{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,25]],"date-time":"2026-03-25T16:03:38Z","timestamp":1774454618284,"version":"3.50.1"},"publisher-location":"New York, NY, USA","reference-count":32,"publisher":"ACM","license":[{"start":{"date-parts":[[2022,11,9]],"date-time":"2022-11-09T00:00:00Z","timestamp":1667952000000},"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":[[2022,11,9]]},"DOI":"10.1145\/3563357.3564080","type":"proceedings-article","created":{"date-parts":[[2022,12,8]],"date-time":"2022-12-08T13:31:36Z","timestamp":1670506296000},"page":"208-217","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":12,"title":["On the use of conditional TimeGAN to enhance the robustness of a reinforcement learning agent in the building domain"],"prefix":"10.1145","author":[{"given":"Marta","family":"Fochesato","sequence":"first","affiliation":[{"name":"ETH Z\u00fcrich, Z\u00fcrich, Switzerland"}]},{"given":"Fazel","family":"Khayatian","sequence":"additional","affiliation":[{"name":"Urban Energy System Laboratory, D\u00fcbendorf, Switzerland"}]},{"given":"Doris Fonseca","family":"Lima","sequence":"additional","affiliation":[{"name":"ETH Z\u00fcrich, Z\u00fcrich, Switzerland"}]},{"given":"Zoltan","family":"Nagy","sequence":"additional","affiliation":[{"name":"University of Texas at Austin"}]}],"member":"320","published-online":{"date-parts":[[2022,12,8]]},"reference":[{"key":"e_1_3_2_1_1_1","doi-asserted-by":"publisher","unstructured":"2021. Synthetic building performance data. 10.5281\/zenodo.4696060","DOI":"10.5281\/zenodo.4696060"},{"key":"e_1_3_2_1_2_1","unstructured":"2021. Synthetic Energy and Environment Replicator. https:\/\/github.com\/Khayatian\/seer."},{"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","unstructured":"Shane Barratt and Rishi Sharma. 2018. A Note on the Inception Score. 10.48550\/ARXIV.1801.01973","DOI":"10.48550\/ARXIV.1801.01973"},{"key":"e_1_3_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.enbuild.2020.109792"},{"key":"e_1_3_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.enbuild.2018.06.029"},{"key":"e_1_3_2_1_7_1","doi-asserted-by":"publisher","DOI":"10.1016\/S0378-7788(00)00114-6"},{"key":"e_1_3_2_1_8_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jedc.2005.08.008"},{"key":"e_1_3_2_1_9_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.apenergy.2019.02.052"},{"key":"e_1_3_2_1_10_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.enbuild.2012.08.002"},{"key":"e_1_3_2_1_11_1","volume-title":"Weinberger (Eds.)","volume":"27","author":"Goodfellow Ian","year":"2014","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, Z. Ghahramani, M. Welling, C. Cortes, N. Lawrence, and K.Q. Weinberger (Eds.), Vol. 27. Curran Associates, Inc. https:\/\/proceedings.neurips.cc\/paper\/2014\/file\/5ca3e9b122f61f8f06494c97b1afccf3-Paper.pdf"},{"key":"e_1_3_2_1_12_1","volume-title":"GAN-based Model for Residential Load Generation Considering Typical Consumption Patterns. In 2019 IEEE Power & Energy Society Innovative Smart Grid Technologies Conference (ISGT). 1--5.","author":"Gu Yuxuan","year":"2019","unstructured":"Yuxuan Gu, Qixin Chen, Kai Liu, Le Xie, and Chongqing Kang. 2019. GAN-based Model for Residential Load Generation Considering Typical Consumption Patterns. In 2019 IEEE Power & Energy Society Innovative Smart Grid Technologies Conference (ISGT). 1--5."},{"key":"e_1_3_2_1_13_1","volume-title":"A Review on Generative Adversarial Networks: Algorithms, Theory, and Applications. CoRR abs\/2001.06937","author":"Gui Jie","year":"2020","unstructured":"Jie Gui, Zhenan Sun, Yonggang Wen, Dacheng Tao, and Jieping Ye. 2020. A Review on Generative Adversarial Networks: Algorithms, Theory, and Applications. CoRR abs\/2001.06937 (2020). https:\/\/arxiv.org\/abs\/2001.06937"},{"key":"e_1_3_2_1_14_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.enbuild.2020.109831"},{"key":"e_1_3_2_1_15_1","doi-asserted-by":"publisher","DOI":"10.1007\/s12273-020-0661-y"},{"key":"e_1_3_2_1_16_1","doi-asserted-by":"publisher","DOI":"10.1145\/3127023"},{"key":"e_1_3_2_1_17_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.enbuild.2021.111334"},{"key":"e_1_3_2_1_18_1","series-title":"and Time Series","volume-title":"Convolutional Networks for Images, Speech","author":"LeCun Yann","unstructured":"Yann LeCun and Yoshua Bengio. 1998. Convolutional Networks for Images, Speech, and Time Series. MIT Press, Cambridge, MA, USA, 255--258."},{"key":"e_1_3_2_1_19_1","doi-asserted-by":"publisher","DOI":"10.1190\/tle37080578.1"},{"key":"e_1_3_2_1_20_1","doi-asserted-by":"publisher","unstructured":"Maryam MeshkinKiya and Riccardo Paolini. 2020. Preparing Weather Data for Real-Time Building Energy Simulation. 10.48550\/ARXIV.2011.09733","DOI":"10.48550\/ARXIV.2011.09733"},{"key":"e_1_3_2_1_21_1","unstructured":"Mehdi Mirza and Simon Osindero. 2014. Conditional Generative Adversarial Nets. https:\/\/arxiv.org\/abs\/1411.1784"},{"key":"e_1_3_2_1_22_1","volume-title":"C-RNN-GAN: Continuous recurrent neural networks with adversarial training. CoRR abs\/1611.09904","author":"Mogren Olof","year":"2016","unstructured":"Olof Mogren. 2016. C-RNN-GAN: Continuous recurrent neural networks with adversarial training. CoRR abs\/1611.09904 (2016). http:\/\/arxiv.org\/abs\/1611.09904"},{"key":"e_1_3_2_1_23_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICTAI.2019.00271"},{"key":"e_1_3_2_1_24_1","volume-title":"On GANs and GMMs. CoRR abs\/1805.12462","author":"Richardson Eitan","year":"2018","unstructured":"Eitan Richardson and Yair Weiss. 2018. On GANs and GMMs. CoRR abs\/1805.12462 (2018). arXiv:1805.12462 http:\/\/arxiv.org\/abs\/1805.12462"},{"key":"e_1_3_2_1_25_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.apenergy.2020.115981"},{"key":"e_1_3_2_1_26_1","volume-title":"Proceedings of The 24th International Conference on Artificial Intelligence and Statistics (Proceedings of Machine Learning Research","volume":"2619","author":"Subbaswamy Adarsh","year":"2021","unstructured":"Adarsh Subbaswamy, Roy Adams, and Suchi Saria. 2021. Evaluating Model Robustness and Stability to Dataset Shift. In Proceedings of The 24th International Conference on Artificial Intelligence and Statistics (Proceedings of Machine Learning Research, Vol. 130), Arindam Banerjee and Kenji Fukumizu (Eds.). PMLR, 2611--2619."},{"key":"e_1_3_2_1_27_1","doi-asserted-by":"publisher","DOI":"10.1137\/1118101"},{"key":"e_1_3_2_1_28_1","doi-asserted-by":"publisher","DOI":"10.1145\/3408308.3427604"},{"key":"e_1_3_2_1_29_1","doi-asserted-by":"publisher","DOI":"10.1145\/3360322.3360998"},{"key":"e_1_3_2_1_30_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.enbuild.2020.110299"},{"key":"e_1_3_2_1_31_1","doi-asserted-by":"publisher","DOI":"10.1162\/neco.1989.1.2.270"},{"key":"e_1_3_2_1_32_1","volume-title":"Advances in Neural Information Processing Systems","author":"Yoon Jinsung","unstructured":"Jinsung Yoon, Daniel Jarrett, and Mihaela van der Schaar. 2019. Time-series Generative Adversarial Networks. In Advances in Neural Information Processing Systems, Vol. 32. Curran Associates, Inc."}],"event":{"name":"BuildSys '22: The 9th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation","location":"Boston Massachusetts","acronym":"BuildSys '22","sponsor":["SIGEnergy ACM Special Interest Group on Energy Systems and Informatics"]},"container-title":["Proceedings of the 9th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3563357.3564080","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3563357.3564080","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,8,22]],"date-time":"2025-08-22T07:26:13Z","timestamp":1755847573000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3563357.3564080"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,11,9]]},"references-count":32,"alternative-id":["10.1145\/3563357.3564080","10.1145\/3563357"],"URL":"https:\/\/doi.org\/10.1145\/3563357.3564080","relation":{},"subject":[],"published":{"date-parts":[[2022,11,9]]},"assertion":[{"value":"2022-12-08","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}