prediction.","https://schema.org",{"og:url":83,"og:type":125,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":127,"canonical":83},"index,follow",{"doc_id":129,"site_id":62},290797,1789642580,{"code":4,"msg":5,"data":132},{"doc_id":129,"user_id":133,"nickname":92,"user_avatar":134,"doc_module":4,"category_id":39,"category_name":40,"doc_title":65,"doc_description":67,"doc_content":135,"file_id":136,"file_url":137,"file_type":138,"file_size":139,"view_count":105,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":140,"language":141,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":142,"faqs":143,"seo_title":144,"seo_description":67,"update_tm":130,"read_time":145,"preview_page_count":4},1099514067438,"https://ap-avatar.wpscdn.com/avatar/100002539ee87300030?x-image-process=image/resize,m_fixed,w_180,h_180&k=1780474512215547542","Article  \nEvaluation of Smart Building Integration into a Smart City by Applying Machine Learning Techniques  \nMustafa Muthanna Najm Shahrabani * and Rasa Apanaviciene *  \nAcademic Editor: Adrian Pitts  \nReceived: 13 May 2025  \nRevised: 2 June 2025  \nAccepted: 4 June 2025  \nPublished: 12 June 2025  \nCitation: Shahrabani, M.M.N.; Apanaviciene, R. Evaluation of Smart Building Integration into a Smart City by Applying Machine Learning Techniques. Buildings 2025, 15, 2031 .  \n[https://doi.org/10.3390/](https://doi.org/10.3390/)[ ](https://doi.org/10.3390/)buildings15122031  \nCopyright: © 2025 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([https://creativecommons.org/](https://creativecommons.org/)[ ](https://creativecommons.org/)[licenses/by/4.0/](licenses/by/4.0/)) .  \nFaculty of Civil Engineering and Architecture, Kaunas University of Technology, Studentu˛ Str. 48, LT-51367 Kaunas, Lithuania  \n* [Correspondence: mustafa.shahrabani@ktu.lt](Correspondence: mustafa.shahrabani@ktu.lt) (M.M.N.S.); [rasa.apanaviciene@ktu.lt](rasa.apanaviciene@ktu.lt) (R.A.)  \nAbstract: Smart buildings’ role is crucial for advancing smart cities’ performance in achieving environmental sustainability, resiliency, and efficiency. The integration barriers continue due to technology, infrastructure, and operations misalignments and are escalated due to inadequate assessment frameworks and classification systems. The existing literature on assessment methodologies reveals diverging evaluation frameworks for smart buildings and smart cities, non-uniform metrics and taxonomies that hinder scalability, and the low use of machine learning in predictive integration modelling. To fill these gaps, this paper introduces a novel machine learning model to predict smart building integration into smart city levels and assess their impact on smart city performance by leveraging data from 147 smart buildings in 13 regions. Six optimised machine learning algorithms (K-Nearest Neighbours (KNNs), Support Vector Regression (SVR), Random Forest, Adaptive Boosting (AdaBoost), Decision Tree (DT), and Extra Tree (ET)) were employed to train the model and perform feature engineering and permutation importance analysis. The SVR-trained model substantially outperformed other models, achieving an R-squared of 0.81, Root Mean Square Error (RMSE) of 0.33 and Mean Absolute Error (MAE) of 0.27, enabling precise integration prediction. Case studies revealed that low-integration buildings gain significant benefits from progressive target upgrades, whilst those buildings that have already implemented some integrated systems tend to experience diminishing marginal benefits with further, potentially disruptive upgrades. The conclusion of this study states that by utilising the developed machine learning model, owners and policymakers are capable of significantly improving the integration of smart buildings to build better, more sustainable, and resilient urban environments.  \nKeywords: smart building; smart city; integration level; machine learning; efficiency; resilience; sustainability  \n1. Introduction  \nSmart cities harness cutting-edge information and communication technologies (ICTs) to maintain healthy and prosperous communities, embrace socio-economic efficiency, and instigate the future of urban modelling. Smart cities use different technology solutions to better manage resources, enhance urban services, and create engaged and responsive urban communities [1–3], as well as to improve transportation systems, enhance energy efficiency, promote sustainable resource management, and facilitate improved governance and citizen engagement [1,4,5] . The concept of smart cities is multifaceted: smart infrastructures refer to smart dimensions such as energy, mobility, living, building, water management, environment, smart governance, and economy [1,3,6] . ","cbCailGZg2g9b6UM","https://ap.wps.com/l/cbCailGZg2g9b6UM","pdf",4398426,39,"English","# Introduction\n## Smart cities and ICT foundations\n## Smart buildings as a core element\n## Integration barriers and misalignment impacts\n# Machine learning model and evaluation","[{\"question\":\"What problem does the study address regarding smart buildings and smart cities?\",\"answer\":\"It addresses integration barriers caused by misalignments among technology, infrastructure, and operations, which are worsened by inadequate assessment frameworks and inconsistent metrics.\"},{\"question\":\"How is the machine learning model built and evaluated?\",\"answer\":\"The model is trained on data from 147 smart buildings across 13 regions using six optimized algorithms, with feature engineering and permutation importance analysis.\"},{\"question\":\"Which algorithm performs best and what results does it achieve?\",\"answer\":\"The SVR-trained model outperforms others, reaching R-squared 0.81, RMSE 0.33, and MAE 0.27 for integration prediction.\"}]","Evaluation of Smart Building Integration into a Smart City - Applying Machine Learning Techniques | PDF",98]