{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,24]],"date-time":"2026-05-24T00:09:34Z","timestamp":1779581374907,"version":"3.53.1"},"reference-count":73,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"funder":[{"DOI":"10.13039\/501100000923","name":"Australian Research Council","doi-asserted-by":"publisher","award":["DP220103881"],"award-info":[{"award-number":["DP220103881"]}],"id":[{"id":"10.13039\/501100000923","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62072060"],"award-info":[{"award-number":["62072060"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["72074036"],"award-info":[{"award-number":["72074036"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Applied Soft Computing"],"published-print":{"date-parts":[[2026,6]]},"DOI":"10.1016\/j.asoc.2026.115110","type":"journal-article","created":{"date-parts":[[2026,3,25]],"date-time":"2026-03-25T19:00:49Z","timestamp":1774465249000},"page":"115110","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"C","title":["Generalizable zero-shot home energy management via representation learning and behavioral cloning"],"prefix":"10.1016","volume":"196","author":[{"given":"Xiao","family":"Du","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fengji","family":"Luo","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Juntao","family":"Hu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wei","family":"Zhou","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6561-560X","authenticated-orcid":false,"given":"Junhao","family":"Wen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"key":"10.1016\/j.asoc.2026.115110_bib0005","article-title":"Global status report for buildings and construction\u2014towards a zero-emissions, efficient and resilient buildings and construction sector","volume":"390","author":"Abergel","year":"2019","journal-title":"Env., Programme U. N. Env., Programme"},{"issue":"6","key":"10.1016\/j.asoc.2026.115110_bib0015","doi-asserted-by":"crossref","first-page":"8329","DOI":"10.1109\/TIA.2025.3576745","article-title":"Cloud-based real-time model predictive control for a multi-carrier and multi-objective home energy management system","volume":"61","author":"Kazemi","year":"2025","journal-title":"IEEE Trans. Ind. Appl."},{"issue":"4","key":"10.1016\/j.asoc.2026.115110_bib0020","doi-asserted-by":"crossref","first-page":"2358","DOI":"10.1109\/TSTE.2025.3551682","article-title":"A multi-level home energy management system (hems) for dc-microgrids","volume":"16","author":"Lin","year":"2025","journal-title":"IEEE Trans. Sustain. Energy"},{"key":"10.1016\/j.asoc.2026.115110_bib0025","doi-asserted-by":"crossref","first-page":"30","DOI":"10.1016\/j.rser.2016.03.047","article-title":"Smart home energy management systems: concept, configurations, and scheduling strategies","volume":"61","author":"Zhou","year":"2016","journal-title":"Renew. Sustain. Energy Rev."},{"key":"10.1016\/j.asoc.2026.115110_bib0030","series-title":"Reinforcement Learning: an Introduction","author":"Sutton","year":"2018"},{"key":"10.1016\/j.asoc.2026.115110_bib0035","doi-asserted-by":"crossref","first-page":"12046","DOI":"10.1109\/JIOT.2021.3078462","article-title":"A review of deep reinforcement learning for smart building energy management","volume":"8","author":"Yu","year":"2021","journal-title":"IEEE Internet Things J."},{"key":"10.1016\/j.asoc.2026.115110_bib0040","series-title":"Proceedings of the 25th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval","first-page":"253","article-title":"Methods and metrics for cold-start recommendations","author":"Schein","year":"2002"},{"key":"10.1016\/j.asoc.2026.115110_bib0045","doi-asserted-by":"crossref","DOI":"10.1016\/j.buildenv.2023.110435","article-title":"Ten questions concerning reinforcement learning for building energy management","volume":"241","author":"Nagy","year":"2023","journal-title":"Build. Environ."},{"key":"10.1016\/j.asoc.2026.115110_bib0050","doi-asserted-by":"crossref","first-page":"4","DOI":"10.1109\/TSG.2019.2915679","article-title":"Optimal home energy management system with demand charge tariff and appliance operational dependencies","volume":"11","author":"Luo","year":"2019","journal-title":"IEEE Trans. Smart Grid"},{"key":"10.1016\/j.asoc.2026.115110_bib0055","doi-asserted-by":"crossref","first-page":"116","DOI":"10.1109\/TII.2018.2871159","article-title":"A multistage home energy management system with residential photovoltaic penetration","volume":"15","author":"Luo","year":"2018","journal-title":"IEEE Trans. Ind. Informat."},{"key":"10.1016\/j.asoc.2026.115110_bib0060","article-title":"Home energy management system for enhancing grid resiliency in post-disaster recovery period using electric vehicle","volume":"34","author":"Candan","year":"2023","journal-title":"Sustain. Energy Grids Netw."},{"key":"10.1016\/j.asoc.2026.115110_bib0065","doi-asserted-by":"crossref","first-page":"324","DOI":"10.1109\/TSG.2014.2349352","article-title":"Optimal smart home energy management considering energy saving and a comfortable lifestyle","volume":"6","author":"Anvari-Moghaddam","year":"2014","journal-title":"IEEE Trans. Smart Grid"},{"key":"10.1016\/j.asoc.2026.115110_bib0070","article-title":"Optimizing home energy management systems: a mixed integer linear programming model considering battery cycle degradation","volume":"329","author":"de Lima","year":"2025","journal-title":"Energy Build."},{"key":"10.1016\/j.asoc.2026.115110_bib0075","doi-asserted-by":"crossref","first-page":"233","DOI":"10.1016\/j.solener.2019.04.039","article-title":"Cash flow prediction optimization using dynamic programming for a residential photovoltaic system with storage battery","volume":"186","author":"Bernasconi","year":"2019","journal-title":"Solar Energy"},{"key":"10.1016\/j.asoc.2026.115110_bib0080","doi-asserted-by":"crossref","first-page":"407","DOI":"10.1016\/j.asoc.2016.05.005","article-title":"Predictive control of a building hybrid heating system for energy cost reduction","volume":"46","author":"Khanmirza","year":"2016","journal-title":"Appl. Soft Comput."},{"key":"10.1016\/j.asoc.2026.115110_bib0085","article-title":"Two-level hierarchical model predictive control with an optimised cost function for energy management in building microgrids","volume":"285","author":"Yamashita","year":"2021","journal-title":"Appl. Energy"},{"key":"10.1016\/j.asoc.2026.115110_bib0090","doi-asserted-by":"crossref","DOI":"10.1016\/j.energy.2020.118568","article-title":"Optimal self-scheduling of home energy management system in the presence of photovoltaic power generation and batteries","volume":"210","author":"Javadi","year":"2020","journal-title":"Energy"},{"key":"10.1016\/j.asoc.2026.115110_bib0095","series-title":"Building Energy Management Systems and Techniques: Principles, Methods, and Modelling","author":"Luo","year":"2024"},{"key":"10.1016\/j.asoc.2026.115110_bib0100","doi-asserted-by":"crossref","DOI":"10.1016\/j.energy.2025.134420","article-title":"Deep reinforcement learning-based plug-in electric vehicle charging\/discharging scheduling in a home energy management system","volume":"316","author":"Mansour","year":"2025","journal-title":"Energy"},{"key":"10.1016\/j.asoc.2026.115110_bib0105","doi-asserted-by":"crossref","first-page":"932","DOI":"10.1109\/TSG.2012.2226065","article-title":"Uncertainty-aware household appliance scheduling considering dynamic electricity pricing in smart home","volume":"4","author":"Chen","year":"2013","journal-title":"IEEE Trans. Smart Grid"},{"key":"10.1016\/j.asoc.2026.115110_bib0110","series-title":"2020 39th Chinese Control Conference (CCC)","first-page":"1490","article-title":"Energy management of a residential consumer with uncertain renewable generation: a robust dual dynamic programming approach","author":"Liu","year":"2020"},{"key":"10.1016\/j.asoc.2026.115110_bib0115","doi-asserted-by":"crossref","DOI":"10.1016\/j.epsr.2024.111055","article-title":"Data-driven real-time home energy management system based on adaptive dynamic programming","volume":"238","author":"Yuan","year":"2025","journal-title":"Electr. Power Syst. Res."},{"key":"10.1016\/j.asoc.2026.115110_bib0120","doi-asserted-by":"crossref","first-page":"239","DOI":"10.1109\/TSG.2022.3198401","article-title":"Drl-hems: deep reinforcement learning agent for demand response in home energy management systems considering customers and operators perspectives","volume":"14","author":"Amer","year":"2022","journal-title":"IEEE Trans. Smart Grid"},{"key":"10.1016\/j.asoc.2026.115110_bib0125","doi-asserted-by":"crossref","first-page":"4079","DOI":"10.1109\/TSG.2021.3088290","article-title":"Privacy preserving load control of residential microgrid via deep reinforcement learning","volume":"12","author":"Qin","year":"2021","journal-title":"IEEE Trans. Smart Grid"},{"key":"10.1016\/j.asoc.2026.115110_bib0130","doi-asserted-by":"crossref","first-page":"2751","DOI":"10.1109\/JIOT.2019.2957289","article-title":"Deep reinforcement learning for smart home energy management","volume":"7","author":"Yu","year":"2019","journal-title":"IEEE Internet Things J."},{"key":"10.1016\/j.asoc.2026.115110_bib0135","doi-asserted-by":"crossref","first-page":"529","DOI":"10.1038\/nature14236","article-title":"Human-level control through deep reinforcement learning","volume":"518","author":"Mnih","year":"2015","journal-title":"Nature"},{"issue":"14","key":"10.1016\/j.asoc.2026.115110_bib0140","doi-asserted-by":"crossref","first-page":"27003","DOI":"10.1109\/JIOT.2025.3562137","article-title":"Multi-agent drl-based demand response optimization for iot-based smart home energy management systems","volume":"12","author":"Abishu","year":"2025","journal-title":"IEEE Internet Things J."},{"key":"10.1016\/j.asoc.2026.115110_bib0145","doi-asserted-by":"crossref","DOI":"10.1016\/j.energy.2022.125679","article-title":"Cross temporal-spatial transferability investigation of deep reinforcement learning control strategy in the building HVAC system level","volume":"263","author":"Fang","year":"2023","journal-title":"Energy"},{"key":"10.1016\/j.asoc.2026.115110_bib0150","series-title":"Proceedings of the 1st International Workshop on Reinforcement Learning for Energy Management in Buildings & Cities, RLEM\u201920","first-page":"43","article-title":"Transferable reinforcement learning for smart homes","author":"Zhang","year":"2020"},{"key":"10.1016\/j.asoc.2026.115110_bib0155","doi-asserted-by":"crossref","first-page":"4060","DOI":"10.1109\/JSEN.2022.3218840","article-title":"A transfer reinforcement learning framework for smart home energy management systems","volume":"23","author":"Khan","year":"2022","journal-title":"IEEE Sens. J."},{"key":"10.1016\/j.asoc.2026.115110_bib0160","series-title":"Building Simulation","first-page":"739","article-title":"An innovative heterogeneous transfer learning framework to enhance the scalability of deep reinforcement learning controllers in buildings with integrated energy systems","volume":"vol. 17","author":"Coraci","year":"2024"},{"key":"10.1016\/j.asoc.2026.115110_bib0165","doi-asserted-by":"crossref","DOI":"10.1016\/j.apenergy.2022.120598","article-title":"Online transfer learning strategy for enhancing the scalability and deployment of deep reinforcement learning control in smart buildings","volume":"333","author":"Coraci","year":"2023","journal-title":"Appl. Energy"},{"key":"10.1016\/j.asoc.2026.115110_bib0170","doi-asserted-by":"crossref","DOI":"10.1016\/j.enbuild.2024.114696","article-title":"Multi-source transfer learning method for enhancing the deployment of deep reinforcement learning in multi-zone building HVAC control","volume":"322","author":"Hou","year":"2024","journal-title":"Energy Build."},{"key":"10.1016\/j.asoc.2026.115110_bib0175","series-title":"27th European Conference on Artificial Intelligence, ECAI 2024","first-page":"2814","article-title":"A meta-learning approach for multi-objective reinforcement learning in sustainable home energy management","author":"Lu","year":"2024"},{"key":"10.1016\/j.asoc.2026.115110_bib0180","doi-asserted-by":"crossref","first-page":"1685","DOI":"10.1109\/TCSI.2023.3240702","article-title":"Meta-reinforcement learning-based transferable scheduling strategy for energy management","volume":"70","author":"Xiong","year":"2023","journal-title":"IEEE Trans. Circuits Syst. I Regul. Pap."},{"key":"10.1016\/j.asoc.2026.115110_bib0185","author":"Zhang"},{"key":"10.1016\/j.asoc.2026.115110_bib0190","doi-asserted-by":"crossref","first-page":"12923","DOI":"10.1109\/JIOT.2023.3253693","article-title":"Evolutionary multi-agent deep meta reinforcement learning method for swarm intelligence energy management of isolated multi-area microgrid with internet of things","volume":"10","author":"Li","year":"2023","journal-title":"IEEE Internet Things J."},{"key":"10.1016\/j.asoc.2026.115110_bib0195","doi-asserted-by":"crossref","DOI":"10.1016\/j.apenergy.2023.121323","article-title":"Merlin: multi-agent offline and transfer learning for occupant-centric operation of grid-interactive communities","volume":"346","author":"Nweye","year":"2023","journal-title":"Appl. Energy"},{"key":"10.1016\/j.asoc.2026.115110_bib0200","first-page":"1","article-title":"Offline deep reinforcement learning-based home energy management systems with heterogeneous EV charging load models","author":"Xiong","year":"2025","journal-title":"IEEE Trans. Circuits Syst. I Regul. Pap."},{"key":"10.1016\/j.asoc.2026.115110_bib0205","first-page":"4051","article-title":"A review of generalized zero-shot learning methods","volume":"45","author":"Pourpanah","year":"2022","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"10.1016\/j.asoc.2026.115110_bib0210","doi-asserted-by":"crossref","first-page":"43","DOI":"10.1109\/JPROC.2020.3004555","article-title":"A comprehensive survey on transfer learning","volume":"109","author":"Zhuang","year":"2020","journal-title":"Proc. IEEE"},{"key":"10.1016\/j.asoc.2026.115110_bib0215","doi-asserted-by":"crossref","DOI":"10.1016\/j.adapen.2022.100084","article-title":"Transfer learning for smart buildings: a critical review of algorithms, applications, and future perspectives","volume":"5","author":"Pinto","year":"2022","journal-title":"Adv. Appl. Energy"},{"key":"10.1016\/j.asoc.2026.115110_bib0220","doi-asserted-by":"crossref","first-page":"81","DOI":"10.1109\/TIA.2022.3223347","article-title":"Deep transfer learning-enabled energy management strategy for smart home sensor networks","volume":"59","author":"Alibrahim","year":"2022","journal-title":"IEEE Trans. Ind. Appl."},{"key":"10.1016\/j.asoc.2026.115110_bib0225","doi-asserted-by":"crossref","first-page":"3535","DOI":"10.1109\/TSG.2022.3231592","article-title":"Privacy-preserving regulation capacity evaluation for HVAC systems in heterogeneous buildings based on federated learning and transfer learning","volume":"14","author":"Wang","year":"2022","journal-title":"IEEE Trans. Smart Grid"},{"key":"10.1016\/j.asoc.2026.115110_bib0230","doi-asserted-by":"crossref","DOI":"10.1016\/j.energy.2024.132394","article-title":"Enabling cross-type full-knowledge transferable energy management for hybrid electric vehicles via deep transfer reinforcement learning","volume":"305","author":"Huang","year":"2024","journal-title":"Energy"},{"key":"10.1016\/j.asoc.2026.115110_bib0235","series-title":"International Conference on Machine Learning","first-page":"1126","article-title":"Model-agnostic meta-learning for fast adaptation of deep networks","author":"Finn","year":"2017"},{"key":"10.1016\/j.asoc.2026.115110_bib0240","author":"Levine"},{"key":"10.1016\/j.asoc.2026.115110_bib0245","doi-asserted-by":"crossref","DOI":"10.1016\/j.apenergy.2023.121408","article-title":"An efficient energy management framework for residential communities based on demand pattern clustering","volume":"347","author":"Deng","year":"2023","journal-title":"Appl. Energy"},{"key":"10.1016\/j.asoc.2026.115110_bib0250","doi-asserted-by":"crossref","first-page":"99","DOI":"10.1016\/S0004-3702(98)00023-X","article-title":"Planning and acting in partially observable stochastic domains","volume":"101","author":"Kaelbling","year":"1998","journal-title":"Artif. Intell."},{"key":"10.1016\/j.asoc.2026.115110_bib0255","author":"Heess"},{"key":"10.1016\/j.asoc.2026.115110_bib0260","doi-asserted-by":"crossref","first-page":"2812","DOI":"10.1109\/TSG.2022.3158814","article-title":"Home Energy recommendation System (Hers): a deep reinforcement learning method based on residents\u2019 feedback and activity","volume":"13","author":"Shuvo","year":"2022","journal-title":"IEEE Trans. Smart Grid"},{"key":"10.1016\/j.asoc.2026.115110_bib0265","doi-asserted-by":"crossref","first-page":"445","DOI":"10.1016\/j.apenergy.2015.12.089","article-title":"High-resolution stochastic integrated thermal\u2013electrical domestic demand model","volume":"165","author":"McKenna","year":"2016","journal-title":"Appl. Energy"},{"key":"10.1016\/j.asoc.2026.115110_bib0270","doi-asserted-by":"crossref","first-page":"1878","DOI":"10.1016\/j.enbuild.2010.05.023","article-title":"Domestic electricity use: a high-resolution energy demand model","volume":"42","author":"Richardson","year":"2010","journal-title":"Energy Build."},{"key":"10.1016\/j.asoc.2026.115110_bib0275","doi-asserted-by":"crossref","first-page":"742","DOI":"10.1016\/j.enpol.2013.07.095","article-title":"Analysis and modeling of active occupancy of the residential sector in Spain: an indicator of residential electricity consumption","volume":"62","author":"L\u00f3pez-Rodr\u00edguez","year":"2013","journal-title":"Energy Policy"},{"key":"10.1016\/j.asoc.2026.115110_bib0290","series-title":"International Conference on Machine Learning","first-page":"1597","article-title":"A simple framework for contrastive learning of visual representations","author":"Chen","year":"2020"},{"key":"10.1016\/j.asoc.2026.115110_bib0295","author":"Chen"},{"key":"10.1016\/j.asoc.2026.115110_bib0300","author":"van den Oord"},{"key":"10.1016\/j.asoc.2026.115110_bib0305","series-title":"Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition","first-page":"1","article-title":"Going deeper with convolutions","author":"Szegedy","year":"2015"},{"key":"10.1016\/j.asoc.2026.115110_bib0310","series-title":"Proceedings of the 3rd International Conference on Learning Representations (ICLR)","article-title":"Adam: a method for stochastic optimization","author":"Kingma","year":"2015"},{"key":"10.1016\/j.asoc.2026.115110_bib0315","series-title":"Proceedings of the Fifth Berkeley Symposium on Mathematical Statistics and Probability","first-page":"281","article-title":"Some methods for classification and analysis of multivariate observations","volume":"vol. 1","author":"MacQueen","year":"1967"},{"key":"10.1016\/j.asoc.2026.115110_bib0320","doi-asserted-by":"crossref","first-page":"88","DOI":"10.1162\/neco.1991.3.1.88","article-title":"Efficient training of artificial neural networks for autonomous navigation","volume":"3","author":"Pomerleau","year":"1991","journal-title":"Neural Comput."},{"key":"10.1016\/j.asoc.2026.115110_bib0325","series-title":"Advances in Neural Information Processing Systems","article-title":"Is behavior cloning all you need? Understanding horizon in imitation learning","volume":"vol. 37","author":"Foster","year":"2024"},{"key":"10.1016\/j.asoc.2026.115110_bib0330","author":"Achiam"},{"key":"10.1016\/j.asoc.2026.115110_bib0335","series-title":"International Conference on Machine Learning","first-page":"1861","article-title":"Soft actor-critic: off-policy maximum entropy deep reinforcement learning with a stochastic actor","author":"Haarnoja","year":"2018"},{"key":"10.1016\/j.asoc.2026.115110_bib0340","doi-asserted-by":"crossref","first-page":"1518","DOI":"10.1109\/TII.2012.2230637","article-title":"Optimal home energy management under dynamic electrical and thermal constraints","volume":"9","author":"De Angelis","year":"2012","journal-title":"IEEE Trans. Ind. Informat."},{"key":"10.1016\/j.asoc.2026.115110_bib0345","series-title":"AAAI Workshops","article-title":"Comparison of clustering techniques for residential energy behavior using smart meter data","author":"Jin","year":"2017"},{"key":"10.1016\/j.asoc.2026.115110_bib0350","doi-asserted-by":"crossref","first-page":"3693","DOI":"10.1109\/TPWRS.2015.2493083","article-title":"Predicting consumer load profiles using commercial and open data","volume":"31","author":"Vercamer","year":"2015","journal-title":"IEEE Trans. Power Syst."},{"key":"10.1016\/j.asoc.2026.115110_bib0355","doi-asserted-by":"crossref","first-page":"205","DOI":"10.21105\/joss.00205","article-title":"Hdbscan: hierarchical density based clustering","volume":"2","author":"McInnes","year":"2017","journal-title":"J. Open Source Softw."},{"key":"10.1016\/j.asoc.2026.115110_bib0360","author":"Schulman"},{"key":"10.1016\/j.asoc.2026.115110_bib0365","series-title":"International Conference on Machine Learning","first-page":"1587","article-title":"Addressing function approximation error in actor-critic methods","author":"Fujimoto","year":"2018"},{"key":"10.1016\/j.asoc.2026.115110_bib0370","first-page":"1179","article-title":"Conservative q-learning for offline reinforcement learning","volume":"33","author":"Kumar","year":"2020","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"10.1016\/j.asoc.2026.115110_bib0375","first-page":"2579","article-title":"Visualizing data using t-SNE","volume":"9","author":"van der Maaten","year":"2008","journal-title":"J. Mach. Learn. Res."},{"key":"10.1016\/j.asoc.2026.115110_bib0380","article-title":"Privacy-preserving data analytics for smart decision-making energy systems in sustainable smart community","volume":"57","author":"Zhang","year":"2023","journal-title":"Sustain. Energy Technol. Assess."}],"container-title":["Applied Soft Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1568494626005582?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1568494626005582?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,5,23]],"date-time":"2026-05-23T23:36:21Z","timestamp":1779579381000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S1568494626005582"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,6]]},"references-count":73,"alternative-id":["S1568494626005582"],"URL":"https:\/\/doi.org\/10.1016\/j.asoc.2026.115110","relation":{},"ISSN":["1568-4946"],"issn-type":[{"value":"1568-4946","type":"print"}],"subject":[],"published":{"date-parts":[[2026,6]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Generalizable zero-shot home energy management via representation learning and behavioral cloning","name":"articletitle","label":"Article Title"},{"value":"Applied Soft Computing","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.asoc.2026.115110","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 Elsevier B.V. All rights are reserved, including those for text and data mining, AI training, and similar technologies.","name":"copyright","label":"Copyright"}],"article-number":"115110"}}