{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,15]],"date-time":"2026-06-15T11:30:54Z","timestamp":1781523054444,"version":"3.54.1"},"publisher-location":"New York, NY, USA","reference-count":50,"publisher":"ACM","license":[{"start":{"date-parts":[[2023,6,20]],"date-time":"2023-06-20T00:00:00Z","timestamp":1687219200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2023,6,20]]},"DOI":"10.1145\/3599733.3600260","type":"proceedings-article","created":{"date-parts":[[2023,6,28]],"date-time":"2023-06-28T20:13:33Z","timestamp":1687983213000},"page":"125-133","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":10,"title":["Synthesizing Building Operation Data with Generative Models: VAEs, GANs, or Something In Between?"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2311-8645","authenticated-orcid":false,"given":"Alessandro","family":"Salatiello","sequence":"first","affiliation":[{"name":"Mitsubishi Electric Research Laboratories, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5220-1830","authenticated-orcid":false,"given":"Ye","family":"Wang","sequence":"additional","affiliation":[{"name":"Mitsubishi Electric Research Laboratories, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8597-6795","authenticated-orcid":false,"given":"Gordon","family":"Wichern","sequence":"additional","affiliation":[{"name":"Mitsubishi Electric Research Laboratories, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2578-5372","authenticated-orcid":false,"given":"Toshiaki","family":"Koike-Akino","sequence":"additional","affiliation":[{"name":"Mitsubishi Electric Research Laboratories, United States of America"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0005-8334-4208","authenticated-orcid":false,"given":"Yoshihiro","family":"Ohta","sequence":"additional","affiliation":[{"name":"Mitsubishi Electric Corporation, Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0000-5980-5741","authenticated-orcid":false,"given":"Yosuke","family":"Kaneko","sequence":"additional","affiliation":[{"name":"Mitsubishi Electric Corporation, Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8540-2249","authenticated-orcid":false,"given":"Christopher","family":"Laughman","sequence":"additional","affiliation":[{"name":"Mitsubishi Electric Research Laboratories, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9637-854X","authenticated-orcid":false,"given":"Ankush","family":"Chakrabarty","sequence":"additional","affiliation":[{"name":"Mitsubishi Electric Research Laboratories, United States of America"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2023,6,28]]},"reference":[{"key":"e_1_3_2_1_1_1","volume-title":"Towards Principled Methods for Training Generative Adversarial Networks. In International Conference on Learning Representations.","author":"Arjovsky Martin","year":"2017","unstructured":"Martin Arjovsky and Leon Bottou . 2017 . Towards Principled Methods for Training Generative Adversarial Networks. In International Conference on Learning Representations. Martin Arjovsky and Leon Bottou. 2017. Towards Principled Methods for Training Generative Adversarial Networks. In International Conference on Learning Representations."},{"key":"e_1_3_2_1_2_1","volume-title":"International conference on machine learning. PMLR, 214\u2013223","author":"Arjovsky Martin","year":"2017","unstructured":"Martin Arjovsky , Soumith Chintala , and L\u00e9on Bottou . 2017 . Wasserstein generative adversarial networks . In International conference on machine learning. PMLR, 214\u2013223 . Martin Arjovsky, Soumith Chintala, and L\u00e9on Bottou. 2017. Wasserstein generative adversarial networks. In International conference on machine learning. PMLR, 214\u2013223."},{"key":"e_1_3_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.egyai.2021.100087"},{"key":"e_1_3_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.apenergy.2019.01.187"},{"key":"e_1_3_2_1_5_1","volume-title":"Deep generative modelling: A comparative review of vaes, gans, normalizing flows, energy-based and autoregressive models","author":"Bond-Taylor Sam","year":"2021","unstructured":"Sam Bond-Taylor , Adam Leach , Yang Long , and Chris\u00a0 G Willcocks . 2021. Deep generative modelling: A comparative review of vaes, gans, normalizing flows, energy-based and autoregressive models . IEEE transactions on pattern analysis and machine intelligence ( 2021 ). Sam Bond-Taylor, Adam Leach, Yang Long, and Chris\u00a0G Willcocks. 2021. Deep generative modelling: A comparative review of vaes, gans, normalizing flows, energy-based and autoregressive models. IEEE transactions on pattern analysis and machine intelligence (2021)."},{"key":"e_1_3_2_1_6_1","volume-title":"International Conference on Learning Representations.","author":"Cemgil Taylan","year":"2020","unstructured":"Taylan Cemgil , Sumedh Ghaisas , Krishnamurthy\u00a0Dj Dvijotham , and Pushmeet Kohli . 2020 . Adversarially robust representations with smooth encoders . In International Conference on Learning Representations. Taylan Cemgil, Sumedh Ghaisas, Krishnamurthy\u00a0Dj Dvijotham, and Pushmeet Kohli. 2020. Adversarially robust representations with smooth encoders. In International Conference on Learning Representations."},{"key":"e_1_3_2_1_7_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.enbuild.2018.06.029"},{"key":"e_1_3_2_1_8_1","volume-title":"Proceedings of the AAAI Conference on Artificial Intelligence, Vol.\u00a036","author":"Das Hari\u00a0Prasanna","year":"2022","unstructured":"Hari\u00a0Prasanna Das , Ryan Tran , Japjot Singh , Xiangyu Yue , Geoffrey Tison , Alberto Sangiovanni-Vincentelli , and Costas\u00a0 J Spanos . 2022 . Conditional synthetic data generation for robust machine learning applications with limited pandemic data . In Proceedings of the AAAI Conference on Artificial Intelligence, Vol.\u00a036 . 11792\u201311800. Hari\u00a0Prasanna Das, Ryan Tran, Japjot Singh, Xiangyu Yue, Geoffrey Tison, Alberto Sangiovanni-Vincentelli, and Costas\u00a0J Spanos. 2022. Conditional synthetic data generation for robust machine learning applications with limited pandemic data. In Proceedings of the AAAI Conference on Artificial Intelligence, Vol.\u00a036. 11792\u201311800."},{"key":"e_1_3_2_1_9_1","volume-title":"Adversarial Feature Learning. In International Conference on Learning Representations.","author":"Donahue Jeff","year":"2017","unstructured":"Jeff Donahue , Philipp Kr\u00e4henb\u00fchl , and Trevor Darrell . 2017 . Adversarial Feature Learning. In International Conference on Learning Representations. Jeff Donahue, Philipp Kr\u00e4henb\u00fchl, and Trevor Darrell. 2017. Adversarial Feature Learning. In International Conference on Learning Representations."},{"key":"e_1_3_2_1_10_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.apenergy.2021.118387"},{"key":"e_1_3_2_1_11_1","unstructured":"Vincent Dumoulin Ishmael Belghazi Ben Poole Olivier Mastropietro Alex Lamb Martin Arjovsky and Aaron Courville. 2017. Adversarially Learned Inference. arxiv:1606.00704\u00a0[stat.ML]  Vincent Dumoulin Ishmael Belghazi Ben Poole Olivier Mastropietro Alex Lamb Martin Arjovsky and Aaron Courville. 2017. Adversarially Learned Inference. arxiv:1606.00704\u00a0[stat.ML]"},{"key":"e_1_3_2_1_12_1","volume-title":"Building Simulation, Vol.\u00a015","author":"Fan Cheng","unstructured":"Cheng Fan , Meiling Chen , Rui Tang , and Jiayuan Wang . 2022. A novel deep generative modeling-based data augmentation strategy for improving short-term building energy predictions . In Building Simulation, Vol.\u00a015 . Tsinghua University Press , 197\u2013211. Cheng Fan, Meiling Chen, Rui Tang, and Jiayuan Wang. 2022. A novel deep generative modeling-based data augmentation strategy for improving short-term building energy predictions. In Building Simulation, Vol.\u00a015. Tsinghua University Press, 197\u2013211."},{"key":"e_1_3_2_1_13_1","volume-title":"Deep learning-based feature engineering methods for improved building energy prediction. Applied energy 240","author":"Fan Cheng","year":"2019","unstructured":"Cheng Fan , Yongjun Sun , Yang Zhao , Mengjie Song , and Jiayuan Wang . 2019. Deep learning-based feature engineering methods for improved building energy prediction. Applied energy 240 ( 2019 ), 35\u201345. Cheng Fan, Yongjun Sun, Yang Zhao, Mengjie Song, and Jiayuan Wang. 2019. Deep learning-based feature engineering methods for improved building energy prediction. Applied energy 240 (2019), 35\u201345."},{"key":"e_1_3_2_1_14_1","doi-asserted-by":"publisher","DOI":"10.1145\/3563357.3564080"},{"key":"e_1_3_2_1_15_1","unstructured":"Ian Goodfellow Jean Pouget-Abadie Mehdi Mirza Bing Xu David Warde-Farley Sherjil Ozair Aaron Courville and Yoshua Bengio. 2014. Generative adversarial nets. In Advances in neural information processing systems. 2672\u20132680. http:\/\/papers.nips.cc\/paper\/5423-generative-adversarial-nets.pdf  Ian Goodfellow Jean Pouget-Abadie Mehdi Mirza Bing Xu David Warde-Farley Sherjil Ozair Aaron Courville and Yoshua Bengio. 2014. Generative adversarial nets. In Advances in neural information processing systems. 2672\u20132680. http:\/\/papers.nips.cc\/paper\/5423-generative-adversarial-nets.pdf"},{"key":"e_1_3_2_1_16_1","doi-asserted-by":"publisher","DOI":"10.1109\/ISGT.2019.8791575"},{"key":"e_1_3_2_1_17_1","unstructured":"Ishaan Gulrajani Faruk Ahmed Martin Arjovsky Vincent Dumoulin and Aaron Courville. 2017. Improved Training of Wasserstein GANs. arxiv:1704.00028\u00a0[cs.LG]  Ishaan Gulrajani Faruk Ahmed Martin Arjovsky Vincent Dumoulin and Aaron Courville. 2017. Improved Training of Wasserstein GANs. arxiv:1704.00028\u00a0[cs.LG]"},{"key":"e_1_3_2_1_18_1","doi-asserted-by":"publisher","DOI":"10.5555\/3295222.3295408"},{"key":"e_1_3_2_1_19_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.apenergy.2021.116601"},{"key":"e_1_3_2_1_20_1","doi-asserted-by":"publisher","DOI":"10.1145\/3127023"},{"key":"e_1_3_2_1_21_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.enbuild.2021.111334"},{"key":"e_1_3_2_1_22_1","volume-title":"Kingma and Max Welling","author":"P.","year":"2014","unstructured":"Diederik\u00a0 P. Kingma and Max Welling . 2014 . Auto-Encoding Variational Bayes. In 2nd International Conference on Learning Representations, ICLR 2014, Banff, AB, Canada, April 14-16, 2014, Conference Track Proceedings . arXiv:http:\/\/arxiv.org\/abs\/1312.6114v10\u00a0[stat.ML] Diederik\u00a0P. Kingma and Max Welling. 2014. Auto-Encoding Variational Bayes. In 2nd International Conference on Learning Representations, ICLR 2014, Banff, AB, Canada, April 14-16, 2014, Conference Track Proceedings. arXiv:http:\/\/arxiv.org\/abs\/1312.6114v10\u00a0[stat.ML]"},{"key":"e_1_3_2_1_23_1","volume-title":"International conference on machine learning. PMLR, 1558\u20131566","author":"Boesen\u00a0Lindbo Larsen Anders","year":"2016","unstructured":"Anders Boesen\u00a0Lindbo Larsen , S\u00f8ren\u00a0Kaae S\u00f8nderby , Hugo Larochelle , and Ole Winther . 2016 . Autoencoding beyond pixels using a learned similarity metric . In International conference on machine learning. PMLR, 1558\u20131566 . Anders Boesen\u00a0Lindbo Larsen, S\u00f8ren\u00a0Kaae S\u00f8nderby, Hugo Larochelle, and Ole Winther. 2016. Autoencoding beyond pixels using a learned similarity metric. In International conference on machine learning. PMLR, 1558\u20131566."},{"key":"e_1_3_2_1_24_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-58574-7_10"},{"key":"e_1_3_2_1_25_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.enbuild.2021.111044"},{"key":"e_1_3_2_1_26_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.apenergy.2021.116459"},{"key":"e_1_3_2_1_27_1","doi-asserted-by":"publisher","DOI":"10.1145\/3419394.3423643"},{"key":"e_1_3_2_1_28_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.enbuild.2021.111718"},{"key":"e_1_3_2_1_29_1","volume-title":"International Conference on Learning Representations.","author":"Makhzani Alireza","year":"2016","unstructured":"Alireza Makhzani , Jonathon Shlens , Navdeep Jaitly , Ian Goodfellow , and Brendan Frey . 2016 . Adversarial autoencoders . In International Conference on Learning Representations. Alireza Makhzani, Jonathon Shlens, Navdeep Jaitly, Ian Goodfellow, and Brendan Frey. 2016. Adversarial autoencoders. In International Conference on Learning Representations."},{"key":"e_1_3_2_1_30_1","volume-title":"International conference on machine learning. 2391\u20132400","author":"Mescheder Lars","year":"2017","unstructured":"Lars Mescheder , Sebastian Nowozin , and Andreas Geiger . 2017 . Adversarial variational Bayes: Unifying variational autoencoders and generative adversarial networks . In International conference on machine learning. 2391\u20132400 . Lars Mescheder, Sebastian Nowozin, and Andreas Geiger. 2017. Adversarial variational Bayes: Unifying variational autoencoders and generative adversarial networks. In International conference on machine learning. 2391\u20132400."},{"key":"e_1_3_2_1_31_1","unstructured":"Mehdi Mirza and Simon Osindero. 2014. Conditional Generative Adversarial Nets. arXiv preprint arXiv:1411.1784. http:\/\/arxiv.org\/abs\/1411.1784  Mehdi Mirza and Simon Osindero. 2014. Conditional Generative Adversarial Nets. arXiv preprint arXiv:1411.1784. http:\/\/arxiv.org\/abs\/1411.1784"},{"key":"e_1_3_2_1_32_1","unstructured":"Takeru Miyato Toshiki Kataoka Masanori Koyama and Yuichi Yoshida. 2018. Spectral normalization for generative adversarial networks. arXiv preprint arXiv:1802.05957.  Takeru Miyato Toshiki Kataoka Masanori Koyama and Yuichi Yoshida. 2018. Spectral normalization for generative adversarial networks. arXiv preprint arXiv:1802.05957."},{"key":"e_1_3_2_1_33_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.enbuild.2015.02.053"},{"key":"e_1_3_2_1_34_1","volume-title":"International conference on machine learning. PMLR, 2642\u20132651","author":"Odena Augustus","year":"2017","unstructured":"Augustus Odena , Christopher Olah , and Jonathon Shlens . 2017 . Conditional image synthesis with auxiliary classifier GANs . In International conference on machine learning. PMLR, 2642\u20132651 . Augustus Odena, Christopher Olah, and Jonathon Shlens. 2017. Conditional image synthesis with auxiliary classifier GANs. In International conference on machine learning. PMLR, 2642\u20132651."},{"key":"e_1_3_2_1_35_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICC45855.2022.9839249"},{"key":"e_1_3_2_1_36_1","doi-asserted-by":"publisher","DOI":"10.5555\/3157096.3157346"},{"key":"e_1_3_2_1_37_1","doi-asserted-by":"publisher","DOI":"10.1186\/s40537-019-0197-0"},{"key":"e_1_3_2_1_38_1","volume-title":"Proceedings of the 28th International Conference on Neural Information Processing Systems -","volume":"2","author":"Sohn Kihyuk","year":"2015","unstructured":"Kihyuk Sohn , Xinchen Yan , and Honglak Lee . 2015 . Learning Structured Output Representation Using Deep Conditional Generative Models . In Proceedings of the 28th International Conference on Neural Information Processing Systems - Volume 2 (Montreal, Canada) (NIPS\u201915). MIT Press, Cambridge, MA, USA, 3483\u20133491. Kihyuk Sohn, Xinchen Yan, and Honglak Lee. 2015. Learning Structured Output Representation Using Deep Conditional Generative Models. In Proceedings of the 28th International Conference on Neural Information Processing Systems - Volume 2 (Montreal, Canada) (NIPS\u201915). MIT Press, Cambridge, MA, USA, 3483\u20133491."},{"key":"e_1_3_2_1_39_1","volume-title":"International Conference on Artificial Intelligence and Statistics. PMLR, 2611\u20132619","author":"Subbaswamy Adarsh","year":"2021","unstructured":"Adarsh Subbaswamy , Roy Adams , and Suchi Saria . 2021 . Evaluating model robustness and stability to dataset shift . In International Conference on Artificial Intelligence and Statistics. PMLR, 2611\u20132619 . Adarsh Subbaswamy, Roy Adams, and Suchi Saria. 2021. Evaluating model robustness and stability to dataset shift. In International Conference on Artificial Intelligence and Statistics. PMLR, 2611\u20132619."},{"key":"e_1_3_2_1_40_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.enbuild.2020.110022"},{"key":"e_1_3_2_1_41_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.enbuild.2019.01.034"},{"key":"e_1_3_2_1_42_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.epsr.2022.108603"},{"key":"e_1_3_2_1_43_1","doi-asserted-by":"publisher","DOI":"10.1109\/TSG.2018.2818167"},{"key":"e_1_3_2_1_44_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.enbuild.2020.110299"},{"key":"e_1_3_2_1_45_1","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2021.3131185"},{"key":"e_1_3_2_1_46_1","unstructured":"Yaniv Yacoby Weiwei Pan and Finale Doshi-Velez. 2020. Failure modes of variational autoencoders and their effects on downstream tasks. arXiv preprint arXiv:2007.07124.  Yaniv Yacoby Weiwei Pan and Finale Doshi-Velez. 2020. Failure modes of variational autoencoders and their effects on downstream tasks. arXiv preprint arXiv:2007.07124."},{"key":"e_1_3_2_1_47_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.enbuild.2019.109689"},{"key":"e_1_3_2_1_48_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.enbuild.2022.112247"},{"key":"e_1_3_2_1_49_1","volume-title":"Advances in Neural Information Processing Systems, Vol.\u00a032. Curran Associates","author":"Yoon Jinsung","unstructured":"Jinsung Yoon , Daniel Jarrett , and Mihaela van\u00a0der Schaar . 2019. Time-series Generative Adversarial Networks . In Advances in Neural Information Processing Systems, Vol.\u00a032. Curran Associates , Inc . Jinsung Yoon, Daniel Jarrett, and Mihaela van\u00a0der Schaar. 2019. Time-series Generative Adversarial Networks. In Advances in Neural Information Processing Systems, Vol.\u00a032. Curran Associates, Inc."},{"key":"e_1_3_2_1_50_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.egyai.2022.100223"}],"event":{"name":"e-Energy '23: The 14th ACM International Conference on Future Energy Systems","location":"Orlando FL USA","acronym":"e-Energy '23","sponsor":["SIGCHI ACM Special Interest Group on Computer-Human Interaction"]},"container-title":["Companion Proceedings of the 14th ACM International Conference on Future Energy Systems"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3599733.3600260","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3599733.3600260","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T16:47:12Z","timestamp":1750178832000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3599733.3600260"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,6,20]]},"references-count":50,"alternative-id":["10.1145\/3599733.3600260","10.1145\/3599733"],"URL":"https:\/\/doi.org\/10.1145\/3599733.3600260","relation":{},"subject":[],"published":{"date-parts":[[2023,6,20]]},"assertion":[{"value":"2023-06-28","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}