[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-127948-en":3,"doc-seo-127948-105":31,"detail-sidebar-cat-0-en-105":92},{"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":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":28,"seo_description":14,"update_tm":29,"read_time":30},127948,687207024643,"Oliver","https://ap-avatar.wpscdn.com/davatar_3d24733baf745e90a7e4bdd5f77d97b2",8,"Research & Report","A Human-Centered Power Conservation Framework based on Reverse Auction Theory and Machine Learning","Extreme temperatures from heat waves and winter storms strain HVAC systems and stress power grids, raising blackout risk and costly peak-load impacts. Prior demand-response methods fall short by ignoring human behavioral complexity and realistic home-level power dynamics, leading to poor long-term engagement and limited real-world applicability. This work introduces an auction-theory HVAC framework combining personalized conservation preferences, realistic user behavior models, and machine-learning-based power saving predictions integrated into an optimization process.","Missouri University of Science and Technology  \nScholars' Mine  \n\n| Computer Science Faculty Research & Creative Works | Computer Science |\n| --- | --- |\n| 29 Jul 2024\u003Cbr>A Human-Centered Power Conservation Framework based on Reverse Auction Theory and Machine Learning\u003Cbr>Enrico Casella\u003Cbr>Simone Silvestri\u003Cbr>Missouri University of Science and Technology, [silvestris@mst.edu](silvestris@mst.edu)\u003Cbr>Denise A. Baker\u003Cbr>Missouri University of Science and Technology, [bakerden@mst.edu](bakerden@mst.edu)\u003Cbr>Sajal K. Das\u003Cbr>Missouri University of Science and Technology, [sdas@mst.edu](sdas@mst.edu)\u003Cbr>Follow this and additional works at: [https://scholarsmine.mst.edu/comsci_facwork](https://scholarsmine.mst.edu/comsci_facwork)\u003Cbr> Part of the Computer Sciences Commons, and the Psychology Commons |  |\n\nRecommended Citation  \nE. Casella et al., \"A Human-Centered Power Conservation Framework based on Reverse Auction Theory and Machine Learning,\" ACM Transactions on Cyber-Physical Systems, vol. 8, no. 3, Association for Computing Machinery (ACM), Jul 2024.  \nThe definitive version is available at [https://doi.org/10.1145/3656348](https://doi.org/10.1145/3656348)  \nThis Article-Journal is brought to you for free and open access by Scholars' Mine. It has been accepted for inclusion in Computer Science Faculty Research & Creative Works by an authorized administrator of Scholars'Mine. This work is protected by U. S. Copyright Law. Unauthorized use including reproduction for redistribution requires the permission of the copyright holder. For more information, please contact [scholarsmine@mst.edu](scholarsmine@mst.edu).  \nA Human-Centered Power Conservation Framework Based on Reverse Auction Theory and Machine Learning  \nENRICO CASELLA, University of Wisconsin-Madison, Madison, Wisconsin, USA SIMONE SILVESTRI, University of Kentucky, Lexington, Kentucky, USA  \nD. A. BAKER and SAJAL K. DAS, Missouri University of Science & Technology, Rolla, Missouri, USA  \nExtreme outside temperatures resulting from heat waves, winter storms, and similar weather-related events trigger the Heating Ventilation and Air Conditioning (HVAC) systems, resulting in challenging, and potentially catastrophic, peak loads. As a consequence, such extreme outside temperatures put a strain on power grids and may thus lead to blackouts. To avoid the financial and personal repercussions of peak loads, demand response and power conservation represent promising solutions. Despite numerous efforts, it has been shown that the current state-of-the-art fails to consider (1) the complexity of human behavior when interacting with power conservation systems and (2) realistic home-level power dynamics. As a consequence, this leads to approaches that are (1) ineffective due to poor long-term user engagement and (2) too abstract to be used in real-world settings. In this article, we propose an auction theory-based power conservation framework for HVAC designed to address such individual human component through a three-fold approach: personalized preferences of power conservation, models of realistic user behavior, and realistic home-level power dynamics. In our framework, the System Operator sends Load Serving Entities (LSEs) the required power saving to tackle peak loads at the residential distribution feeder. Each LSE then prompts its users to provide bids, i.e., personalized preferences of thermostat temperature adjustments, along with corresponding financial compensations. We employ models of realistic user behavior by means of online surveys to gather user bids and evaluate user interaction with such system. Realistic home-level power dynamics are implemented by our machine learning-based Power Saving Predictions (PSP) algorithm, calculating the individual power savings in each user’s home resulting from such bids. A machine learning-based PSPs algorithm is executed by the users’ Smart Energy Management System (SEMS) . PSP translates temperature adjustments into the corresponding po","cbCaipMb1Ousz0BX","https://ap.wps.com/l/cbCaipMb1Ousz0BX","pdf",1338447,3,1,27,"English","en",105,"# Abstract\n## Motivation and Problem\n## Proposed Human-Centered Auction Framework\n## Power Saving Prediction and Optimization\n## Experimental Validation and Results","[{\"question\":\"Why do extreme outside temperatures make HVAC peak loads risky?\",\"answer\":\"Heat waves and winter storms trigger HVAC operation and generate challenging peak loads that can strain power grids and increase blackout risk.\"},{\"question\":\"What limitations are identified in current demand-response power conservation approaches?\",\"answer\":\"They do not capture human behavior complexity interacting with conservation systems and do not model realistic home-level power dynamics, causing ineffective long-term engagement and approaches that are too abstract for real settings.\"},{\"question\":\"How does the proposed framework allocate power conservation to residential users?\",\"answer\":\"Load Serving Entities request user bids based on personalized thermostat preferences and compensations; machine-learning predicts power savings from temperature adjustments, and an optimization problem (POCO) selects auction winners.\"}]","A Human-Centered Power Conservation Framework based on Reverse Auction Theory and Machine Learning | PDF",1785943174,68,{"code":4,"msg":32,"data":33},"ok",{"site_id":25,"language":24,"slug":34,"title":13,"keywords":35,"description":14,"schema_data":36,"social_meta":87,"head_meta":89,"extra_data":91,"updated_unix":29},"a-human-centered-power-conservation-framework-based-on-reverse-auction-theory-and-machine-learning","",{"@graph":37,"@context":86},[38,54,69],{"@type":39,"itemListElement":40},"BreadcrumbList",[41,45,49,51],{"item":42,"name":43,"@type":44,"position":21},"https://docshare.wps.com","Home","ListItem",{"item":46,"name":47,"@type":44,"position":48},"https://docshare.wps.com/document/","Document",2,{"item":50,"name":12,"@type":44,"position":20},"https://docshare.wps.com/document/research-report/",{"item":52,"name":13,"@type":44,"position":53},"https://docshare.wps.com/document/a-human-centered-power-conservation-framework-based-on-reverse-auction-theory-and-machine-learning/127948/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":24,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":42,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-25","2026-08-05",true,{"@type":66,"interactionType":67,"userInteractionCount":20},"InteractionCounter",{"@type":68},"ViewAction",{"@type":70,"mainEntity":71},"FAQPage",[72,78,82],{"name":73,"@type":74,"acceptedAnswer":75},"Why do extreme outside temperatures make HVAC peak loads risky?","Question",{"text":76,"@type":77},"Heat waves and winter storms trigger HVAC operation and generate challenging peak loads that can strain power grids and increase blackout risk.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"What limitations are identified in current demand-response power conservation approaches?",{"text":81,"@type":77},"They do not capture human behavior complexity interacting with conservation systems and do not model realistic home-level power dynamics, causing ineffective long-term engagement and approaches that are too abstract for real settings.",{"name":83,"@type":74,"acceptedAnswer":84},"How does the proposed framework allocate power conservation to residential users?",{"text":85,"@type":77},"Load Serving Entities request user bids based on personalized thermostat preferences and compensations; 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