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This paper presents a machine-learning method to quantify the technical potential of shallow GSHPs for Switzerland under limited data and computational resources. A training dataset is built from meteorological and geological variables, correlations and feature importance are analyzed, and multiple feature sets and ML models are compared. Results show Random Forest as best, with strong R2 performance across annual energy potential, heat extraction rate, and borehole number potential.",{"@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/using-machine-learning-to-estimate-the-technical-potential-of-shallow-ground-source-heat-pumps-with-thermal-interference/290763/",{"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/using-machine-learning-to-estimate-the-technical-potential-of-shallow-ground-source-heat-pumps-with-thermal-interference/290763.png","ImageObject",300,407,{"name":92,"@type":93},"Rowan","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-11-07","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},"Why must thermal interaction be considered when estimating GSHP technical potential?","Question",{"text":113,"@type":114},"Because dense vertical borehole heat exchanger (BHE) installations can cause excessive long-term cooling of the shallow subsurface due to thermal interactions between boreholes.","Answer",{"name":116,"@type":111,"acceptedAnswer":117},"What approach does the paper use to estimate the technical potential of shallow GSHPs?",{"text":118,"@type":114},"It develops a machine-learning method combined with geospatial preprocessing to estimate technical potential at national scale for Switzerland.",{"name":120,"@type":111,"acceptedAnswer":121},"Which machine-learning model performed best, and what were the reported accuracy metrics?",{"text":122,"@type":114},"Random Forest trained on the full dataset achieved the best performance, with reported R2 scores of 0.95 for annual energy potential, 0.86 for heat extraction rate, and 0.82 for potential borehole number per system.","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},290763,1789642526,{"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":34,"language":140,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":141,"faqs":142,"seo_title":143,"seo_description":67,"update_tm":130,"read_time":144,"preview_page_count":4},1099514067415,"https://ap-avatar.wpscdn.com/avatar/100002539d78ffe74a7?x-image-process=image/resize,m_fixed,w_180,h_180&k=1779092875211072502","PAPER • OPEN ACCESS  \nUsing Machine Learning to estimate the technical potential of shallow ground-source heat pumps with thermal interference  \nTo cite this article: Alina Walch et al 2021 J. Phys.: Conf. Ser. 2042 012010  \nView the article online for updates and enhancements.  \nYou may also like  \n-Low-carbon Technology Application in Wuhan Based on Ground Source Heat Pump System  \nHongmin Li and Zuxu Zou  \n-Integration of a heat exchanger on the supply air with the ground-source heat pump in a passive house – case study  \nB Radomski, L Drojetzki and T Mróz  \n-Collocated geothermal resources of the South of Western Siberia (Russia) and prospects of their use  \nA A Shevchenko, O V Skrobot, E G Bordet al.  \nThis content was downloaded from IP address [128.179.251.116](128.179.251.116) on 05/01/2022 at 08:21  \nJournal of Physics: Conference Series 2042 (2021) 012010 doi:10.1088/1742-6596/2042/1/012010  \nUsing Machine Learning to estimate the technical potential of shallow ground-source heat pumps with thermal interference  \nAlina Walch1 , Roberto Castello1 , Nahid Mohajeri2 , Agust Gudmundsson3 , JeanLouis Scartezzini1  \n1 Solar Energy and Building Physics Laboratory, EPFL, Lausanne, Switzerland  \n2 Institute for Environmental Design and Engineering, UCL, London, UK  \n3 Department of Earth Sciences, Royal Holloway University of London, Egham, UK  \nAbstract. The increasing use of ground-source heat pumps (GSHPs) for heating and cooling of buildings raises questions regarding the technical potential of GSHPs and their impact on the temperature in the shallow subsurface . In this paper , we develop a method using Machine Learning to estimate the technical potential of shallow GSHPs , which enables such an estimation for Switzerland with limited data and computational resources. A training dataset is constructed based on meteorological and geological data across Switzerland. We analyse correlations and the importance of each of the input data for estimating the GSHP potential and compare different input feature sets and Machine Learning models . The Random Forest algorithm, trained on the full dataset, provides the best performance to estimate the GSHP potential. The resulting model yields an R2 score of 0.95 for the annual energy potential , 0.86 for the heat extraction rate , and 0.82 for the potential number of boreholes per GSHP system .  \n1. Introduction  \nThe extraction of shallow geothermal energy using ground-source heat pumps (GSHPs) of depths up to 400 m is growing worldwide. Switzerland is the country with the world’s highest density of shallow geothermal installations, most of which are GSHPs with vertical borehole heat exchangers (BHEs) [1] . A dense installation of BHEs may, however, lead to an excessive long-term cooling of the subsurface due to thermal interactions between boreholes. Thus, thermal interaction must be considered when quantifying the technical potential of BHEs, that is, the long-term maximum energy which can be extracted annually using GSHPs [2,3] . While analytical methods exist to quantify this potential, they are computationally intensive and require the availability of high-resolution data of the thermal properties of the shallow ground. These limitations may hinder the use of analytical models to quantify the technical potential of shallow GSHPs at national scale. By contrast, Machine Learning (ML) can handle missing data in analytical models and, once trained, is computationally highly efficient [4] .  \nHere we use ML in combination with geospatial tools for data pre-processing to estimate the technical potential of BHEs. More specifically, we optimise the set of features and the ML design to (i) use available data from the entire Switzerland and (ii) estimate the technical GSHP potential with high accuracy. For this, we first design the input dataset for the ML model, using an existing regionalscale study of shallow GSHP potential based on analytical modelling [2] as input. Second, we analyse ","cbCaid5vNE0QnKAI","https://ap.wps.com/l/cbCaid5vNE0QnKAI","pdf",1908644,"English","# Abstract\n# Introduction\n# Methods and data\n## Dataset description","[{\"question\":\"Why must thermal interaction be considered when estimating GSHP technical potential?\",\"answer\":\"Because dense vertical borehole heat exchanger (BHE) installations can cause excessive long-term cooling of the shallow subsurface due to thermal interactions between boreholes.\"},{\"question\":\"What approach does the paper use to estimate the technical potential of shallow GSHPs?\",\"answer\":\"It develops a machine-learning method combined with geospatial preprocessing to estimate technical potential at national scale for Switzerland.\"},{\"question\":\"Which machine-learning model performed best, and what were the reported accuracy metrics?\",\"answer\":\"Random Forest trained on the full dataset achieved the best performance, with reported R2 scores of 0.95 for annual energy potential, 0.86 for heat extraction rate, and 0.82 for potential borehole number per system.\"}]","Using Machine Learning to estimate the technical potential of shallow ground-source heat pumps with thermal interference | PDF",18]