{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,24]],"date-time":"2025-12-24T08:20:39Z","timestamp":1766564439257,"version":"3.48.0"},"reference-count":44,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"1","license":[{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"name":"National Key Research and Development Program of China","award":["2023YFE0119800"],"award-info":[{"award-number":["2023YFE0119800"]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["52277095"],"award-info":[{"award-number":["52277095"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. Smart Grid"],"published-print":{"date-parts":[[2026,1]]},"DOI":"10.1109\/tsg.2025.3622228","type":"journal-article","created":{"date-parts":[[2025,10,16]],"date-time":"2025-10-16T17:37:53Z","timestamp":1760636273000},"page":"666-677","source":"Crossref","is-referenced-by-count":0,"title":["Robust Demand-Side Resource Management Scheme Against State-Altering Attacks"],"prefix":"10.1109","volume":"17","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9790-6439","authenticated-orcid":false,"given":"Yimeng","family":"Sun","sequence":"first","affiliation":[{"name":"State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources, North China Electric Power University, Beijing, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8621-0022","authenticated-orcid":false,"given":"Jiayao","family":"Chen","sequence":"additional","affiliation":[{"name":"State Grid Zhejiang Electric Power Company Ltd., Taizhou Power Supply Company, Taizhou, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7085-260X","authenticated-orcid":false,"given":"Zhaohao","family":"Ding","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources, North China Electric Power University, Beijing, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5790-5025","authenticated-orcid":false,"given":"Mingyang","family":"Sun","sequence":"additional","affiliation":[{"name":"Peking University, Beijing, China"}]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1109\/TSG.2024.3400968"},{"volume-title":"Demand Response","year":"2023","key":"ref2"},{"volume-title":"How AI Could Slash Energy Use in Buildings","year":"2024","author":"Thompson","key":"ref3"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1016\/j.apenergy.2024.123862"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1109\/TSG.2025.3598070"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1016\/j.egyr.2022.07.038"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1109\/TSG.2024.3458074"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1109\/TSG.2020.3025082"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1109\/TSG.2024.3460486"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1109\/TSG.2024.3482390"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1109\/TSG.2020.3011739"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-62416-7_19"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1109\/TSG.2021.3062700"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1109\/TSG.2022.3174918"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1016\/j.apenergy.2025.125897"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1109\/TII.2022.3209287"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1109\/TSG.2022.3199305"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1109\/TSG.2015.2418280"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2017.2709252"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1109\/TII.2023.3288221"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1109\/TSG.2022.3175470"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1016\/j.apenergy.2020.116015"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1016\/j.apenergy.2024.124831"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1016\/j.ress.2021.107966"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1016\/j.apenergy.2022.119688"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1109\/TSG.2021.3062722"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1016\/j.rser.2022.112423"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1109\/TSG.2016.2537210"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1016\/j.cose.2006.03.001"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1016\/B978-1-55860-335-6.50027-1"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1109\/TSG.2023.3324731"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1016\/j.apenergy.2022.118980"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1109\/TAI.2021.3111139"},{"key":"ref34","article-title":"Robust deep reinforcement learning with adversarial attacks","author":"Pattanaik","year":"2017","journal-title":"arXiv:1712.03632"},{"key":"ref35","first-page":"21024","article-title":"Robust deep reinforcement learning against adversarial perturbations on state observations","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Zhang"},{"article-title":"Robust reinforcement learning on state observations with learned optimal adversary","volume-title":"Proc. Int. Conf. Learn. Represent.","author":"Zhang","key":"ref36"},{"article-title":"Policy smoothing for provably robust reinforcement learning","volume-title":"Proc. Int. Conf. Learn. Represent. (ICLR)","author":"Kumar","key":"ref37"},{"key":"ref38","first-page":"1275","article-title":"Detection as regression: Certified object detection with median smoothing","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"33","author":"Chiang"},{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.1214\/aos\/1176342363"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.1109\/TSG.2023.3281782"},{"article-title":"Summary of travel trends: 2009 national household travel survey","year":"2011","author":"Santos","key":"ref41"},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.1109\/TII.2018.2812755"},{"key":"ref43","article-title":"Proximal policy optimization algorithms","author":"Schulman","year":"2017","journal-title":"arXiv:1707.06347"},{"key":"ref44","doi-asserted-by":"publisher","DOI":"10.1109\/TSG.2021.3088290"}],"container-title":["IEEE Transactions on Smart Grid"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx8\/5165411\/11311981\/11205874.pdf?arnumber=11205874","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,12,24]],"date-time":"2025-12-24T07:03:10Z","timestamp":1766559790000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/11205874\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,1]]},"references-count":44,"journal-issue":{"issue":"1"},"URL":"https:\/\/doi.org\/10.1109\/tsg.2025.3622228","relation":{},"ISSN":["1949-3053","1949-3061"],"issn-type":[{"type":"print","value":"1949-3053"},{"type":"electronic","value":"1949-3061"}],"subject":[],"published":{"date-parts":[[2026,1]]}}}