{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,20]],"date-time":"2026-06-20T21:46:24Z","timestamp":1781991984675,"version":"3.54.5"},"publisher-location":"New York, NY, USA","reference-count":51,"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\/3744256.3812565","type":"proceedings-article","created":{"date-parts":[[2026,6,19]],"date-time":"2026-06-19T11:01:41Z","timestamp":1781866901000},"page":"115-125","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["Thermal-GEMs: Generalized Models for Building Thermal Dynamics"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0009-0007-0203-6946","authenticated-orcid":false,"given":"Felix","family":"Koch","sequence":"first","affiliation":[{"name":"Technical University of Applied Sciences Rosenheim, Rosenheim, Bavaria, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1869-9801","authenticated-orcid":false,"given":"Fabian","family":"Raisch","sequence":"additional","affiliation":[{"name":"Technical University of Applied Sciences Rosenheim, Rosenheim, Bavaria, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6274-8036","authenticated-orcid":false,"given":"Benjamin","family":"Tischler","sequence":"additional","affiliation":[{"name":"Technical University of Applied Sciences Rosenheim, Rosenheim, Bavaria, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2026,6,22]]},"reference":[{"key":"e_1_3_3_2_2_2","doi-asserted-by":"publisher","DOI":"10.1145\/3292500.3330701"},{"key":"e_1_3_3_2_3_2","unstructured":"Abdul\u00a0Fatir Ansari Oleksandr Shchur Jaris K\u00fcken Andreas Auer Boran Han Pedro Mercado Syama\u00a0Sundar Rangapuram Huibin Shen Lorenzo Stella Xiyuan Zhang et\u00a0al. 2025. Chronos-2: From Univariate to Universal Forecasting. arXiv preprint arXiv:https:\/\/arXiv.org\/abs\/2510.15821 (2025)."},{"key":"e_1_3_3_2_4_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_5_2","doi-asserted-by":"crossref","unstructured":"Javier Arroyo Carlo Manna Fred Spiessens and Lieve Helsen. 2022. Reinforced model predictive control (RL-MPC) for building energy management. Applied Energy 309 (2022) 118346.","DOI":"10.1016\/j.apenergy.2021.118346"},{"key":"e_1_3_3_2_6_2","doi-asserted-by":"crossref","unstructured":"Yasaman Balali Adrian Chong Andrew Busch and Steven O\u2019Keefe. 2023. Energy modelling and control of building heating and cooling systems with data-driven and hybrid models\u2014A review. Renewable and Sustainable Energy Reviews 183 (2023) 113496.","DOI":"10.1016\/j.rser.2023.113496"},{"key":"e_1_3_3_2_7_2","doi-asserted-by":"crossref","unstructured":"Michael Batty. 2018. Digital twins. 817\u2013820\u00a0pages.","DOI":"10.1177\/2399808318796416"},{"key":"e_1_3_3_2_8_2","doi-asserted-by":"crossref","unstructured":"Maximilian Beck Korbinian P\u00f6ppel Markus Spanring Andreas Auer Oleksandra Prudnikova Michael Kopp G\u00fcnter Klambauer Johannes Brandstetter and Sepp Hochreiter. 2024. xlstm: Extended long short-term memory. Advances in Neural Information Processing Systems 37 (2024) 107547\u2013107603.","DOI":"10.52202\/079017-3417"},{"key":"e_1_3_3_2_9_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_10_2","doi-asserted-by":"crossref","unstructured":"Yujiao Chen Zheming Tong Yang Zheng Holly Samuelson and Leslie Norford. 2020. Transfer learning with deep neural networks for model predictive control of HVAC and natural ventilation in smart buildings. Journal of Cleaner Production 254 (2020) 119866.","DOI":"10.1016\/j.jclepro.2019.119866"},{"key":"e_1_3_3_2_11_2","doi-asserted-by":"publisher","unstructured":"Zhelun Chen Zheng O\u2019Neill Jin Wen Ojas Pradhan Tao Yang Xing Lu Guanjing Lin Shohei Miyata Seungjae Lee Chou Shen Roberto Chiosa Marco\u00a0Savino Piscitelli Alfonso Capozzoli Franz Hengel Alexander K\u00fchrer Marco Pritoni Wei Liu John Clau\u00df Yimin Chen and Terry Herr. 2023. A review of data-driven fault detection and diagnostics for building HVAC systems. Applied Energy 339 (2023) 121030. 10.1016\/j.apenergy.2023.121030","DOI":"10.1016\/j.apenergy.2023.121030"},{"key":"e_1_3_3_2_12_2","doi-asserted-by":"crossref","unstructured":"Wonjun Choi and Sangwon Lee. 2023. Performance evaluation of deep learning architectures for load and temperature forecasting under dataset size constraints and seasonality. Energy and buildings 288 (2023) 113027.","DOI":"10.1016\/j.enbuild.2023.113027"},{"key":"e_1_3_3_2_13_2","unstructured":"Ben Cohen Emaad Khwaja Youssef Doubli Salahidine Lemaachi Chris Lettieri Charles Masson Hugo Miccinilli Elise Ram\u00e9 Qiqi Ren Afshin Rostamizadeh et\u00a0al. 2025. This Time is Different: An Observability Perspective on Time Series Foundation Models. arXiv preprint arXiv:https:\/\/arXiv.org\/abs\/2505.14766 (2025)."},{"key":"e_1_3_3_2_14_2","unstructured":"Copernicus Climate Change Service (C3S). 2017. ERA5: Fifth generation of ECMWF atmospheric reanalyses of the global climate. Climate Data Store (CDS). Access via cds.climate.copernicus.eu."},{"key":"e_1_3_3_2_15_2","doi-asserted-by":"publisher","unstructured":"Davide Coraci Silvio Brandi and Alfonso Capozzoli. 2023. Effective pre-training of a deep reinforcement learning agent by means of long short-term memory models for thermal energy management in buildings. Energy Conversion and Management 291 (2023) 117303. 10.1016\/j.enconman.2023.117303","DOI":"10.1016\/j.enconman.2023.117303"},{"key":"e_1_3_3_2_16_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_17_2","doi-asserted-by":"crossref","unstructured":"Hongwen Dou and Kun Zhang. 2025. Transfer learning for cross-building forecasting of building energy and indoor air temperature in model predictive control applications. Journal of Building Engineering (2025) 113341.","DOI":"10.1016\/j.jobe.2025.113341"},{"key":"e_1_3_3_2_18_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_19_2","doi-asserted-by":"publisher","unstructured":"Zhimin Du Xinqiao Jin and Yunyu Yang. 2009. Fault diagnosis for temperature flow rate and pressure sensors in VAV systems using wavelet neural network. Applied Energy 86 9 (2009) 1624\u20131631. 10.1016\/j.apenergy.2009.01.015","DOI":"10.1016\/j.apenergy.2009.01.015"},{"key":"e_1_3_3_2_20_2","doi-asserted-by":"crossref","unstructured":"Vijay Ekambaram Arindam Jati Pankaj Dayama Sumanta Mukherjee Nam Nguyen Wesley\u00a0M Gifford Chandra Reddy and Jayant Kalagnanam. 2024. Tiny time mixers (ttms): Fast pre-trained models for enhanced zero\/few-shot forecasting of multivariate time series. Advances in Neural Information Processing Systems 37 (2024) 74147\u201374181.","DOI":"10.52202\/079017-2359"},{"key":"e_1_3_3_2_21_2","doi-asserted-by":"crossref","unstructured":"Furkan Elmaz Reinout Eyckerman Wim Casteels Steven Latr\u00e9 and Peter Hellinckx. 2021. CNN-LSTM architecture for predictive indoor temperature modeling. Building and Environment 206 (2021) 108327.","DOI":"10.1016\/j.buildenv.2021.108327"},{"key":"e_1_3_3_2_22_2","unstructured":"Google Research. 2025. TimesFM: Time Series Foundation Model. https:\/\/github.com\/google-research\/timesfm. Accessed: 2025-12-09."},{"key":"e_1_3_3_2_23_2","volume-title":"International Conference on Machine Learning","author":"Goswami Mononito","year":"2024","unstructured":"Mononito Goswami, Konrad Szafer, Arjun Choudhry, Yifu Cai, Shuo Li, and Artur Dubrawski. 2024. MOMENT: A Family of Open Time-series Foundation Models. In International Conference on Machine Learning."},{"key":"e_1_3_3_2_24_2","unstructured":"Albert Gu and Tri Dao. 2023. Mamba: Linear-time sequence modeling with selective state spaces. arXiv preprint arXiv:https:\/\/arXiv.org\/abs\/2312.00752 (2023)."},{"key":"e_1_3_3_2_25_2","doi-asserted-by":"crossref","unstructured":"Matthias Hertel Maximilian Beichter Benedikt Heidrich Oliver Neumann Benjamin Sch\u00e4fer Ralf Mikut and Veit Hagenmeyer. 2023. Transformer training strategies for forecasting multiple load time series. Energy Informatics 6 Suppl 1 (2023) 20.","DOI":"10.1186\/s42162-023-00278-z"},{"key":"e_1_3_3_2_26_2","doi-asserted-by":"crossref","unstructured":"Sepp Hochreiter and J\u00fcrgen Schmidhuber. 1997. Long short-term memory. Neural computation 9 8 (1997) 1735\u20131780.","DOI":"10.1162\/neco.1997.9.8.1735"},{"key":"e_1_3_3_2_27_2","unstructured":"Thomas Krug Fabian Raisch Dominik Aimer Markus Wirnsberger Ferdinand Sigg Felix Koch Benjamin Sch\u00e4fer and Benjamin Tischler. 2025. A Highly Configurable Framework for Large-Scale Thermal Building Data Generation to drive Machine Learning Research. arXiv preprint arXiv:https:\/\/arXiv.org\/abs\/2512.00483 (2025)."},{"key":"e_1_3_3_2_28_2","doi-asserted-by":"publisher","DOI":"10.22360\/ANNSIM.2025.MLAIS.36"},{"key":"e_1_3_3_2_29_2","doi-asserted-by":"crossref","unstructured":"Doyun Lee Ryozo Ooka Yuki Matsuda Shintaro Ikeda and Wonjun Choi. 2022. Experimental analysis of artificial intelligence-based model predictive control for thermal energy storage under different cooling load conditions. Sustainable Cities and Society 79 (2022) 103700.","DOI":"10.1016\/j.scs.2022.103700"},{"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","unstructured":"Rui Liang Yang Deng Donghua Xie Fang He and Dan Wang. 2024. Enabling Time-series Foundation Model for Building Energy Forecasting via Contrastive Curriculum Learning. arXiv preprint arXiv:https:\/\/arXiv.org\/abs\/2412.17285 (2024)."},{"key":"e_1_3_3_2_32_2","doi-asserted-by":"crossref","unstructured":"Bryan Lim Sercan\u00a0\u00d6 Ar\u0131k Nicolas Loeff and Tomas Pfister. 2021. Temporal fusion transformers for interpretable multi-horizon time series forecasting. International journal of forecasting 37 4 (2021) 1748\u20131764.","DOI":"10.1016\/j.ijforecast.2021.03.012"},{"key":"e_1_3_3_2_33_2","unstructured":"Ilya Loshchilov and Frank Hutter. 2017. Decoupled weight decay regularization. arXiv preprint arXiv:https:\/\/arXiv.org\/abs\/1711.05101 (2017)."},{"key":"e_1_3_3_2_34_2","volume-title":"Ecobee donate your data 1,000 homes in 2017","author":"Luo Na","year":"2022","unstructured":"Na Luo and Tianzhen Hong. 2022. Ecobee donate your data 1,000 homes in 2017. Technical Report. Pacific Northwest National Lab.(PNNL), Richland, WA (United States)."},{"key":"e_1_3_3_2_35_2","doi-asserted-by":"publisher","DOI":"10.1145\/3671127.3698177"},{"key":"e_1_3_3_2_36_2","volume-title":"International Conference on Learning Representations","author":"Nie Yuqi","year":"2023","unstructured":"Yuqi Nie, Nam H.\u00a0Nguyen, Phanwadee Sinthong, and Jayant Kalagnanam. 2023. A Time Series is Worth 64 Words: Long-term Forecasting with Transformers. In International Conference on Learning Representations."},{"key":"e_1_3_3_2_37_2","doi-asserted-by":"crossref","unstructured":"Young-Jin Park Fran\u00e7ois Germain Jing Liu Ye Wang Toshiaki Koike-Akino Gordon Wichern Navid Azizan Christopher Laughman and Ankush Chakrabarty. 2025. Probabilistic Forecasting for Building Energy Systems using Time-Series Foundation Models. Energy and Buildings (2025) 116446.","DOI":"10.1016\/j.enbuild.2025.116446"},{"key":"e_1_3_3_2_38_2","doi-asserted-by":"crossref","unstructured":"Giuseppe Pinto Riccardo Messina Han Li Tianzhen Hong Marco\u00a0Savino Piscitelli and Alfonso Capozzoli. 2022. Sharing is caring: An extensive analysis of parameter-based transfer learning for the prediction of building thermal dynamics. Energy and Buildings 276 (2022) 112530.","DOI":"10.1016\/j.enbuild.2022.112530"},{"key":"e_1_3_3_2_39_2","doi-asserted-by":"crossref","unstructured":"Giuseppe Pinto Zhe Wang Abhishek Roy Tianzhen Hong and Alfonso Capozzoli. 2022. Transfer learning for smart buildings: A critical review of algorithms applications and future perspectives. Advances in Applied Energy 5 (2022) 100084.","DOI":"10.1016\/j.adapen.2022.100084"},{"key":"e_1_3_3_2_40_2","doi-asserted-by":"crossref","unstructured":"Martin Pullinger Jonathan Kilgour Nigel Goddard Niklas Berliner Lynda Webb Myroslava Dzikovska Heather Lovell Janek Mann Charles Sutton Janette Webb et\u00a0al. 2021. The IDEAL household energy dataset electricity gas contextual sensor data and survey data for 255 UK homes. Scientific Data 8 1 (2021) 146.","DOI":"10.1038\/s41597-021-00921-y"},{"key":"e_1_3_3_2_41_2","unstructured":"Fabian Raisch Timo Germann J.\u00a0Nathan Kutz Christoph Goebel and Benjamin Tischler. 2026. Transfer Learning for Neural Parameter Estimation applied to Building RC Models. arxiv:https:\/\/arXiv.org\/abs\/2604.05904\u00a0[eess.SY] https:\/\/arxiv.org\/abs\/2604.05904"},{"key":"e_1_3_3_2_42_2","doi-asserted-by":"crossref","unstructured":"Fabian Raisch Thomas Krug Christoph Goebel and Benjamin Tischler. 2025. GenTL: A General Transfer Learning Model for Building Thermal Dynamics.","DOI":"10.1145\/3679240.3734589"},{"key":"e_1_3_3_2_43_2","doi-asserted-by":"crossref","unstructured":"Fabian Raisch Max Langtry Felix Koch Ruchi Choudhary Christoph Goebel and Benjamin Tischler. 2025. Adapting to Change: A Comparison of Continual and Transfer Learning for Modeling Building Thermal Dynamics under Concept Drifts. Energy and Buildings (2025) 116868.","DOI":"10.1016\/j.enbuild.2025.116868"},{"key":"e_1_3_3_2_44_2","unstructured":"Kashif Rasul Arjun Ashok Andrew\u00a0Robert Williams Hena Ghonia Rishika Bhagwatkar Arian Khorasani Mohammad Javad\u00a0Darvishi Bayazi George Adamopoulos Roland Riachi Nadhir Hassen Marin Bilo\u0161 Sahil Garg Anderson Schneider Nicolas Chapados Alexandre Drouin Valentina Zantedeschi Yuriy Nevmyvaka and Irina Rish. 2024. Lag-Llama: Towards Foundation Models for Probabilistic Time Series Forecasting. arxiv:https:\/\/arXiv.org\/abs\/2310.08278\u00a0[cs.LG]"},{"key":"e_1_3_3_2_45_2","doi-asserted-by":"crossref","unstructured":"Skipper Seabold Josef Perktold et\u00a0al. 2010. Statsmodels: econometric and statistical modeling with python. SciPy 7 1 (2010) 92\u201396.","DOI":"10.25080\/Majora-92bf1922-011"},{"key":"e_1_3_3_2_46_2","doi-asserted-by":"crossref","unstructured":"Wei Tian Yeonsook Heo Pieter De\u00a0Wilde Zhanyong Li Da Yan Cheol\u00a0Soo Park Xiaohang Feng and Godfried Augenbroe. 2018. A review of uncertainty analysis in building energy assessment. Renewable and Sustainable Energy Reviews 93 (2018).","DOI":"10.1016\/j.rser.2018.05.029"},{"key":"e_1_3_3_2_47_2","unstructured":"Ashish Vaswani Noam Shazeer Niki Parmar Jakob Uszkoreit Llion Jones Aidan\u00a0N Gomez \u0141ukasz Kaiser and Illia Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 (2017)."},{"key":"e_1_3_3_2_48_2","doi-asserted-by":"crossref","unstructured":"Zihan Wang Fanheng Kong Shi Feng Ming Wang Xiaocui Yang Han Zhao Daling Wang and Yifei Zhang. 2025. Is mamba effective for time series forecasting? Neurocomputing 619 (2025) 129178.","DOI":"10.1016\/j.neucom.2024.129178"},{"key":"e_1_3_3_2_49_2","unstructured":"Weather Underground. 2023. Daily Weather History for UK. https:\/\/www.wunderground.com\/history. Accessed: 2025-05-12."},{"key":"e_1_3_3_2_50_2","unstructured":"Gerald Woo Chenghao Liu Akshat Kumar Caiming Xiong Silvio Savarese and Doyen Sahoo. 2024. Unified Training of Universal Time Series Forecasting Transformers. arXiv preprint arXiv:https:\/\/arXiv.org\/abs\/2402.02592 (2024)."},{"key":"e_1_3_3_2_51_2","unstructured":"Haixu Wu Jiehui Xu Jianmin Wang and Mingsheng Long. 2021. Autoformer: Decomposition transformers with auto-correlation for long-term series forecasting. Advances in neural information processing systems 34 (2021) 22419\u201322430."},{"key":"e_1_3_3_2_52_2","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v35i12.17325"}],"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":"2026-06-19T11:32:55Z","timestamp":1781868775000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3744256.3812565"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,6,22]]},"references-count":51,"alternative-id":["10.1145\/3744256.3812565","10.1145\/3744256"],"URL":"https:\/\/doi.org\/10.1145\/3744256.3812565","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"}}]}}