[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-120360-en":3,"doc-seo-120360-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},120360,687197207057,"Sage","https://ap-avatar.wpscdn.com/davatar_29158cc5080c5b710cf443261637dec0",8,"Research & Report","Data Driven Urban Building Energy Modeling with Machine Learning in Satom CH","The study integrates district heating systems into urban planning for sustainable development in moderate to cold climates through a data-driven urban energy modeling framework. The framework connects traditional engineering simulation models with emerging machine learning (ML) approaches to provide accurate, comprehensive insights into urban energy demand patterns. Engineering and ML models are evaluated for generalization across building and urban scales, using LightGBM and Random Forest regression, while Multiple Linear Regression supports simpler scenarios and urban energy planning decisions.","POLITECNICO DI TORINO  \nRepository ISTITUZIONALE  \nData Driven Urban Building Energy Modeling with Machine Learning in Satom CH  \nOriginal  \nData Driven Urban Building Energy Modeling with Machine Learning in Satom CH / Montazeri, Ahad; Kämpf, Jérôme H. ; Mutani, Guglielmina. -ELETTRONICO. - (2023), pp. 000113-000118. ( 2023 IEEE 6th International Conference and Workshop Óbuda on Electrical and Power Engineering (CANDO-EPE) Budapest October 19-20, 2023)  \n[10 . 1109/CANDO-EPE60507 .2023. 10417986] .  \nAvailability:  \nThis version is available at: 11583/2985812 since: 2024-02-08T22:32:22Z  \nPublisher: IEEE  \nPublished  \nDOI:10.1109/CANDO-EPE60507.2023.10417986  \nTerms of use:  \nThis article is made available under terms and conditions as specified in the corresponding bibliographic description in the repository  \nPublisher copyright  \nIEEE postprint/Author's Accepted Manuscript  \n©2023 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collecting works, for resale or lists, or reuse of any copyrighted component of this work in other works.  \n(Article begins on next page)  \n21 February 2026  \nData driven Urban Building Energy Modeling with Machine Learning in Satom CH  \nAhad Montazeri  \nDepartment of Energy, DENERG Politecnico di Torino Torino, Italy  \n[ahad.montazeri69@outlook.com](ahad.montazeri69@outlook.com)  \nJérôme H. Kämpf Energy Informatics Group Idiap Research Institute Martigny, Switzerland [jerome.kaempf@idiap.ch](jerome.kaempf@idiap.ch)  \nGuglielmina Mutani Department of Energy, DENERG Politecnico di Torino Torino, Italy guglielmina.mutani@polito.it  \nAbstract—This article delves into the integration of district heating systems into urban planning for sustainable development in regions with moderate to cold climates. The study introduces the Data-driven Urban Energy modeling framework, which aims to bridge the gap between conventional engineering-based energy simulation models and emerging data-driven machine learning (ML) models. By doing so, it provides accurate and comprehensive insights into urban energy demand (ED) patterns.  \nThe methodology involves evaluating engineering and ML model's generalization power, revealing its ability to predict energy demand accurately at both building and urban scales. Machine learning algorithms, including LightGBM (LGBM) and Random Forest (RF) regression, are employed to fine-tune the energy-use model for future energy demand predictions. The results demonstrate the model's exceptional accuracy and suitability for diverse urban scenarios. Incorporating a more straightforward approach like Multiple Linear Regression (MLR) into the methodology also highlights its capability to predict energy demand in less complex research scenarios and offer valuable insights for effective urban energy planning.  \nOverall, this article emphasizes the significance of datadriven approaches and machine learning techniques in optimizing energy demand, promoting sustainable urban development, and guiding informed decision-making for energy-efficient cities. The findings have implications for urban planners, policymakers, and energy analysts seeking to enhance energy efficiency and contribute to a greener and more sustainable future for urban communities.  \nKeywords— Urban building energy modeling, Data-driven models, Machine learning, Place-based approach, Geographic Information System GIS  \nI. INTRODUCTION  \nIn numerous countries characterized by moderate to cold climates, the substantial portion of overall energy consumption stems from the need for space heating and domestic hot water in buildings [1]. While district heating and cooling systems have been in existence for some time, they are continually evolving worldwide [2]. Presently, integrating these systems into urban planning has become a crucial componen","cbCainWyZJ3dW0U2","https://ap.wps.com/l/cbCainWyZJ3dW0U2","pdf",492808,1,7,"English","en",105,"# Introduction\n## Data-driven urban energy modeling framework\n## Machine learning methods and evaluation approach\n## Implications for urban energy planning","[{\"question\":\"What problem does the data-driven urban energy modeling framework address?\",\"answer\":\"It bridges the gap between conventional engineering-based energy simulation models and data-driven machine learning models to better capture urban energy demand patterns in sustainable urban planning.\"},{\"question\":\"Which machine learning methods are used to predict energy demand?\",\"answer\":\"The study employs LightGBM (LGBM) and Random Forest (RF) regression to fine-tune energy-use models for future energy demand predictions.\"},{\"question\":\"How does the paper evaluate model performance at different scales?\",\"answer\":\"It compares engineering and ML models in terms of generalization power, demonstrating accurate energy demand prediction at both building and urban scales.\"}]","Data Driven Urban Building Energy Modeling with Machine Learning in Satom CH | 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