{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,16]],"date-time":"2026-06-16T11:53:33Z","timestamp":1781610813345,"version":"3.54.5"},"publisher-location":"New York, NY, USA","reference-count":80,"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"}],"funder":[{"name":"RGC GRF","award":["15201322"],"award-info":[{"award-number":["15201322"]}]},{"name":"RGC GRF","award":["15230624"],"award-info":[{"award-number":["15230624"]}]},{"name":"RGC GRF","award":["15239925"],"award-info":[{"award-number":["15239925"]}]},{"name":"CRF","award":["C5020-25GF"],"award-info":[{"award-number":["C5020-25GF"]}]},{"name":"ITC","award":["ITS\/052\/23MX"],"award-info":[{"award-number":["ITS\/052\/23MX"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2026,6,22]]},"DOI":"10.1145\/3744255.3811745","type":"proceedings-article","created":{"date-parts":[[2026,6,16]],"date-time":"2026-06-16T10:13:49Z","timestamp":1781604829000},"page":"636-651","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["ThermoStill: Distilling Time Series Foundation Model into Thermal Dynamics Model for HVAC Model Predictive Control"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0009-0005-6109-3049","authenticated-orcid":false,"given":"Rui","family":"Liang","sequence":"first","affiliation":[{"name":"The Hong Kong Polytechnic University, Hong Kong, Hong Kong"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9822-3346","authenticated-orcid":false,"given":"Yang","family":"Deng","sequence":"additional","affiliation":[{"name":"The Hong Kong Polytechnic University, Hong Kong, Hong Kong"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0009-5564-4624","authenticated-orcid":false,"given":"Yaohui","family":"Liu","sequence":"additional","affiliation":[{"name":"The Hong Kong Polytechnic University, Hong Kong, Hong Kong"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8107-4260","authenticated-orcid":false,"given":"Dafang","family":"Zhao","sequence":"additional","affiliation":[{"name":"The University of Osaka, Osaka, Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0004-5302-0352","authenticated-orcid":false,"given":"Ozan Baris","family":"Mulayim","sequence":"additional","affiliation":[{"name":"Carnegie Mellon University, Pittsburgh, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7843-5907","authenticated-orcid":false,"given":"Ittetsu","family":"Taniguchi","sequence":"additional","affiliation":[{"name":"The University of Osaka, Osaka, Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0921-2726","authenticated-orcid":false,"given":"Dan","family":"Wang","sequence":"additional","affiliation":[{"name":"The Hong Kong University of Science and Technology, Hong Kong, Hong Kong"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2026,6,22]]},"reference":[{"key":"e_1_3_3_2_2_2","unstructured":"Abdul\u00a0Fatir Ansari Lorenzo Stella Caner Turkmen Xiyuan Zhang Pedro Mercado Huibin Shen Oleksandr Shchur Syama\u00a0Sundar Rangapuram Sebastian\u00a0Pineda Arango Shubham Kapoor et\u00a0al. 2024. Chronos: Learning the language of time series. arXiv preprint arXiv:https:\/\/arXiv.org\/abs\/2403.07815 (2024)."},{"key":"e_1_3_3_2_3_2","doi-asserted-by":"publisher","unstructured":"Peder Bacher and Henrik Madsen. 2011. Identifying suitable models for the heat dynamics of buildings. Energy and Buildings 43 7 (2011) 1511\u20131522. 10.1016\/j.enbuild.2011.02.005","DOI":"10.1016\/j.enbuild.2011.02.005"},{"key":"e_1_3_3_2_4_2","doi-asserted-by":"crossref","unstructured":"David Blum Javier Arroyo Sen Huang J\u00e1n Drgo\u0148a Filip Jorissen Harald\u00a0Taxt Walnum Yan Chen Kyle Benne Draguna Vrabie Michael Wetter et\u00a0al. 2021. Building optimization testing framework (BOPTEST) for simulation-based benchmarking of control strategies in buildings. Journal of Building Performance Simulation 14 5 (2021) 586\u2013610.","DOI":"10.1080\/19401493.2021.1986574"},{"key":"e_1_3_3_2_5_2","unstructured":"Rishi Bommasani. 2021. On the opportunities and risks of foundation models. arXiv preprint arXiv:https:\/\/arXiv.org\/abs\/2108.07258 (2021)."},{"key":"e_1_3_3_2_6_2","doi-asserted-by":"publisher","DOI":"10.1145\/3716554.3716619"},{"key":"e_1_3_3_2_7_2","doi-asserted-by":"crossref","unstructured":"Felix B\u00fcnning et\u00a0al. 2022. Physics-informed linear regression is competitive with two Machine Learning methods in residential building MPC. Applied Energy 310 (2022) 118491.","DOI":"10.1016\/j.apenergy.2021.118491"},{"key":"e_1_3_3_2_8_2","doi-asserted-by":"crossref","unstructured":"Gaurav Chaudhary Hicham Johra Laurent Georges and Bj\u00f8rn Austb\u00f8. 2025. Transfer learning in building dynamics prediction. Energy and Buildings 330 (2025) 115384.","DOI":"10.1016\/j.enbuild.2025.115384"},{"key":"e_1_3_3_2_9_2","doi-asserted-by":"publisher","unstructured":"Xin Chen et\u00a0al. 2022. Reinforcement Learning for Selective Key Applications in Power Systems: Recent Advances and Future Challenges. IEEE Transactions on Smart Grid 13 4 (2022) 2935\u20132958. 10.1109\/TSG.2022.3154718","DOI":"10.1109\/TSG.2022.3154718"},{"key":"e_1_3_3_2_10_2","volume-title":"Forty-first International Conference on Machine Learning","author":"Das Abhimanyu","year":"2024","unstructured":"Abhimanyu Das, Weihao Kong, Rajat Sen, and Yichen Zhou. 2024. A decoder-only foundation model for time-series forecasting. In Forty-first International Conference on Machine Learning."},{"key":"e_1_3_3_2_11_2","doi-asserted-by":"crossref","unstructured":"J\u00e1n Drgo\u0148a Javier Arroyo Iago\u00a0Cupeiro Figueroa David Blum Krzysztof Arendt Donghun Kim Enric\u00a0Perarnau Oll\u00e9 Juraj Oravec Michael Wetter Draguna\u00a0L Vrabie et\u00a0al. 2020. All you need to know about model predictive control for buildings. Annual reviews in control 50 (2020) 190\u2013232.","DOI":"10.1016\/j.arcontrol.2020.09.001"},{"key":"e_1_3_3_2_12_2","doi-asserted-by":"crossref","unstructured":"Yuwei Fan Tao Song Chenlong Feng Chao Liu and Dongxiang Jiang. 2025. Wind power prediction using foundation large time series models enhanced by time series prompt in exogenous and tuning forms. Applied Energy 400 (2025) 126535.","DOI":"10.1016\/j.apenergy.2025.126535"},{"key":"e_1_3_3_2_13_2","doi-asserted-by":"publisher","DOI":"10.1145\/3679240.3734584"},{"key":"e_1_3_3_2_14_2","doi-asserted-by":"crossref","unstructured":"Srinivas Garimella Kristian Lockyear David Pharis Omar El\u00a0Chawa Matthew\u00a0T Hughes and Girish Kini. 2022. Realistic pathways to decarbonization of building energy systems. Joule 6 5 (2022) 956\u2013971.","DOI":"10.1016\/j.joule.2022.04.003"},{"key":"e_1_3_3_2_15_2","doi-asserted-by":"crossref","unstructured":"Jianping Gou Baosheng Yu Stephen\u00a0J Maybank and Dacheng Tao. 2021. Knowledge distillation: A survey. International journal of computer vision 129 6 (2021) 1789\u20131819.","DOI":"10.1007\/s11263-021-01453-z"},{"key":"e_1_3_3_2_16_2","unstructured":"Yanggan Gu Yuanyi Wang Zhaoyi Yan Yiming Zhang Qi Zhou Fei Wu and Hongxia Yang. 2025. InfiFPO: Implicit model fusion via preference optimization in large language models. arXiv preprint arXiv:https:\/\/arXiv.org\/abs\/2505.13878 (2025)."},{"key":"e_1_3_3_2_17_2","doi-asserted-by":"crossref","unstructured":"Dimitri Guyot Florine Giraud Florian Simon David Corgier Christophe Marvillet and Brice Tremeac. 2020. Building energy model calibration: A detailed case study using sub-hourly measured data. Energy and Buildings 223 (2020) 110189.","DOI":"10.1016\/j.enbuild.2020.110189"},{"key":"e_1_3_3_2_18_2","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2025\/1074"},{"key":"e_1_3_3_2_19_2","doi-asserted-by":"publisher","DOI":"10.1145\/3679240.3734633"},{"key":"e_1_3_3_2_20_2","doi-asserted-by":"crossref","unstructured":"Seon-Young Heo Jin-Hong Kim Sunghyun Kim Young\u00a0Sub Kim and Cheol\u00a0Soo Park. 2025. Graphical federated MPC for real-life cooling towers and chillers using two simple thermal and energy efficiencies. Energy and Buildings (2025) 116769.","DOI":"10.1016\/j.enbuild.2025.116769"},{"key":"e_1_3_3_2_21_2","unstructured":"Geoffrey Hinton Oriol Vinyals and Jeff Dean. 2015. Distilling the knowledge in a neural network. arXiv preprint arXiv:https:\/\/arXiv.org\/abs\/1503.02531 (2015)."},{"key":"e_1_3_3_2_22_2","unstructured":"Chengsong Huang Qian Liu Bill\u00a0Yuchen Lin Tianyu Pang Chao Du and Min Lin. 2023. Lorahub: Efficient cross-task generalization via dynamic lora composition. arXiv preprint arXiv:https:\/\/arXiv.org\/abs\/2307.13269 (2023)."},{"key":"e_1_3_3_2_23_2","unstructured":"Nan Huang Haishuai Wang Zihuai He Marinka Zitnik and Xiang Zhang. 2024. Repurposing foundation model for generalizable medical time series classification. arXiv preprint arXiv:https:\/\/arXiv.org\/abs\/2410.03794 (2024)."},{"key":"e_1_3_3_2_24_2","unstructured":"IEA. 2022. Global status report for buildings and construction. https:\/\/www.iea.org\/energy-system\/buildings."},{"key":"e_1_3_3_2_25_2","doi-asserted-by":"crossref","unstructured":"Camille John Charalampos Vallianos Jos\u00e9 Candanedo and Andreas Athienitis. 2018. Estimating time constants for over 10 000 residential buildings in North America: towards a statistical characterization of thermal dynamics. (2018).","DOI":"10.14305\/ibpc.2018.ps17"},{"key":"e_1_3_3_2_26_2","doi-asserted-by":"publisher","DOI":"10.1145\/3679240.3734632"},{"key":"e_1_3_3_2_27_2","doi-asserted-by":"publisher","DOI":"10.1145\/3722565.3727193"},{"key":"e_1_3_3_2_28_2","doi-asserted-by":"crossref","unstructured":"Benjamin\u00a0D Leibowicz Christopher\u00a0M Lanham Max\u00a0T Brozynski Jos\u00e9\u00a0R V\u00e1zquez-Canteli Nicol\u00e1s\u00a0Castillo Castej\u00f3n and Zoltan Nagy. 2018. Optimal decarbonization pathways for urban residential building energy services. Applied energy 230 (2018) 1311\u20131325.","DOI":"10.1016\/j.apenergy.2018.09.046"},{"key":"e_1_3_3_2_29_2","unstructured":"Hao Li Bowen Deng Chang Xu Zhiyuan Feng Viktor Schlegel Yu-Hao Huang Yizheng Sun Jingyuan Sun Kailai Yang Yiyao Yu et\u00a0al. 2025. MIRA: Medical Time Series Foundation Model for Real-World Health Data. arXiv preprint arXiv:https:\/\/arXiv.org\/abs\/2506.07584 (2025)."},{"key":"e_1_3_3_2_30_2","doi-asserted-by":"crossref","unstructured":"Han Li Giuseppe Pinto Marco\u00a0Savino Piscitelli Alfonso Capozzoli and Tianzhen Hong. 2024. Building thermal dynamics modeling with deep transfer learning using a large residential smart thermostat dataset. Engineering Applications of Artificial Intelligence 130 (2024) 107701.","DOI":"10.1016\/j.engappai.2023.107701"},{"key":"e_1_3_3_2_31_2","doi-asserted-by":"publisher","DOI":"10.1145\/3736425.3772354"},{"key":"e_1_3_3_2_32_2","unstructured":"Weishi Li Yong Peng Miao Zhang Liang Ding Han Hu and Li Shen. 2023. Deep model fusion: A survey. arXiv preprint arXiv:https:\/\/arXiv.org\/abs\/2309.15698 (2023)."},{"key":"e_1_3_3_2_33_2","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v37i2.25236"},{"key":"e_1_3_3_2_34_2","unstructured":"Zuguang Li Shaohua Wu Wen Wu Qiaohua Ling Yaping Sun and Hui Wang. 2025. Split Knowledge Distillation for Large Models in IoT: Challenges and Solutions. IEEE Internet of Things Magazine (2025)."},{"key":"e_1_3_3_2_35_2","unstructured":"Pengchen Liang Haishan Huang Bin Pu Jianguo Chen Xiang Hua Jing Zhang Weibo Ma Zhuangzhuang Chen Yiwei Li and Qing Chang. 2025. Task-Specific Knowledge Distillation from the Vision Foundation Model for Enhanced Medical Image Segmentation. arXiv preprint arXiv:https:\/\/arXiv.org\/abs\/2503.06976 (2025)."},{"key":"e_1_3_3_2_36_2","volume-title":"ICML 2025 CO-BUILD Workshop on Computational Optimization of Buildings","author":"LIANG Rui","year":"2025","unstructured":"Rui LIANG, Yang Deng, xiedonghua, and Dan Wang. 2025. Enabling Time-series Foundation Model for Building Energy Forecasting via Contrastive Curriculum Learning. In ICML 2025 CO-BUILD Workshop on Computational Optimization of Buildings. https:\/\/openreview.net\/forum?id=7TgKHQeUsL"},{"key":"e_1_3_3_2_37_2","doi-asserted-by":"publisher","DOI":"10.1145\/3637528.3671451"},{"key":"e_1_3_3_2_38_2","unstructured":"Fan Liu Behrooz Farkiani and Patrick Crowley. 2025. Time-Series Foundation Models for ISP Traffic Forecasting. arXiv preprint arXiv:https:\/\/arXiv.org\/abs\/2511.17529 (2025)."},{"key":"e_1_3_3_2_39_2","doi-asserted-by":"crossref","unstructured":"Yuang Liu Wei Zhang and Jun Wang. 2020. Adaptive multi-teacher multi-level knowledge distillation. Neurocomputing 415 (2020) 106\u2013113.","DOI":"10.1016\/j.neucom.2020.07.048"},{"key":"e_1_3_3_2_40_2","doi-asserted-by":"publisher","DOI":"10.1145\/3708036.3708154"},{"key":"e_1_3_3_2_41_2","doi-asserted-by":"publisher","unstructured":"Na Luo and Tianzhen Hong. 2022. Ecobee Donate Your Data 1 000 homes in 2017. (3 2022). 10.25584\/ecobee\/1854924","DOI":"10.25584\/ecobee\/1854924"},{"key":"e_1_3_3_2_42_2","unstructured":"Jiabo Ma Zhengrui Guo Fengtao Zhou Yihui Wang Yingxue Xu Jinbang Li Fang Yan Yu Cai Zhengjie Zhu Cheng Jin et\u00a0al. 2024. Towards a generalizable pathology foundation model via unified knowledge distillation. arXiv preprint arXiv:https:\/\/arXiv.org\/abs\/2407.18449 (2024)."},{"key":"e_1_3_3_2_43_2","unstructured":"Amir\u00a0M Mansourian Rozhan Ahmadi Masoud Ghafouri Amir\u00a0Mohammad Babaei Elaheh\u00a0Badali Golezani Zeynab\u00a0Yasamani Ghamchi Vida Ramezanian Alireza Taherian Kimia Dinashi Amirali Miri et\u00a0al. 2025. A Comprehensive Survey on Knowledge Distillation. arXiv preprint arXiv:https:\/\/arXiv.org\/abs\/2503.12067 (2025)."},{"key":"e_1_3_3_2_44_2","unstructured":"Ben\u00a0A Marconi. 2025. Time series foundation models for multivariate financial time series forecasting. arXiv preprint arXiv:https:\/\/arXiv.org\/abs\/2507.07296 (2025)."},{"key":"e_1_3_3_2_45_2","unstructured":"Marcel Meyer David Zapata Sascha Kaltenpoth and Oliver M\u00fcller. 2024. Benchmarking time series foundation models for short-term household electricity load forecasting. arXiv preprint arXiv:https:\/\/arXiv.org\/abs\/2410.09487 (2024)."},{"key":"e_1_3_3_2_46_2","doi-asserted-by":"crossref","unstructured":"Panagiotis Michailidis Iakovos Michailidis Federico Minelli Hasan\u00a0Huseyin Coban and Elias Kosmatopoulos. 2025. Model Predictive Control for Smart Buildings: Applications and Innovations in Energy Management. Buildings 15 18 (2025) 3298.","DOI":"10.3390\/buildings15183298"},{"key":"e_1_3_3_2_47_2","doi-asserted-by":"publisher","DOI":"10.1145\/3736425.3770113"},{"key":"e_1_3_3_2_48_2","doi-asserted-by":"publisher","DOI":"10.1109\/SmartGridComm60555.2024.10738056"},{"key":"e_1_3_3_2_49_2","doi-asserted-by":"publisher","DOI":"10.1145\/3671127.3698177"},{"key":"e_1_3_3_2_50_2","doi-asserted-by":"crossref","unstructured":"Ozan\u00a0Baris Mulayim Pengrui Quan Liying Han Xiaomin Ouyang Dezhi Hong Mario Berg\u00e9s and Mani Srivastava. 2025. Can Time-Series Foundation Models Perform Building Energy Management Tasks? arXiv preprint arXiv:https:\/\/arXiv.org\/abs\/2506.11250 (2025).","DOI":"10.1017\/dce.2026.10040"},{"key":"e_1_3_3_2_51_2","doi-asserted-by":"crossref","unstructured":"Gernot Pucher Amin Dada Felix Nensa Martin Schuler Christian Reinhardt Jens Kleesiek and Christopher\u00a0M Sauer. 2025. Evaluating zero-shot foundation models for time series forecasting in clinical settings: A simulation study with electronic health records. Studies in health technology and informatics 329 (2025) 820\u2013824.","DOI":"10.3233\/SHTI250954"},{"key":"e_1_3_3_2_52_2","doi-asserted-by":"publisher","DOI":"10.1145\/3690624.3709325"},{"key":"e_1_3_3_2_53_2","doi-asserted-by":"crossref","unstructured":"Eghbal Rahimikia Hao Ni and Weiguan Wang. 2025. Re (Visiting) Time Series Foundation Models in Finance. arXiv preprint arXiv:https:\/\/arXiv.org\/abs\/2511.18578 (2025).","DOI":"10.2139\/ssrn.5770562"},{"key":"e_1_3_3_2_54_2","doi-asserted-by":"publisher","DOI":"10.1145\/3679240.3734589"},{"key":"e_1_3_3_2_55_2","doi-asserted-by":"crossref","unstructured":"Simon Rouchier Micka\u00ebl Rabouille and Pierre Oberl\u00e9. 2018. Calibration of simplified building energy models for parameter estimation and forecasting: Stochastic versus deterministic modelling. Building and Environment 134 (2018) 181\u2013190.","DOI":"10.1016\/j.buildenv.2018.02.043"},{"key":"e_1_3_3_2_56_2","first-page":"353","volume-title":"European Conference on Computer Vision","author":"Sar\u0131y\u0131ld\u0131z Mert\u00a0B\u00fclent","year":"2024","unstructured":"Mert\u00a0B\u00fclent Sar\u0131y\u0131ld\u0131z, Philippe Weinzaepfel, Thomas Lucas, Diane Larlus, and Yannis Kalantidis. 2024. UNIC: Universal classification models via multi-teacher distillation. In European Conference on Computer Vision. Springer, 353\u2013371."},{"key":"e_1_3_3_2_57_2","unstructured":"John Schulman Filip Wolski Prafulla Dhariwal Alec Radford and Oleg Klimov. 2017. Proximal policy optimization algorithms. arXiv preprint arXiv:https:\/\/arXiv.org\/abs\/1707.06347 (2017)."},{"key":"e_1_3_3_2_58_2","unstructured":"Xiaoming Shi Shiyu Wang Yuqi Nie Dianqi Li Zhou Ye Qingsong Wen and Ming Jin. 2024. Time-moe: Billion-scale time series foundation models with mixture of experts. arXiv preprint arXiv:https:\/\/arXiv.org\/abs\/2409.16040 (2024)."},{"key":"e_1_3_3_2_59_2","first-page":"9626","volume-title":"International Conference on Machine Learning","author":"Shu Yang","year":"2021","unstructured":"Yang Shu, Zhi Kou, Zhangjie Cao, Jianmin Wang, and Mingsheng Long. 2021. Zoo-tuning: Adaptive transfer from a zoo of models. In International Conference on Machine Learning. PMLR, 9626\u20139637."},{"key":"e_1_3_3_2_60_2","doi-asserted-by":"crossref","unstructured":"Rui Tang and Shengwei Wang. 2019. Model predictive control for thermal energy storage and thermal comfort optimization of building demand response in smart grids. Applied Energy 242 (2019) 873\u2013882.","DOI":"10.1016\/j.apenergy.2019.03.038"},{"key":"e_1_3_3_2_61_2","doi-asserted-by":"crossref","unstructured":"Shihao Tu Yupeng Zhang Jing Zhang Zhendong Fu Yin Zhang and Yang Yang. 2024. Powerpm: Foundation model for power systems. Advances in Neural Information Processing Systems 37 (2024) 115233\u2013115260.","DOI":"10.52202\/079017-3659"},{"key":"e_1_3_3_2_62_2","doi-asserted-by":"crossref","unstructured":"Charalampos Vallianos Andreas Athienitis and Benoit Delcroix. 2022. Automatic generation of multi-zone RC models using smart thermostat data from homes. Energy and Buildings 277 (2022) 112571.","DOI":"10.1016\/j.enbuild.2022.112571"},{"key":"e_1_3_3_2_63_2","doi-asserted-by":"crossref","unstructured":"Chenghao Wang Jiyun Song Dachuan Shi Janet\u00a0L Reyna Henry Horsey Sarah Feron Yuyu Zhou Zutao Ouyang Ying Li and Robert\u00a0B Jackson. 2023. Impacts of climate change population growth and power sector decarbonization on urban building energy use. Nature Communications 14 1 (2023) 6434.","DOI":"10.1038\/s41467-023-41458-5"},{"key":"e_1_3_3_2_64_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICASSP.2019.8682450"},{"key":"e_1_3_3_2_65_2","doi-asserted-by":"crossref","unstructured":"Tianqi Xiao and Fengqi You. 2023. Building thermal modeling and model predictive control with physically consistent deep learning for decarbonization and energy optimization. Applied Energy 342 (2023) 121165.","DOI":"10.1016\/j.apenergy.2023.121165"},{"key":"e_1_3_3_2_66_2","doi-asserted-by":"crossref","unstructured":"Jiajia Xie Han Li and Tianzhen Hong. 2024. A lifelong meta-learning approach for learning deep grey-box representative thermal dynamics models for residential buildings. Energy and Buildings 318 (2024) 114408.","DOI":"10.1016\/j.enbuild.2024.114408"},{"key":"e_1_3_3_2_67_2","doi-asserted-by":"crossref","unstructured":"Qing Xu Keyu Wu Min Wu Kezhi Mao Xiaoli Li and Zhenghua Chen. 2023. Reinforced knowledge distillation for time series regression. IEEE Transactions on Artificial Intelligence 5 6 (2023) 3184\u20133194.","DOI":"10.1109\/TAI.2023.3341854"},{"key":"e_1_3_3_2_68_2","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v39i9.32990"},{"key":"e_1_3_3_2_69_2","doi-asserted-by":"publisher","DOI":"10.1145\/3336191.3371792"},{"key":"e_1_3_3_2_70_2","unstructured":"Qingren Yao Chao-Han\u00a0Huck Yang Renhe Jiang Yuxuan Liang Ming Jin and Shirui Pan. 2024. Towards neural scaling laws for time series foundation models. arXiv preprint arXiv:https:\/\/arXiv.org\/abs\/2410.12360 (2024)."},{"key":"e_1_3_3_2_71_2","doi-asserted-by":"crossref","unstructured":"Peipei Yu Hongcai Zhang Yonghua Song Zhenyi Wang Huiyu Dong and Liang Ji. 2025. Safe reinforcement learning for power system control: A review. Renewable and Sustainable Energy Reviews 223 (2025) 116022.","DOI":"10.1016\/j.rser.2025.116022"},{"key":"e_1_3_3_2_72_2","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v39i1.32085"},{"key":"e_1_3_3_2_73_2","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v35i16.17680"},{"key":"e_1_3_3_2_74_2","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v37i9.26317"},{"key":"e_1_3_3_2_75_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICASSP43922.2022.9747534"},{"key":"e_1_3_3_2_76_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICME55011.2023.00333"},{"key":"e_1_3_3_2_77_2","doi-asserted-by":"crossref","unstructured":"Dafang Zhao Daichi Watari Yuki Ozawa Ittetsu Taniguchi Toshihiro Suzuki Yoshiyuki Shimoda and Takao Onoye. 2022. A thermal comfort and peak power demand aware vrf heating\/cooling management framework: Simulation and on-site experiment. Journal of Information Processing 30 (2022) 476\u2013485.","DOI":"10.2197\/ipsjjip.30.476"},{"key":"e_1_3_3_2_78_2","doi-asserted-by":"crossref","unstructured":"Dafang Zhao Daichi Watari Yuki Ozawa Ittetsu Taniguchi Toshihiro Suzuki Yoshiyuki Shimoda and Takao Onoye. 2023. Data-driven online energy management framework for HVAC systems: An experimental study. Applied Energy 352 (2023) 121921.","DOI":"10.1016\/j.apenergy.2023.121921"},{"key":"e_1_3_3_2_79_2","doi-asserted-by":"crossref","unstructured":"Wanfu Zheng Dan Wang and Zhe Wang. 2024. Economic model predictive control for building HVAC system: A comparative analysis of model-based and data-driven approaches using the BOPTEST Framework. Applied Energy 374 (2024) 123969.","DOI":"10.1016\/j.apenergy.2024.123969"},{"key":"e_1_3_3_2_80_2","doi-asserted-by":"crossref","unstructured":"Jie Zhu Jide Niu Sicheng Zhan Zhe Tian Adrian Chong Huilong Wang and Haizhu Zhou. 2025. A learning-based model predictive control method for unlocking the potential of building energy flexibility. Energy and Buildings 330 (2025) 115299.","DOI":"10.1016\/j.enbuild.2025.115299"},{"key":"e_1_3_3_2_81_2","doi-asserted-by":"publisher","DOI":"10.1145\/3746252.3761261"}],"event":{"name":"E-Energy '26: The 17th ACM International Conference on Future and Sustainable Energy Systems","location":"Banff , Alberta , Canada","acronym":"E-Energy '26","sponsor":["SIGENERGY ACM Special Interest Group on Energy Systems and Informatics"]},"container-title":["Proceedings of the 17th ACM International Conference on Future and Sustainable Energy Systems"],"original-title":[],"deposited":{"date-parts":[[2026,6,16]],"date-time":"2026-06-16T11:15:17Z","timestamp":1781608517000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3744255.3811745"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,6,22]]},"references-count":80,"alternative-id":["10.1145\/3744255.3811745","10.1145\/3744255"],"URL":"https:\/\/doi.org\/10.1145\/3744255.3811745","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"}}]}}