{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,4]],"date-time":"2025-11-04T23:29:01Z","timestamp":1762298941717,"version":"build-2065373602"},"publisher-location":"New York, NY, USA","reference-count":46,"publisher":"ACM","license":[{"start":{"date-parts":[[2024,10,29]],"date-time":"2024-10-29T00:00:00Z","timestamp":1730160000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"RGC GRF","award":["15200321, 15201322, 15230624"],"award-info":[{"award-number":["15200321, 15201322, 15230624"]}]},{"name":"RGC-CRF","award":["C5018-20G"],"award-info":[{"award-number":["C5018-20G"]}]},{"name":"ITC","award":["ITF-ITS\/056\/22MX"],"award-info":[{"award-number":["ITF-ITS\/056\/22MX"]}]},{"name":"PolyU","award":["1-CDKK, G-SAC8"],"award-info":[{"award-number":["1-CDKK, G-SAC8"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2024,10,29]]},"DOI":"10.1145\/3671127.3698190","type":"proceedings-article","created":{"date-parts":[[2024,10,30]],"date-time":"2024-10-30T00:30:41Z","timestamp":1730248241000},"page":"143-153","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["AugPlug: An Automated Data Augmentation Model to Enhance Online Building Load Forecasting"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9822-3346","authenticated-orcid":false,"given":"Yang","family":"Deng","sequence":"first","affiliation":[{"name":"Hong Kong Polytechnic Univ., Hong Kong"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0005-6109-3049","authenticated-orcid":false,"given":"Rui","family":"Liang","sequence":"additional","affiliation":[{"name":"Hong Kong Polytechnic Univ., Hong Kong"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0009-5564-4624","authenticated-orcid":false,"given":"Yaohui","family":"Liu","sequence":"additional","affiliation":[{"name":"Hong Kong Polytechnic Univ., Hong Kong"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0002-3541-7984","authenticated-orcid":false,"given":"Jiaqi","family":"Fan","sequence":"additional","affiliation":[{"name":"Hong Kong Polytechnic Univ., Hong Kong"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0921-2726","authenticated-orcid":false,"given":"Dan","family":"Wang","sequence":"additional","affiliation":[{"name":"Hong Kong Polytechnic Univ., Hong Kong"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2024,10,29]]},"reference":[{"doi-asserted-by":"publisher","key":"e_1_3_2_1_1_1","DOI":"10.1109\/TPWRS.2021.3050837"},{"doi-asserted-by":"crossref","unstructured":"G. Baasch G. Rousseau et al. 2021. A Conditional Generative adversarial Network for energy use in multiple buildings using scarce data. Energy and AI (2021).","key":"e_1_3_2_1_2_1","DOI":"10.1016\/j.egyai.2021.100087"},{"unstructured":"S. Bai J. Kolter et al. 2018. An empirical evaluation of generic convolutional and recurrent networks for sequence modeling. arXiv preprint arXiv:1803.01271 (2018).","key":"e_1_3_2_1_3_1"},{"unstructured":"Lucas Baier. 2021. Concept Drift Handling in Information Systems: Preserving the Validity of Deployed Machine Learning Models. (2021).","key":"e_1_3_2_1_4_1"},{"doi-asserted-by":"crossref","unstructured":"A. A. Bencz\u00far L. Kocsis et al. 2018. Online machine learning in big data streams. arXiv preprint arXiv:1802.05872 (2018).","key":"e_1_3_2_1_5_1","DOI":"10.1007\/978-3-319-63962-8_326-1"},{"doi-asserted-by":"publisher","key":"e_1_3_2_1_6_1","DOI":"10.1016\/j.segan.2022.100873"},{"doi-asserted-by":"publisher","key":"e_1_3_2_1_7_1","DOI":"10.1016\/j.enbuild.2018.06.029"},{"key":"e_1_3_2_1_8_1","volume-title":"A Survey of Automated Data Augmentation for Image Classification: Learning to Compose, Mix, and Generate","author":"Cheung Tsz-Him","year":"2023","unstructured":"Tsz-Him Cheung and Dit-Yan Yeung. 2023. A Survey of Automated Data Augmentation for Image Classification: Learning to Compose, Mix, and Generate. IEEE Transactions on Neural Networks and Learning Systems (2023)."},{"doi-asserted-by":"publisher","key":"e_1_3_2_1_9_1","DOI":"10.1016\/j.apenergy.2020.115410"},{"doi-asserted-by":"publisher","key":"e_1_3_2_1_10_1","DOI":"10.1109\/CVPR.2019.00020"},{"doi-asserted-by":"publisher","key":"e_1_3_2_1_11_1","DOI":"10.1109\/CVPRW50498.2020.00359"},{"doi-asserted-by":"publisher","key":"e_1_3_2_1_12_1","DOI":"10.1145\/3538637.3538841"},{"doi-asserted-by":"publisher","key":"e_1_3_2_1_13_1","DOI":"10.1145\/3600100.3623727"},{"doi-asserted-by":"publisher","key":"e_1_3_2_1_14_1","DOI":"10.1145\/3632775.3661979"},{"doi-asserted-by":"crossref","unstructured":"C. Fan M. Chen R. Tang and J. Wang. 2022. A novel deep generative modeling-based data augmentation strategy for improving short-term building energy predictions. In Building Simulation.","key":"e_1_3_2_1_15_1","DOI":"10.1007\/s12273-021-0807-6"},{"doi-asserted-by":"crossref","unstructured":"C. Fan F. Xiao and Y. Zhao. 2017. A short-term building cooling load prediction method using deep learning algorithms. Applied energy (2017).","key":"e_1_3_2_1_16_1","DOI":"10.1016\/j.apenergy.2017.03.064"},{"doi-asserted-by":"publisher","key":"e_1_3_2_1_17_1","DOI":"10.1016\/j.apenergy.2022.120063"},{"doi-asserted-by":"publisher","key":"e_1_3_2_1_18_1","DOI":"10.1016\/j.apenergy.2020.116177"},{"doi-asserted-by":"publisher","key":"e_1_3_2_1_19_1","DOI":"10.1145\/3563357.3564080"},{"volume-title":"Proceedings of the Tenth ACM International Conference on Future Energy Systems. 403--405","author":"Gugulothu N.","unstructured":"N. Gugulothu and E. Subramanian. 2019. Load Forecasting in Energy Markets: An Approach Using Sparse Neural Networks. In Proceedings of the Tenth ACM International Conference on Future Energy Systems. 403--405.","key":"e_1_3_2_1_20_1"},{"key":"e_1_3_2_1_21_1","volume-title":"AutoML: A survey of the state-of-the-art. Knowledge-based systems 212","author":"He Xin","year":"2021","unstructured":"Xin He, Kaiyong Zhao, and Xiaowen Chu. 2021. AutoML: A survey of the state-of-the-art. Knowledge-based systems 212 (2021), 106622."},{"doi-asserted-by":"publisher","key":"e_1_3_2_1_22_1","DOI":"10.1145\/3538637.3539759"},{"key":"e_1_3_2_1_23_1","volume-title":"International conference on machine learning. PMLR, 2731--2741","author":"Ho D.","year":"2019","unstructured":"D. Ho, E. Liang, et al. 2019. Population based augmentation: Efficient learning of augmentation policy schedules. In International conference on machine learning. PMLR, 2731--2741."},{"doi-asserted-by":"publisher","key":"e_1_3_2_1_24_1","DOI":"10.1109\/ICCV51070.2023.00165"},{"doi-asserted-by":"publisher","key":"e_1_3_2_1_25_1","DOI":"10.1109\/ACCESS.2021.3095420"},{"doi-asserted-by":"crossref","unstructured":"Y. Ji G. Geng et al. 2021. Enhancing model adaptability using concept drift detection for short-term load forecast. In 2021 IEEE\/IAS Industrial and Commercial Power System Asia (I&CPS Asia). IEEE 464--469.","key":"e_1_3_2_1_26_1","DOI":"10.1109\/ICPSAsia52756.2021.9621522"},{"doi-asserted-by":"publisher","key":"e_1_3_2_1_27_1","DOI":"10.1016\/j.ijepes.2021.107707"},{"doi-asserted-by":"crossref","unstructured":"A. Li F. Xiao et al. 2021. Attention-based interpretable neural network for building cooling load prediction. Applied Energy (2021).","key":"e_1_3_2_1_28_1","DOI":"10.1016\/j.apenergy.2021.117238"},{"doi-asserted-by":"publisher","key":"e_1_3_2_1_29_1","DOI":"10.1016\/j.ijepes.2021.107627"},{"doi-asserted-by":"publisher","key":"e_1_3_2_1_30_1","DOI":"10.1609\/aaai.v36i4.20327"},{"unstructured":"S. Lim I. Kim et al. 2019. Fast autoaugment. Advances in Neural Information Processing Systems 32 (2019).","key":"e_1_3_2_1_31_1"},{"doi-asserted-by":"publisher","key":"e_1_3_2_1_32_1","DOI":"10.3390\/su14105857"},{"doi-asserted-by":"crossref","unstructured":"C. Miller A. Kathirgamanathan et al. 2020. The building data genome project 2 energy meter data from the ASHRAE great energy predictor III competition. Scientific data (2020).","key":"e_1_3_2_1_33_1","DOI":"10.1038\/s41597-020-00712-x"},{"doi-asserted-by":"publisher","key":"e_1_3_2_1_34_1","DOI":"10.1016\/j.jobe.2021.103851"},{"doi-asserted-by":"publisher","key":"e_1_3_2_1_35_1","DOI":"10.1109\/ICCV48922.2021.00081"},{"key":"e_1_3_2_1_36_1","volume-title":"Data Augmentation Policy Search for Long-Term Forecasting. arXiv preprint arXiv:2405.00319","author":"Nochumsohn Liran","year":"2024","unstructured":"Liran Nochumsohn and Omri Azencot. 2024. Data Augmentation Policy Search for Long-Term Forecasting. arXiv preprint arXiv:2405.00319 (2024)."},{"doi-asserted-by":"publisher","key":"e_1_3_2_1_37_1","DOI":"10.1063\/1.5094494"},{"doi-asserted-by":"publisher","key":"e_1_3_2_1_38_1","DOI":"10.1016\/j.egyr.2023.08.075"},{"doi-asserted-by":"publisher","key":"e_1_3_2_1_39_1","DOI":"10.3390\/s20123524"},{"doi-asserted-by":"crossref","unstructured":"S. Ren J. Zhang et al. 2021. Text autoaugment: Learning compositional augmentation policy for text classification. arXiv preprint arXiv:2109.00523 (2021).","key":"e_1_3_2_1_40_1","DOI":"10.18653\/v1\/2021.emnlp-main.711"},{"unstructured":"A. Salem A. Bhattacharya et al. 2020. {Updates-Leak}: Data set inference and reconstruction attacks in online learning. In 29th USENIX security symposium (USENIX Security 20). 1291--1308.","key":"e_1_3_2_1_41_1"},{"doi-asserted-by":"publisher","key":"e_1_3_2_1_42_1","DOI":"10.36001\/phmconf.2020.v12i1.1319"},{"doi-asserted-by":"publisher","key":"e_1_3_2_1_43_1","DOI":"10.1016\/j.buildenv.2020.106698"},{"unstructured":"J. Yoon D. Jarrett et al. 2019. Time-series generative adversarial networks. Advances in neural information processing systems (2019).","key":"e_1_3_2_1_44_1"},{"doi-asserted-by":"publisher","key":"e_1_3_2_1_45_1","DOI":"10.1145\/3208903.3208913"},{"doi-asserted-by":"publisher","key":"e_1_3_2_1_46_1","DOI":"10.1016\/j.enbuild.2022.112098"}],"event":{"acronym":"BuildSys '24","name":"BuildSys '24: The 11th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation","location":"Hangzhou China"},"container-title":["Proceedings of the 11th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3671127.3698190","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3671127.3698190","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,11,4]],"date-time":"2025-11-04T23:25:18Z","timestamp":1762298718000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3671127.3698190"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,10,29]]},"references-count":46,"alternative-id":["10.1145\/3671127.3698190","10.1145\/3671127"],"URL":"https:\/\/doi.org\/10.1145\/3671127.3698190","relation":{},"subject":[],"published":{"date-parts":[[2024,10,29]]},"assertion":[{"value":"2024-10-29","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}