{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,20]],"date-time":"2026-07-20T14:15:04Z","timestamp":1784556904630,"version":"3.55.0"},"reference-count":59,"publisher":"Association for Computing Machinery (ACM)","issue":"3","license":[{"start":{"date-parts":[[2024,7,29]],"date-time":"2024-07-29T00:00:00Z","timestamp":1722211200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"name":"NSF","award":["EPCN-1936131, CPS-1545037 and CNS-2008878"],"award-info":[{"award-number":["EPCN-1936131, CPS-1545037 and CNS-2008878"]}]},{"name":"NSF CAREER","award":["CPS-1943035"],"award-info":[{"award-number":["CPS-1943035"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Cyber-Phys. Syst."],"published-print":{"date-parts":[[2024,7,31]]},"abstract":"<jats:p>\n            Extreme 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:\n            <jats:italic>personalized preferences<\/jats:italic>\n            of power conservation,\n            <jats:italic>models of realistic user behavior<\/jats:italic>\n            , and\n            <jats:italic>realistic home-level power dynamics<\/jats:italic>\n            . 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\n            <jats:italic>bids<\/jats:italic>\n            , i.e.,\n            <jats:italic>personalized preferences<\/jats:italic>\n            of thermostat temperature adjustments, along with corresponding financial compensations. We employ\n            <jats:italic>models of realistic user behavior<\/jats:italic>\n            by means of online surveys to gather user bids and evaluate user interaction with such system.\n            <jats:italic>Realistic home-level power dynamics<\/jats:italic>\n            are implemented by our machine learning-based Power Saving Predictions (PSP) algorithm, calculating the individual power savings in each user\u2019s home resulting from such bids. A machine learning-based PSPs algorithm is executed by the users\u2019 Smart Energy Management System (SEMS). PSP translates temperature adjustments into the corresponding power savings. Then, the SEMS sends bids back to the LSE, which selects the auction winners through an optimization problem called POwer Conservation Optimization (POCO). We prove that POCO is NP-hard, and thus provide two approaches to solve this problem. One approach is an optimal pseudo-polynomial algorithm called DYnamic programming Power Saving (DYPS), while the second is a heuristic polynomial time algorithm called Greedy Ranking AllocatioN (GRAN). EnergyPlus, the high-fidelity and gold-standard energy simulator funded by the U.S. Department of Energy, was used to validate our experiments, as well as to collect data to train PSP. We further evaluate the results of the auctions across several scenarios, showing that, as expected, DYPS finds the optimal solution, while GRAN outperforms recent state-of-the-art approaches.\n          <\/jats:p>","DOI":"10.1145\/3656348","type":"journal-article","created":{"date-parts":[[2024,4,5]],"date-time":"2024-04-05T12:51:48Z","timestamp":1712321508000},"page":"1-26","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":5,"title":["A Human-Centered Power Conservation Framework Based on Reverse Auction Theory and Machine Learning"],"prefix":"10.1145","volume":"8","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3286-8598","authenticated-orcid":false,"given":"Enrico","family":"Casella","sequence":"first","affiliation":[{"name":"University of Wisconsin-Madison, Madison, Wisconsin, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2357-3429","authenticated-orcid":false,"given":"Simone","family":"Silvestri","sequence":"additional","affiliation":[{"name":"University of Kentucky, Lexington, Kentucky, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8999-0810","authenticated-orcid":false,"given":"D. A.","family":"Baker","sequence":"additional","affiliation":[{"name":"Missouri University of Science &amp; Technology, Rolla, Missouri, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9471-0868","authenticated-orcid":false,"given":"Sajal K.","family":"Das","sequence":"additional","affiliation":[{"name":"Missouri University of Science &amp; Technology, Rolla, Missouri, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2024,7,29]]},"reference":[{"key":"e_1_3_2_2_2","volume-title":"World Energy Outlook","author":"International Energy Agency","year":"2009","unstructured":"International Energy Agency. 2009. World Energy Outlook. 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