{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,19]],"date-time":"2026-06-19T11:54:23Z","timestamp":1781870063684,"version":"3.54.5"},"publisher-location":"New York, NY, USA","reference-count":38,"publisher":"ACM","license":[{"start":{"date-parts":[[2026,6,22]],"date-time":"2026-06-22T00:00:00Z","timestamp":1782086400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/legalcode"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2026,6,22]]},"DOI":"10.1145\/3744256.3812552","type":"proceedings-article","created":{"date-parts":[[2026,6,19]],"date-time":"2026-06-19T11:01:41Z","timestamp":1781866901000},"page":"240-250","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Adaptive Control for Energy Optimization in Data Center"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0009-0007-9314-0051","authenticated-orcid":false,"given":"Rani","family":"Farinda","sequence":"first","affiliation":[{"name":"Equinix, Warsaw, Poland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-8520-9806","authenticated-orcid":false,"given":"Kanstantsin","family":"Palitai","sequence":"additional","affiliation":[{"name":"Equinix, Warsaw, Poland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0004-3004-1695","authenticated-orcid":false,"given":"Krzysztof","family":"Popiolek","sequence":"additional","affiliation":[{"name":"Equinix, Warsaw, Poland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7382-6722","authenticated-orcid":false,"given":"Alessandro","family":"Seganti","sequence":"additional","affiliation":[{"name":"Equinix, Warsaw, Poland"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2026,6,22]]},"reference":[{"key":"e_1_3_3_1_2_2","volume-title":"2016 ASHRAE Handbook\u2014HVAC Systems and Equipment","author":"Engineers American Society of Heating, Refrigerating and Air-Conditioning","year":"2016","unstructured":"American Society of Heating, Refrigerating and Air-Conditioning Engineers. 2016. 2016 ASHRAE Handbook\u2014HVAC Systems and Equipment. ASHRAE, Atlanta, GA, USA. https:\/\/www.ashrae.org\/technical-resources\/ashrae-handbook"},{"key":"e_1_3_3_1_3_2","doi-asserted-by":"publisher","unstructured":"Kiam\u00a0Heong Ang Gregory Chong and Yun Li. 2005. PID Control System Analysis Design and Technology. IEEE Transactions on Control Systems Technology 13 4 (2005) 559\u2013576. 10.1109\/TCST.2005.847331","DOI":"10.1109\/TCST.2005.847331"},{"key":"e_1_3_3_1_4_2","volume-title":"Above the Clouds: A Berkeley View of Cloud Computing","author":"Armbrust Michael","year":"2009","unstructured":"Michael Armbrust, Armando Fox, Rean Griffith, Anthony\u00a0D. Joseph, Randy\u00a0H. Katz, Andrew Konwinski, Gunho Lee, David\u00a0A. Patterson, Ariel Rabkin, Ion Stoica, and Matei Zaharia. 2009. Above the Clouds: A Berkeley View of Cloud Computing. Technical Report UCB\/EECS-2009-28. Electrical Engineering and Computer Sciences, University of California at Berkeley. https:\/\/www2.eecs.berkeley.edu\/Pubs\/TechRpts\/2009\/EECS-2009-28.pdf"},{"key":"e_1_3_3_1_5_2","volume-title":"Advanced PID Control","author":"\u00c5str\u00f6m Karl\u00a0Johan","year":"2006","unstructured":"Karl\u00a0Johan \u00c5str\u00f6m and Tore H\u00e4gglund. 2006. Advanced PID Control. ISA, Research Triangle Park, NC, USA."},{"key":"e_1_3_3_1_6_2","doi-asserted-by":"publisher","unstructured":"Rakesh\u00a0P. Borase D.\u00a0K. Maghade S.\u00a0Y. Sondkar and S.\u00a0N. Pawar. 2021. A Review of PID Control Tuning Methods and Applications. International Journal of Dynamics and Control 9 (2021) 818\u2013827. 10.1007\/s40435-020-00665-4","DOI":"10.1007\/s40435-020-00665-4"},{"key":"e_1_3_3_1_7_2","unstructured":"L\u00e9on Bottou Jonas Peters Joaquin Qui\u00f1onero-Candela Denis\u00a0X. Charles D.\u00a0Max Chickering Elon Portugaly Dipankar Ray Patrice Simard and Ed Snelson. 2013. Counterfactual Reasoning and Learning Systems: The Example of Computational Advertising. Journal of Machine Learning Research 14 65 (2013) 3207\u20133260. arxiv:https:\/\/arXiv.org\/abs\/1209.2355\u00a0[cs.LG] https:\/\/www.jmlr.org\/papers\/v14\/bottou13a.html"},{"key":"e_1_3_3_1_8_2","doi-asserted-by":"publisher","unstructured":"Alfonso Capozzoli and Giulio Primiceri. 2015. Cooling Systems in Data Centers: State of Art and Emerging Technologies. Energy Procedia 83 (2015) 484\u2013493. 10.1016\/j.egypro.2015.12.168","DOI":"10.1016\/j.egypro.2015.12.168"},{"key":"e_1_3_3_1_9_2","doi-asserted-by":"publisher","unstructured":"Mark Collier and Hector Urdiales\u00a0Llorens. 2018. Deep Contextual Multi-Armed Bandits. arxiv:https:\/\/arXiv.org\/abs\/1807.09809\u00a0[cs.LG] 10.48550\/arXiv.1807.09809","DOI":"10.48550\/arXiv.1807.09809"},{"key":"e_1_3_3_1_10_2","doi-asserted-by":"publisher","unstructured":"Miyuru Dayarathna Yonggang Wen and Rui Fan. 2016. Data Center Energy Consumption Modeling: A Survey. IEEE Communications Surveys & Tutorials 18 1 (2016) 732\u2013794. 10.1109\/COMST.2015.2481183","DOI":"10.1109\/COMST.2015.2481183"},{"key":"e_1_3_3_1_11_2","doi-asserted-by":"publisher","unstructured":"Jifei Deng Seppo Sierla Jie Sun and Valeriy Vyatkin. 2022. Reinforcement Learning for Industrial Process Control: A Case Study in Flatness Control in Steel Industry. Computers in Industry 143 (2022) 103748. 10.1016\/j.compind.2022.103748","DOI":"10.1016\/j.compind.2022.103748"},{"key":"e_1_3_3_1_12_2","doi-asserted-by":"publisher","unstructured":"Berkeley\u00a0J. Dietvorst Joseph\u00a0P. Simmons and Cade Massey. 2018. Overcoming Algorithm Aversion: People Will Use Imperfect Algorithms If They Can (Even Slightly) Modify Them. Management Science 64 3 (2018) 1155\u20131170. 10.1287\/mnsc.2016.2643","DOI":"10.1287\/mnsc.2016.2643"},{"key":"e_1_3_3_1_13_2","unstructured":"Efficiency Valuation Organization. 2022. International Performance Measurement and Verification Protocol (IPMVP) Core Concepts 2022. https:\/\/evo-world.org\/en\/products-services-mainmenu-en\/protocols\/ipmvp Accessed: 2026-05-12."},{"key":"e_1_3_3_1_14_2","doi-asserted-by":"publisher","DOI":"10.1109\/ITHERM.2016.7517697"},{"key":"e_1_3_3_1_15_2","unstructured":"Richard Evans and Jim Gao. 2016. DeepMind AI Reduces Google Data Centre Cooling Bill by 40%. https:\/\/deepmind.google\/blog\/deepmind-ai-reduces-google-data-centre-cooling-bill-by-40\/"},{"key":"e_1_3_3_1_16_2","unstructured":"Rani Farinda Kanstantsin Palitai Krzysztof Popio\u0142ek and Alessandro Seganti. 2026. Adaptive Control for Energy Optimization in Data Center \u2013 Supplementary Material. https:\/\/github.com\/kpalitai-equinix\/adaptive-control. Appendices and supplementary materials."},{"key":"e_1_3_3_1_17_2","first-page":"3581","volume-title":"Advances in Neural Information Processing Systems","author":"Gal Yarin","year":"2017","unstructured":"Yarin Gal, Jiri Hron, and Alex Kendall. 2017. Concrete Dropout. In Advances in Neural Information Processing Systems , Vol.\u00a030. 3581\u20133590. arxiv:https:\/\/arXiv.org\/abs\/1705.07832\u00a0[stat.ML] https:\/\/papers.neurips.cc\/paper_files\/paper\/2017\/hash\/84ddfb34126fc3a48ee38d7044e87276-Abstract.html"},{"key":"e_1_3_3_1_18_2","doi-asserted-by":"publisher","DOI":"10.1016\/C2013-0-13565-X"},{"key":"e_1_3_3_1_19_2","unstructured":"Amanda Gasparik Chris Gamble and Jim Gao. 2018. Safety-first AI for Autonomous Data Centre Cooling and Industrial Control. https:\/\/deepmind.google\/blog\/safety-first-ai-for-autonomous-data-centre-cooling-and-industrial-control\/"},{"key":"e_1_3_3_1_20_2","doi-asserted-by":"publisher","DOI":"10.1002\/9781119597537"},{"key":"e_1_3_3_1_21_2","doi-asserted-by":"publisher","unstructured":"Gergely Hajgat\u00f3 Gy\u00f6rgy Pa\u00e1l and B\u00e1lint Gyires-T\u00f3th. 2020. Deep Reinforcement Learning for Real-Time Optimization of Pumps in Water Distribution Systems. Journal of Water Resources Planning and Management 146 11 (2020) 04020079. arxiv:https:\/\/arXiv.org\/abs\/2010.06460\u00a0[cs.LG] 10.1061\/(ASCE)WR.1943-5452.0001287","DOI":"10.1061\/(ASCE)WR.1943-5452.0001287"},{"key":"e_1_3_3_1_22_2","doi-asserted-by":"publisher","unstructured":"Ghezlane Halhoul\u00a0Merabet Mohamed Essaaidi Mohamed Ben\u00a0Haddou Basheer Qolomany Junaid Qadir Muhammad Anan Ala Al-Fuqaha Mohamed\u00a0Riduan Abid and Driss Benhaddou. 2021. Intelligent Building Control Systems for Thermal Comfort and Energy-Efficiency: A Systematic Review of Artificial Intelligence-Assisted Techniques. Renewable and Sustainable Energy Reviews 144 (2021) 110969. 10.1016\/j.rser.2021.110969Citation key retained from the previous bibliography entry.","DOI":"10.1016\/j.rser.2021.110969"},{"key":"e_1_3_3_1_23_2","doi-asserted-by":"crossref","unstructured":"IPMVP Committee. 2001. International Performance Measurement and Verification Protocol: Concepts and Options for Determining Energy and Water Savings Volume I. https:\/\/www.nrel.gov\/docs\/fy02osti\/31505.pdf","DOI":"10.2172\/776003"},{"key":"e_1_3_3_1_24_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICRA.2019.8794127"},{"key":"e_1_3_3_1_25_2","doi-asserted-by":"publisher","DOI":"10.1145\/2487575.2488217"},{"key":"e_1_3_3_1_26_2","doi-asserted-by":"publisher","DOI":"10.1017\/9781108653985"},{"key":"e_1_3_3_1_27_2","doi-asserted-by":"publisher","unstructured":"Rui Kong Hainan Zhang Mingsheng Tang Huiming Zou Changqing Tian and Tao Ding. 2024. Enhancing Data Center Cooling Efficiency and Ability: A Comprehensive Review of Direct Liquid Cooling Technologies. Energy 308 (2024) 132846. 10.1016\/j.energy.2024.132846","DOI":"10.1016\/j.energy.2024.132846"},{"key":"e_1_3_3_1_28_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-24853-0"},{"key":"e_1_3_3_1_29_2","first-page":"3818","volume-title":"Advances in Neural Information Processing Systems","author":"Lazic Nevena","year":"2018","unstructured":"Nevena Lazic, Craig Boutilier, Tyler Lu, Eehern Wong, Binz Roy, M.\u00a0K. Ryu, and Greg Imwalle. 2018. Data Center Cooling Using Model-Predictive Control. In Advances in Neural Information Processing Systems , Vol.\u00a031. Curran Associates Inc., Red Hook, NY, USA, 3818\u20133827. https:\/\/proceedings.neurips.cc\/paper\/2018\/hash\/059fdcd96baeb75112f09fa1dcc740cc-Abstract.html"},{"key":"e_1_3_3_1_30_2","doi-asserted-by":"publisher","unstructured":"Dasheng Lee and Chienchieh Lin. 2024. Universal Artificial Intelligence Workflow for Factory Energy Saving: Ten Case Studies. Journal of Cleaner Production 468 (2024) 143049. 10.1016\/j.jclepro.2024.143049","DOI":"10.1016\/j.jclepro.2024.143049"},{"key":"e_1_3_3_1_31_2","doi-asserted-by":"publisher","unstructured":"Yuanlong Li Yonggang Wen Kyle Guan and Dacheng Tao. 2020. Transforming Cooling Optimization for Green Data Center via Deep Reinforcement Learning. IEEE Transactions on Cybernetics 50 5 (2020) 2002\u20132013. arxiv:https:\/\/arXiv.org\/abs\/1709.05077\u00a0[cs.LG] 10.1109\/TCYB.2019.2927410","DOI":"10.1109\/TCYB.2019.2927410"},{"key":"e_1_3_3_1_32_2","doi-asserted-by":"publisher","unstructured":"Jerry Luo Cosmin Paduraru Octavian Voicu Yuri Chervonyi Scott Munns Jerry Li Crystal Qian Praneet Dutta Jared\u00a0Quincy Davis Ningjia Wu Xingwei Yang Chu-Ming Chang Ted Li Rob Rose Mingyan Fan Hootan Nakhost Tinglin Liu Brian Kirkman Frank Altamura Lee Cline Patrick Tonker Joel Gouker Dave Uden Warren\u00a0Buddy Bryan Jason Law Deeni Fatiha Neil Satra Juliet Rothenberg Mandeep Waraich Molly Carlin Satish Tallapaka Sims Witherspoon David Parish Peter Dolan Chenyu Zhao and Daniel\u00a0J. Mankowitz. 2022. Controlling Commercial Cooling Systems Using Reinforcement Learning. arxiv:https:\/\/arXiv.org\/abs\/2211.07357\u00a0[cs.LG] 10.48550\/arXiv.2211.07357","DOI":"10.48550\/arXiv.2211.07357"},{"key":"e_1_3_3_1_33_2","volume-title":"Modern Control Engineering","author":"Ogata Katsuhiko","year":"2009","unstructured":"Katsuhiko Ogata. 2009. Modern Control Engineering. Prentice Hall, Upper Saddle River, NJ, USA."},{"key":"e_1_3_3_1_34_2","volume-title":"Calculating Total Cooling Requirements for Data Centers","author":"Rasmussen Neil","year":"2017","unstructured":"Neil Rasmussen. 2017. Calculating Total Cooling Requirements for Data Centers. White Paper 25 Rev. 4. Schneider Electric Data Center Science Center. https:\/\/www.se.com\/us\/en\/download\/document\/SPD_NRAN-5TE6HE_EN\/"},{"key":"e_1_3_3_1_35_2","doi-asserted-by":"publisher","unstructured":"Aitor Saenz-Aguirre Ekaitz Zulueta Unai Fernandez-Gamiz Javier Lozano and Jose\u00a0Manuel Lopez-Guede. 2019. Artificial Neural Network Based Reinforcement Learning for Wind Turbine Yaw Control. Energies 12 3 (2019) 436. 10.3390\/en12030436","DOI":"10.3390\/en12030436"},{"key":"e_1_3_3_1_36_2","unstructured":"ScienceDirect. 2020. Data Center. https:\/\/www.sciencedirect.com\/topics\/computer-science\/data-center Accessed: 2026-05-12."},{"key":"e_1_3_3_1_37_2","doi-asserted-by":"publisher","unstructured":"Tom Silver Kelsey Allen Josh Tenenbaum and Leslie Kaelbling. 2018. Residual Policy Learning. arxiv:https:\/\/arXiv.org\/abs\/1812.06298\u00a0[cs.LG] 10.48550\/arXiv.1812.06298","DOI":"10.48550\/arXiv.1812.06298"},{"key":"e_1_3_3_1_38_2","doi-asserted-by":"publisher","unstructured":"Sahil Verma John Dickerson and Keegan Hines. 2021. Counterfactual Explanations for Machine Learning: Challenges Revisited. arxiv:https:\/\/arXiv.org\/abs\/2106.07756\u00a0[cs.LG] 10.48550\/arXiv.2106.07756","DOI":"10.48550\/arXiv.2106.07756"},{"key":"e_1_3_3_1_39_2","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.2501.15085"}],"event":{"name":"BuildSys '26: The 13th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation","location":"Banff Canada","acronym":"BuildSys '26","sponsor":["SIGEnergy ACM Special Interest Group on Energy Systems and Informatics"]},"container-title":["Proceedings of the 13th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation"],"original-title":[],"deposited":{"date-parts":[[2026,6,19]],"date-time":"2026-06-19T11:29:17Z","timestamp":1781868557000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3744256.3812552"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,6,22]]},"references-count":38,"alternative-id":["10.1145\/3744256.3812552","10.1145\/3744256"],"URL":"https:\/\/doi.org\/10.1145\/3744256.3812552","relation":{},"subject":[],"published":{"date-parts":[[2026,6,22]]},"assertion":[{"value":"2026-06-22","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}