{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,11]],"date-time":"2026-06-11T16:18:28Z","timestamp":1781194708836,"version":"3.54.1"},"reference-count":190,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","license":[{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/legalcode"}],"funder":[{"DOI":"10.13039\/501100008445","name":"Energistyrelsen","doi-asserted-by":"publisher","award":["134243-533635"],"award-info":[{"award-number":["134243-533635"]}],"id":[{"id":"10.13039\/501100008445","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Access"],"published-print":{"date-parts":[[2025]]},"DOI":"10.1109\/access.2025.3610994","type":"journal-article","created":{"date-parts":[[2025,9,17]],"date-time":"2025-09-17T17:32:08Z","timestamp":1758130328000},"page":"163162-163188","source":"Crossref","is-referenced-by-count":25,"title":["A Review of Large Language Models for Energy Systems: Applications, Challenges, and Future Prospects"],"prefix":"10.1109","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1921-8830","authenticated-orcid":false,"given":"Hamid","family":"Mirshekali","sequence":"first","affiliation":[{"name":"SDU Center for Energy Informatics, Faculty of Engineering, M&#x00E6;rsk Mc-Kinney Moeller Institute, University of Southern Denmark (SDU), Odense, Denmark"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7279-2279","authenticated-orcid":false,"given":"Mohammad","family":"Reza Shadi","sequence":"additional","affiliation":[{"name":"SDU Center for Energy Informatics, Faculty of Engineering, M&#x00E6;rsk Mc-Kinney Moeller Institute, University of Southern Denmark (SDU), Odense, Denmark"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fatemehsadat","family":"Ghanadi Ladani","sequence":"additional","affiliation":[{"name":"Department of Electrical and Computer Engineering, Isfahan University of Technology, Isfahan, Iran"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2858-8400","authenticated-orcid":false,"given":"Hamid","family":"Reza Shaker","sequence":"additional","affiliation":[{"name":"SDU Center for Energy Informatics, Faculty of Engineering, M&#x00E6;rsk Mc-Kinney Moeller Institute, University of Southern Denmark (SDU), Odense, Denmark"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1016\/j.rser.2025.115558"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1016\/j.autcon.2024.105579"},{"key":"ref3","article-title":"A novel distributed PV power forecasting approach based on time-LLM","author":"Lin","year":"2025","journal-title":"arXiv:2503.06216"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-68427-2_2"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1109\/TSG.2025.3579890"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1016\/j.enconman.2025.119673"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1016\/j.enbuild.2024.115042"},{"issue":"3","key":"ref8","first-page":"1","article-title":"Large language models: A comprehensive survey of its applications, challenges, limitations, and future prospects","volume":"1","author":"Hadi","year":"2023","journal-title":"Authorea Preprints"},{"issue":"1","key":"ref9","first-page":"20","article-title":"Large language models: A comprehensive survey on architectures, applications, and challenges","volume":"7","author":"Veeramachaneni","year":"2025","journal-title":"Adv. Innov. Comput. Program. Lang."},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1007\/s12273-025-1235-9"},{"key":"ref11","article-title":"LLM alignment as retriever optimization: An information retrieval perspective","author":"Jin","year":"2025","journal-title":"arXiv:2502.03699"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1016\/j.ress.2024.110382"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1002\/eng2.12824"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.3390\/en17112534"},{"key":"ref15","article-title":"Large language models for forecasting and anomaly detection: A systematic literature review","author":"Su","year":"2024","journal-title":"arXiv:2402.10350"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.3390\/en18030660"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1016\/j.joule.2024.05.009"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1145\/3641289"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1162\/tacl_a_00324"},{"key":"ref20","first-page":"30016","article-title":"An empirical analysis of compute-optimal large language model training","volume-title":"Proc. Adv. neural Inf. Process. Syst.","volume":"35","author":"Hoffmann"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52733.2024.02553"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1016\/j.inffus.2025.103328"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1016\/j.ins.2025.122361"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1016\/j.neunet.2024.106833"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1038\/s41586-024-07421-0"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1207\/s15516709cog1402_1"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1016\/j.ifacol.2020.12.1342"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1162\/neco.1997.9.8.1735"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.48550\/ARXIV.1706.03762"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1016\/j.ipm.2024.103809"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2024.3445770"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2025.130135"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1109\/TSE.2024.3450837"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1016\/j.intell.2025.101922"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1109\/IPDPSW63119.2024.00016"},{"key":"ref36","article-title":"Improving language understanding by generative pre-training","author":"Radford","year":"2018"},{"key":"ref37","first-page":"4171","article-title":"BERT: Pre-training of deep bidirectional transformers for language understanding","volume-title":"Proc. Conf. North Amer. Chapter Assoc. Comput. Linguistics, Hum. Lang. Technol.","volume":"1","author":"Devlin"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/D19-5602"},{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.1007\/s11023-020-09548-1"},{"key":"ref40","article-title":"GPT-4 technical report","volume-title":"arXiv:2303.08774","author":"Achiam","year":"2023"},{"key":"ref41","article-title":"PaLM 2 technical report","volume-title":"arXiv:2305.10403","author":"Anil","year":"2023"},{"key":"ref42","article-title":"Gemini: A family of highly capable multimodal models","author":"Team","year":"2023","journal-title":"arXiv:2312.11805"},{"key":"ref43","article-title":"Bloom: A 176b-parameter open-access multilingual language model","author":"Le Scao","year":"2023","journal-title":"arXiv:2211.05100"},{"key":"ref44","article-title":"Arctic-TILT. Business document Understanding at sub-billion Scale","author":"Borchmann","year":"2024","journal-title":"arXiv:2408.04632"},{"key":"ref45","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-72952-2_19"},{"key":"ref46","article-title":"GPT-4o system card","author":"Hurst","year":"2024","journal-title":"arXiv:2410.21276"},{"key":"ref47","article-title":"The llama 3 herd of models","author":"Grattafiori","year":"2024","journal-title":"arXiv:2407.21783"},{"key":"ref48","article-title":"DeepSeek-V3 technical report","volume-title":"arXiv:2412.19437","author":"Liu","year":"2024"},{"key":"ref49","doi-asserted-by":"crossref","DOI":"10.1016\/j.jss.2025.112436","article-title":"RAGVA: Engineering retrieval augmented generation-based virtual assistants in practice","volume":"226","author":"Yang","year":"2025","journal-title":"J. Syst. Softw."},{"key":"ref50","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2024.3470850"},{"key":"ref51","doi-asserted-by":"publisher","DOI":"10.1109\/ICME57554.2024.10688138"},{"key":"ref52","doi-asserted-by":"publisher","DOI":"10.1109\/KST61284.2024.10499664"},{"key":"ref53","article-title":"CCNet: Extracting high quality monolingual datasets from Web crawl data","author":"Wenzek","year":"2019","journal-title":"arXiv:1911.00359"},{"key":"ref54","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2015.11"},{"key":"ref55","volume-title":"Wikipedia Dumps","year":"2021"},{"key":"ref56","article-title":"On the use of arXiv as a dataset","author":"Clement","year":"2019","journal-title":"arXiv:1905.00075"},{"key":"ref57","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/N18-2097"},{"key":"ref58","article-title":"The pile: An 800GB dataset of diverse text for language modeling","author":"Gao","year":"2021","journal-title":"arXiv:2101.00027"},{"key":"ref59","article-title":"Unsupervised cross-lingual representation learning at scale","author":"Conneau","year":"2019","journal-title":"arXiv:1911.02116"},{"key":"ref60","doi-asserted-by":"publisher","DOI":"10.1145\/3212695"},{"key":"ref61","doi-asserted-by":"publisher","DOI":"10.1609\/icwsm.v14i1.7347"},{"key":"ref62","first-page":"27730","article-title":"Training language models to follow instructions with human feedback","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Ouyang"},{"key":"ref63","article-title":"Finetuned language models are zero-shot learners","author":"Wei","year":"2021","journal-title":"arXiv:2109.01652"},{"key":"ref64","article-title":"Multitask prompted training enables zero-shot task generalization","author":"Sanh","year":"2021","journal-title":"arXiv:2110.08207"},{"key":"ref65","doi-asserted-by":"publisher","DOI":"10.1016\/j.aei.2024.103007"},{"key":"ref66","doi-asserted-by":"publisher","DOI":"10.1109\/IALP63756.2024.10661135"},{"key":"ref67","doi-asserted-by":"publisher","DOI":"10.1093\/bioinformatics\/btz682"},{"key":"ref68","first-page":"2790","article-title":"Parameter-efficient transfer learning for NLP","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Houlsby"},{"key":"ref69","article-title":"LoRA: Low-rank adaptation of large language models","author":"Hu","year":"2021","journal-title":"arXiv:2106.09685"},{"key":"ref70","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2021.emnlp-main.243"},{"key":"ref71","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2021.acl-long.353"},{"key":"ref72","volume-title":"Few-shot and chain-of-thought prompting for equipment maintenance knowledge graph construction via large language models","author":"Xing","year":"2025"},{"key":"ref73","doi-asserted-by":"publisher","DOI":"10.1109\/DASA63652.2024.10836639"},{"key":"ref74","article-title":"Can large language model agents balance energy systems?","author":"Ren","year":"2025","journal-title":"arXiv:2502.10557"},{"key":"ref75","doi-asserted-by":"publisher","DOI":"10.24425\/ijet.2025.153538"},{"key":"ref76","doi-asserted-by":"publisher","DOI":"10.1016\/j.egyai.2024.100431"},{"key":"ref77","article-title":"EF-LLM: Energy forecasting LLM with AI-assisted automation, enhanced sparse prediction, hallucination detection","author":"Qiu","year":"2024","journal-title":"arXiv:2411.00852"},{"key":"ref78","doi-asserted-by":"publisher","DOI":"10.1016\/j.procs.2022.11.180"},{"key":"ref79","doi-asserted-by":"publisher","DOI":"10.17775\/CSEEJPES.2022.02050"},{"key":"ref80","article-title":"SafePowerGraph-LLM: Novel power grid graph embedding and optimization with large language models","author":"Bernier","year":"2025","journal-title":"arXiv:2501.07639"},{"key":"ref81","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-025-91940-x"},{"key":"ref82","article-title":"Enabling large language models to perform power system simulations with previously unseen tools: A case of daline","author":"Jia","year":"2024","journal-title":"arXiv:2406.17215"},{"key":"ref83","doi-asserted-by":"publisher","DOI":"10.1109\/PESGM51994.2024.10688670"},{"key":"ref84","doi-asserted-by":"publisher","DOI":"10.1016\/j.enbuild.2024.114788"},{"key":"ref85","doi-asserted-by":"publisher","DOI":"10.3390\/en17236063"},{"key":"ref86","doi-asserted-by":"publisher","DOI":"10.1109\/ICAIIC64266.2025.10920654"},{"key":"ref87","article-title":"ALLarMa: LLM-based application for operational data, alarms and networks","author":"Shah","year":"2025","journal-title":"TechRxiv"},{"key":"ref88","doi-asserted-by":"publisher","DOI":"10.1016\/j.patter.2025.101211"},{"key":"ref89","article-title":"EFedLLM: Efficient LLM inference based on federated learning","author":"Ding","year":"2024","journal-title":"arXiv:2411.16003"},{"key":"ref90","article-title":"LFLLM: A large language model for load forecasting","author":"Liu","year":"2024","journal-title":"TechRxiv"},{"key":"ref91","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2025.naacl-long.208"},{"key":"ref92","doi-asserted-by":"publisher","DOI":"10.1145\/3679240.3734586"},{"key":"ref93","article-title":"LLM-based frameworks for power engineering from routine to novel tasks","author":"Li","year":"2023","journal-title":"arXiv:2305.11202"},{"key":"ref94","doi-asserted-by":"publisher","DOI":"10.1016\/j.apenergy.2024.123431"},{"key":"ref95","article-title":"Grid-Agent: An LLM-powered multi-agent system for power grid control","author":"Zhang","year":"2025","journal-title":"arXiv:2508.05702"},{"key":"ref96","doi-asserted-by":"publisher","DOI":"10.1145\/3719664"},{"key":"ref97","first-page":"1183","article-title":"LLMs on the edge: Quality, latency, and energy efficiency","volume-title":"Proc. INFORMATIK","author":"Bast"},{"key":"ref98","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2024.3409745"},{"key":"ref99","doi-asserted-by":"publisher","DOI":"10.1109\/CAIN66642.2025.00016"},{"issue":"4","key":"ref100","first-page":"87","article-title":"AWQ: Activation-aware weight quantization for on-device LLM compression and acceleration","volume":"6","author":"Lin","year":"2024","journal-title":"Proc. Mach. Learn. Syst."},{"key":"ref101","first-page":"196","article-title":"Atom: Low-bit quantization for efficient and accurate llm serving","volume":"6","author":"Zhao","year":"2024","journal-title":"Proc. Mach. Learn. Syst."},{"key":"ref102","article-title":"CLONE: Customizing LLMs for efficient latency-aware inference at the edge","author":"Tian","year":"2025","journal-title":"arXiv:2506.02847"},{"key":"ref103","doi-asserted-by":"publisher","DOI":"10.1109\/TMC.2024.3415661"},{"key":"ref104","article-title":"SLED: A speculative LLM decoding framework for efficient edge serving","author":"Li","year":"2025","journal-title":"arXiv:2506.09397"},{"key":"ref105","article-title":"Thoughtful things: Building human-centric smart devices with small language models","author":"King","year":"2024","journal-title":"arXiv:2405.03821"},{"key":"ref106","doi-asserted-by":"publisher","DOI":"10.1145\/3649329.3658473"},{"key":"ref107","doi-asserted-by":"publisher","DOI":"10.1109\/ASPDAC.2018.8297274"},{"key":"ref108","doi-asserted-by":"publisher","DOI":"10.1038\/s41467-024-53352-9"},{"key":"ref109","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-025-07592-4"},{"key":"ref110","doi-asserted-by":"publisher","DOI":"10.1016\/j.future.2024.107599"},{"key":"ref111","doi-asserted-by":"publisher","DOI":"10.1016\/j.iot.2023.101035"},{"key":"ref112","doi-asserted-by":"publisher","DOI":"10.1016\/j.enconman.2024.118331"},{"key":"ref113","doi-asserted-by":"publisher","DOI":"10.1016\/j.apenergy.2024.124378"},{"key":"ref114","doi-asserted-by":"publisher","DOI":"10.1016\/j.enbuild.2024.114691"},{"key":"ref115","doi-asserted-by":"publisher","DOI":"10.1145\/3600100.3623730"},{"key":"ref116","doi-asserted-by":"publisher","DOI":"10.1016\/j.enbuild.2024.114278"},{"key":"ref117","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2025.131288"},{"key":"ref118","doi-asserted-by":"publisher","DOI":"10.3390\/designs8030043"},{"key":"ref119","article-title":"Fault diagnosis in power grids with large language model","author":"Jing","year":"2024","journal-title":"arXiv:2407.08836"},{"key":"ref120","first-page":"1","article-title":"Flex: Fault localization and explanation using open-source large language models in powertrain systems (short paper)","volume-title":"Proc. 35th Int. Conf. Princ. Diagnosis Resilient Syst.","author":"Muehlburger"},{"key":"ref121","volume-title":"Large language models repower autonomous research of data-driven tasks in power systems","author":"Liu","year":"2024"},{"key":"ref122","doi-asserted-by":"publisher","DOI":"10.1109\/tsg.2025.3589114"},{"key":"ref123","doi-asserted-by":"publisher","DOI":"10.1109\/ISPCE-ASIA60405.2023.10365878"},{"key":"ref124","article-title":"SubstationAI: Multimodal large model-based approaches for analyzing substation equipment faults","author":"Wang","year":"2024","journal-title":"arXiv:2412.17077"},{"key":"ref125","doi-asserted-by":"publisher","DOI":"10.1049\/cim2.70007"},{"key":"ref126","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-024-83561-7"},{"key":"ref127","doi-asserted-by":"publisher","DOI":"10.1109\/ICEPG63230.2024.10775609"},{"key":"ref128","doi-asserted-by":"publisher","DOI":"10.1109\/AUPEC62273.2024.10807616"},{"key":"ref129","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2020.3024750"},{"key":"ref130","doi-asserted-by":"publisher","DOI":"10.3390\/en17194855"},{"key":"ref131","doi-asserted-by":"publisher","DOI":"10.1038\/s41560-023-01319-3"},{"key":"ref132","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-74741-0_4"},{"key":"ref133","doi-asserted-by":"publisher","DOI":"10.1016\/j.enbuild.2024.115173"},{"key":"ref134","doi-asserted-by":"publisher","DOI":"10.1109\/MN60932.2024.10615758"},{"key":"ref135","doi-asserted-by":"publisher","DOI":"10.55092\/sc20250004"},{"key":"ref136","volume-title":"LFLLM: A robust large language model for short-term load forecasting in smart grids","author":"Liu","year":"2024"},{"key":"ref137","doi-asserted-by":"publisher","DOI":"10.1145\/3627673.3679933"},{"key":"ref138","article-title":"A general framework for load forecasting based on pre-trained large language model","author":"Gao","year":"2024","journal-title":"arXiv:2406.11336"},{"key":"ref139","article-title":"Zero-shot load forecasting with large language models","author":"Liao","year":"2024","journal-title":"arXiv:2411.11350"},{"key":"ref140","doi-asserted-by":"publisher","DOI":"10.3390\/en17102338"},{"key":"ref141","doi-asserted-by":"publisher","DOI":"10.1109\/TEMPR.2024.3518624"},{"key":"ref142","doi-asserted-by":"publisher","DOI":"10.1016\/j.apenergy.2024.124034"},{"key":"ref143","doi-asserted-by":"publisher","DOI":"10.1109\/ICIEA61579.2024.10665072"},{"key":"ref144","doi-asserted-by":"publisher","DOI":"10.1109\/TII.2023.3348819"},{"key":"ref145","doi-asserted-by":"publisher","DOI":"10.1007\/978-981-97-5618-6_37"},{"key":"ref146","doi-asserted-by":"publisher","DOI":"10.1016\/j.enbenv.2021.05.006"},{"key":"ref147","doi-asserted-by":"publisher","DOI":"10.1016\/j.rser.2019.04.021"},{"key":"ref148","doi-asserted-by":"publisher","DOI":"10.1080\/10789669.2005.10391123"},{"key":"ref149","article-title":"Benchmarking time series foundation models for short-term household electricity load forecasting","author":"Meyer","year":"2024","journal-title":"arXiv:2410.09487"},{"key":"ref150","doi-asserted-by":"publisher","DOI":"10.1016\/j.apenergy.2024.124973"},{"key":"ref151","doi-asserted-by":"publisher","DOI":"10.1145\/3575813.3597345"},{"key":"ref152","doi-asserted-by":"publisher","DOI":"10.1186\/s42162-023-00278-z"},{"key":"ref153","article-title":"Large language model-empowered interactive load forecasting","author":"Zuo","year":"2025","journal-title":"arXiv:2505.16577"},{"key":"ref154","doi-asserted-by":"publisher","DOI":"10.3390\/en18092380"},{"key":"ref155","article-title":"AI for energy: Opportunities for a modern grid and clean energy economy","author":"Benes","year":"2024"},{"key":"ref156","volume-title":"Artificial Intelligence and Machine Learning in Real-time System Operations: White Paper","year":"2024"},{"key":"ref157","doi-asserted-by":"publisher","DOI":"10.1016\/j.apenergy.2024.124067"},{"key":"ref158","article-title":"Interpretable machine learning for power systems: Establishing confidence in SHapley additive exPlanations","volume-title":"Proc. Climate Change AI Workshop ICLR (Tackling Climate Change Machine Learn.)","author":"Ahmad"},{"key":"ref159","doi-asserted-by":"publisher","DOI":"10.1016\/j.egyai.2024.100358"},{"key":"ref160","first-page":"222","article-title":"Interpretable data-driven model predictive control for building energy systems","volume-title":"Proc. Mach. Learn. Res.","volume":"242","author":"Henkel"},{"key":"ref161","article-title":"Benchmarking energy and latency in TinyML: A novel method for resource-constrained AI","author":"Bartoli","year":"2025","journal-title":"arXiv:2505.15622"},{"key":"ref162","doi-asserted-by":"publisher","DOI":"10.1016\/j.rser.2025.115668"},{"key":"ref163","doi-asserted-by":"publisher","DOI":"10.1109\/CPE-POWERENG63314.2025.11027239"},{"key":"ref164","doi-asserted-by":"publisher","DOI":"10.1109\/CPE-POWERENG63314.2025.11027287"},{"key":"ref165","doi-asserted-by":"publisher","DOI":"10.1016\/j.apenergy.2023.120860"},{"key":"ref166","doi-asserted-by":"publisher","DOI":"10.1016\/j.renene.2024.122192"},{"key":"ref167","doi-asserted-by":"publisher","DOI":"10.1080\/01605682.2024.2441224"},{"key":"ref168","article-title":"FD-LLM: Large language model for fault diagnosis of machines","author":"Qaid","year":"2024","journal-title":"arXiv:2412.01218"},{"key":"ref169","doi-asserted-by":"publisher","DOI":"10.1016\/j.enbuild.2025.115515"},{"key":"ref170","doi-asserted-by":"publisher","DOI":"10.3390\/smartcities7060121"},{"key":"ref171","doi-asserted-by":"publisher","DOI":"10.1109\/TIE.2024.3454408"},{"key":"ref172","article-title":"Usable XAI: 10 strategies towards exploiting explainability in the LLM era","author":"Wu","year":"2024","journal-title":"arXiv:2403.08946"},{"key":"ref173","article-title":"A comprehensive survey on the security of smart grid: Challenges, mitigations, and future research opportunities","author":"Zibaeirad","year":"2024","journal-title":"arXiv:2407.07966"},{"key":"ref174","doi-asserted-by":"crossref","DOI":"10.2139\/ssrn.5217718","volume-title":"Power system operation security verification with retrieval-augmented generation enhanced large language model","author":"Cheng","year":"2025"},{"key":"ref175","doi-asserted-by":"publisher","DOI":"10.1109\/TWC.2024.3497923"},{"key":"ref176","doi-asserted-by":"publisher","DOI":"10.1109\/PESGM51994.2024.10688863"},{"key":"ref177","doi-asserted-by":"publisher","DOI":"10.1109\/TSG.2024.3373256"},{"key":"ref178","doi-asserted-by":"publisher","DOI":"10.1109\/BDCAT63179.2024.00065"},{"key":"ref179","doi-asserted-by":"publisher","DOI":"10.1016\/j.rineng.2024.102647"},{"key":"ref180","doi-asserted-by":"publisher","DOI":"10.1016\/j.dib.2024.110461"},{"key":"ref181","article-title":"Zero-shot and few-shot learning with instruction-following LLMs for claim matching in automated fact-checking","author":"Pisarevskaya","year":"2025","journal-title":"arXiv:2501.10860"},{"key":"ref182","doi-asserted-by":"publisher","DOI":"10.1016\/j.jisa.2022.103262"},{"key":"ref183","doi-asserted-by":"publisher","DOI":"10.1145\/3575813.3597352"},{"key":"ref184","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijepes.2025.110878"},{"key":"ref185","article-title":"Risks of practicing large language models in smart grid: Threat modeling and validation","author":"Li","year":"2024","journal-title":"arXiv:2405.06237"},{"key":"ref186","doi-asserted-by":"publisher","DOI":"10.3390\/s23073697"},{"key":"ref187","doi-asserted-by":"publisher","DOI":"10.1016\/j.epsr.2021.107410"},{"key":"ref188","doi-asserted-by":"publisher","DOI":"10.1016\/j.egyr.2024.08.033"},{"key":"ref189","doi-asserted-by":"publisher","DOI":"10.1186\/s42400-025-00361-w"},{"key":"ref190","doi-asserted-by":"publisher","DOI":"10.1016\/j.hcc.2024.100211"}],"container-title":["IEEE Access"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx8\/6287639\/10820123\/11168242.pdf?arnumber=11168242","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,30]],"date-time":"2025-09-30T12:34:07Z","timestamp":1759235647000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/11168242\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025]]},"references-count":190,"URL":"https:\/\/doi.org\/10.1109\/access.2025.3610994","relation":{},"ISSN":["2169-3536"],"issn-type":[{"value":"2169-3536","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025]]}}}