{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,26]],"date-time":"2026-07-26T15:00:24Z","timestamp":1785078024513,"version":"3.55.0"},"reference-count":225,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,6,5]],"date-time":"2026-06-05T00:00:00Z","timestamp":1780617600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Array"],"published-print":{"date-parts":[[2026,9]]},"DOI":"10.1016\/j.array.2026.100987","type":"journal-article","created":{"date-parts":[[2026,6,9]],"date-time":"2026-06-09T23:52:45Z","timestamp":1781049165000},"page":"100987","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"C","title":["A survey of machine unlearning for electric power systems: From privacy compliance to resilient grid operations"],"prefix":"10.1016","volume":"31","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3100-1869","authenticated-orcid":false,"given":"Khaled","family":"Chahine","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9297-0028","authenticated-orcid":false,"given":"Georges","family":"Zakka El Nashef","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4390-9858","authenticated-orcid":false,"given":"Abdel Karim","family":"Abdel Karim","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4421-3628","authenticated-orcid":false,"given":"Marc","family":"Al Atem","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2589-5053","authenticated-orcid":false,"given":"Hassan N.","family":"Noura","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2819-9432","authenticated-orcid":false,"given":"Mohamad","family":"Arnaout","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"key":"10.1016\/j.array.2026.100987_b1","article-title":"Power consumption prediction in warehouses using variational autoencoders and tree-based regression models","author":"Allal","year":"2024","journal-title":"Energy Built Environ"},{"key":"10.1016\/j.array.2026.100987_b2","doi-asserted-by":"crossref","DOI":"10.1016\/j.engappai.2024.108304","article-title":"Leveraging the power of machine learning and data balancing techniques to evaluate stability in smart grids","volume":"133","author":"Allal","year":"2024","journal-title":"Eng Appl Artif Intell"},{"key":"10.1016\/j.array.2026.100987_b3","doi-asserted-by":"crossref","unstructured":"Koubayssi Ali, Arnaout Mohamad, Chahine Mohamad Abou, Absi Rafik. Optimal Reactive Power Compensation for Hybrid Multi-Source Systems Operating in Different IEEE Distribution Buses: Enhancing Voltage Stability and Loss Reduction. In: 2024 international conference on smart systems and power management. IC2SPM, 2024, p. 44\u20139.","DOI":"10.1109\/IC2SPM62723.2024.10841346"},{"key":"10.1016\/j.array.2026.100987_b4","doi-asserted-by":"crossref","unstructured":"Koubayssi Ali, Arnaout Mohamad, Chahine Mohamad Abou, Absi Rafik. Voltage Stability Enhancement in Lebanese Islanded Hybrid Microgrid: The Role of Shunt Capacitor Banks and Synchronous Generator Droop Control. In: 2024 international conference on microelectronics. ICM, 2024, p. 1\u20135.","DOI":"10.1109\/ICM63406.2024.10815882"},{"issue":"1","key":"10.1016\/j.array.2026.100987_b5","doi-asserted-by":"crossref","DOI":"10.3390\/eng6010020","article-title":"Tree-based algorithms and incremental feature optimization for fault detection and diagnosis in photovoltaic systems","volume":"6","author":"Chahine","year":"2025","journal-title":"Eng"},{"key":"10.1016\/j.array.2026.100987_b6","article-title":"Feature engineering for fault detection and diagnosis in power transmission lines using a tree-based approach","volume":"12","author":"Noura","year":"2025","journal-title":"E-Prime - Adv Electr Eng Electron Energy"},{"key":"10.1016\/j.array.2026.100987_b7","article-title":"Wind turbine fault detection and identification using a two-tier machine learning framework","volume":"22","author":"Allal","year":"2024","journal-title":"Intell Syst Appl"},{"key":"10.1016\/j.array.2026.100987_b8","doi-asserted-by":"crossref","DOI":"10.1016\/j.engappai.2024.109503","article-title":"Explainable artificial intelligence of tree-based algorithms for fault detection and diagnosis in grid-connected photovoltaic systems","volume":"139","author":"Noura","year":"2025","journal-title":"Eng Appl Artif Intell"},{"key":"10.1016\/j.array.2026.100987_b9","doi-asserted-by":"crossref","unstructured":"Arnaout Mohamad, Ghizzawi Ahmad, Hassan Ali Al-Hajj, Koubayssi Ali, Kafal Moussa, Noun Ziad. The Detection and Classification of Faults by the Use of Machine Learning Technique. In: 2021 international conference on microelectronics. ICM, 2021, p. 242\u20135.","DOI":"10.1109\/ICM52667.2021.9664906"},{"key":"10.1016\/j.array.2026.100987_b10","doi-asserted-by":"crossref","unstructured":"Reda Ali, Al Kurdi Imad, Noun Ziad, Koubyssi Ali, Arnaout Mohamad, Rammal Rabih. Online Detection of Faults in Transmission Lines. In: 2021 IEEE 3rd international multidisciplinary conference on engineering technology. IMCET, 2021, p. 37\u201342.","DOI":"10.1109\/IMCET53404.2021.9665620"},{"key":"10.1016\/j.array.2026.100987_b11","doi-asserted-by":"crossref","DOI":"10.1016\/j.epsr.2023.109974","article-title":"Visually impaired recognition of a fully loaded high and medium voltages electrical network for layout formation and fault monitoring","volume":"227","author":"Mohamad","year":"2024","journal-title":"Electr Power Syst Res"},{"key":"10.1016\/j.array.2026.100987_b12","doi-asserted-by":"crossref","unstructured":"Zaman Mostafa, Saha Sujay, Zohrabi Nasibeh, Abdelwahed Sherif. Demand-Response Prediction in Smart Grids Using Machine Learning Techniques. In: 2024 IEEE power & energy society innovative smart grid technologies conference. ISGT, 2024, p. 1\u20135.","DOI":"10.1109\/ISGT59692.2024.10454224"},{"issue":"10","key":"10.1016\/j.array.2026.100987_b13","doi-asserted-by":"crossref","DOI":"10.3390\/en17102329","article-title":"A neural network forecasting approach for the smart grid demand response management problem","volume":"17","author":"Belhaiza","year":"2024","journal-title":"Energies"},{"key":"10.1016\/j.array.2026.100987_b14","doi-asserted-by":"crossref","DOI":"10.1016\/j.cosrev.2024.100617","article-title":"Deep learning for intelligent demand response and smart grids: A comprehensive survey","volume":"51","author":"Boopathy","year":"2024","journal-title":"Comput Sci Rev"},{"key":"10.1016\/j.array.2026.100987_b15","doi-asserted-by":"crossref","DOI":"10.1016\/j.jenvman.2024.120392","article-title":"Machine learning solutions for renewable energy systems: Applications, challenges, limitations, and future directions","volume":"354","author":"Allal","year":"2024","journal-title":"J Environ Manag"},{"issue":"1","key":"10.1016\/j.array.2026.100987_b16","doi-asserted-by":"crossref","first-page":"91","DOI":"10.1186\/s44147-024-00422-w","article-title":"Privacy and security of advanced metering infrastructure (AMI) data and network: a comprehensive review","volume":"71","author":"Ajiboye","year":"2024","journal-title":"J Eng Appl Sci"},{"issue":"7","key":"10.1016\/j.array.2026.100987_b17","doi-asserted-by":"crossref","first-page":"3697","DOI":"10.3390\/s23073697","article-title":"Privacy preservation in smart meters: Current status, challenges and future directions","volume":"23","author":"Kua","year":"2023","journal-title":"Sensors"},{"issue":"1","key":"10.1016\/j.array.2026.100987_b18","doi-asserted-by":"crossref","DOI":"10.1038\/s41598-024-78285-7","article-title":"The privacy cost of fine-grained electrical consumption data","volume":"15","author":"Voyez","year":"2025","journal-title":"Sci Rep"},{"issue":"11","key":"10.1016\/j.array.2026.100987_b19","doi-asserted-by":"crossref","first-page":"18951","DOI":"10.1109\/JIOT.2024.3349381","article-title":"Vulnerability of machine learning approaches applied in IoT-based smart grid: A review","volume":"11","author":"Zhang","year":"2024","journal-title":"IEEE Internet Things J"},{"issue":"9","key":"10.1016\/j.array.2026.100987_b20","doi-asserted-by":"crossref","first-page":"4509","DOI":"10.3390\/s23094509","article-title":"Research on data poisoning attack against smart grid cyber\u2013physical system based on edge computing","volume":"23","author":"Zhu","year":"2023","journal-title":"Sensors"},{"issue":"7","key":"10.1016\/j.array.2026.100987_b21","doi-asserted-by":"crossref","DOI":"10.1007\/s10462-024-10827-x","article-title":"Trustworthy cyber-physical power systems using AI: dueling algorithms for PMU anomaly detection and cybersecurity","volume":"57","author":"Cali","year":"2024","journal-title":"Artif Intell Rev"},{"key":"10.1016\/j.array.2026.100987_b22","series-title":"Proceedings of the 36th annual ACM symposium on applied computing","first-page":"116","article-title":"Poisoning attacks on cyber attack detectors for industrial control systems","author":"Kravchik","year":"2021"},{"issue":"13","key":"10.1016\/j.array.2026.100987_b23","doi-asserted-by":"crossref","first-page":"3887","DOI":"10.3390\/en14133887","article-title":"Bi-level poisoning attack model and countermeasure for appliance consumption data of smart homes","volume":"14","author":"Billah","year":"2021","journal-title":"Energies"},{"key":"10.1016\/j.array.2026.100987_b24","series-title":"Machine-learned adversarial attacks against fault prediction systems in smart electrical grids","author":"Ardito","year":"2023"},{"key":"10.1016\/j.array.2026.100987_b25","series-title":"Understanding the safety requirements for learning-based power systems operations","author":"Chen","year":"2021"},{"key":"10.1016\/j.array.2026.100987_b26","series-title":"Security and privacy in communication networks","first-page":"365","article-title":"Adversarial false data injection attack against nonlinear AC state estimation with ANN in smart grid","author":"Liu","year":"2019"},{"key":"10.1016\/j.array.2026.100987_b27","doi-asserted-by":"crossref","unstructured":"Omara Ahmed M, Kantarc\u0131 Burak. Adversarial Machine Learning-Based Anticipation of Threats Against Vehicle-to-Microgrid Services. In: GLOBECOM 2022 - 2022 IEEE global communications conference. 2022, p. 1844.","DOI":"10.1109\/GLOBECOM48099.2022.10000757"},{"key":"10.1016\/j.array.2026.100987_b28","series-title":"Proceedings of the thirty-third international joint conference on artificial intelligence, IJCAI-24","first-page":"8589","article-title":"Machine unlearning: Challenges in data quality and access","author":"Xu","year":"2024"},{"key":"10.1016\/j.array.2026.100987_b29","doi-asserted-by":"crossref","unstructured":"Bourtoule Lucas, Chandrasekaran Varun, Choquette-Choo Christopher A, Jia Hengrui, Travers Adelin, Zhang Baiwu, Lie David, Papernot Nicolas. Machine Unlearning. In: 2021 IEEE symposium on security and privacy. SP, 2021, p. 141\u201359.","DOI":"10.1109\/SP40001.2021.00019"},{"issue":"6","key":"10.1016\/j.array.2026.100987_b30","doi-asserted-by":"crossref","first-page":"7178","DOI":"10.1109\/TPWRS.2024.3376828","article-title":"Task-aware machine unlearning and its application in load forecasting","volume":"39","author":"Xu","year":"2024","journal-title":"IEEE Trans Power Syst"},{"issue":"1","key":"10.1016\/j.array.2026.100987_b31","doi-asserted-by":"crossref","first-page":"62","DOI":"10.1186\/s42162-025-00524-6","article-title":"Machine learning applications in energy systems: current trends, challenges, and research directions","volume":"8","author":"Aslam","year":"2025","journal-title":"Energy Inform"},{"issue":"2","key":"10.1016\/j.array.2026.100987_b32","doi-asserted-by":"crossref","first-page":"1238","DOI":"10.1109\/COMST.2024.3430368","article-title":"A survey on intelligent internet of things: Applications, security, privacy, and future directions","volume":"27","author":"Aouedi","year":"2025","journal-title":"IEEE Commun Surv Tutor"},{"issue":"3","key":"10.1016\/j.array.2026.100987_b33","doi-asserted-by":"crossref","first-page":"79","DOI":"10.1109\/MC.2023.3333319","article-title":"Learn to unlearn: Insights into machine unlearning","volume":"57","author":"Qu","year":"2024","journal-title":"Computer"},{"issue":"1","key":"10.1016\/j.array.2026.100987_b34","doi-asserted-by":"crossref","first-page":"45","DOI":"10.1109\/MC.2024.3405397","article-title":"The frontier of data erasure: A survey on machine unlearning for large language models","volume":"58","author":"Qu","year":"2025","journal-title":"Computer"},{"key":"10.1016\/j.array.2026.100987_b35","doi-asserted-by":"crossref","DOI":"10.1016\/j.compeleceng.2025.110371","article-title":"A comprehensive survey on privacy-preserving technologies for smart grids","volume":"124","author":"Bibi","year":"2025","journal-title":"Comput Electr Eng"},{"issue":"9","key":"10.1016\/j.array.2026.100987_b36","doi-asserted-by":"crossref","first-page":"263","DOI":"10.1007\/s44443-025-00179-z","article-title":"Survey of privacy-preserving data aggregation schemes in smart grid","volume":"37","author":"Han","year":"2025","journal-title":"J King Saud Univ Comput Inf Sci"},{"key":"10.1016\/j.array.2026.100987_b37","series-title":"Advances in information systems development","first-page":"113","article-title":"Smart grid challenges through the lens of the European general data protection regulation","volume":"vol. 39","author":"Martinez","year":"2020"},{"issue":"3","key":"10.1016\/j.array.2026.100987_b38","doi-asserted-by":"crossref","DOI":"10.3390\/en15031088","article-title":"The legal complexities of processing and protecting personal data in the electricity sector","volume":"15","author":"Lavrijssen","year":"2022","journal-title":"Energies"},{"key":"10.1016\/j.array.2026.100987_b39","series-title":"Data sharing, privacy and security considerations in the energy sector: A review from technical landscape to regulatory specifications","author":"Zhang","year":"2025"},{"key":"10.1016\/j.array.2026.100987_b40","doi-asserted-by":"crossref","unstructured":"Baracaldo Nathalie, Chen Bryant, Ludwig Heiko, Safavi Jaehoon Amir. Mitigating Poisoning Attacks on Machine Learning Models: A Data Provenance Based Approach. In: Proceedings of the 10th ACM workshop on artificial intelligence and security. 2017.","DOI":"10.1145\/3128572.3140450"},{"issue":"19","key":"10.1016\/j.array.2026.100987_b41","doi-asserted-by":"crossref","first-page":"8860","DOI":"10.3390\/app14198860","article-title":"Bias in machine learning: A literature review","volume":"14","author":"Mavrogiorgos","year":"2024","journal-title":"Appl Sci"},{"key":"10.1016\/j.array.2026.100987_b42","series-title":"Official journal of the european communities no L 281","article-title":"Directive 95\/46\/EC of the European parliament and of the council on the protection of individuals with regard to the processing of personal data and on the free movement of such data","year":"1995"},{"key":"10.1016\/j.array.2026.100987_b43","series-title":"Argentina\u2019s personal data protection act (law 25.326)","year":"2000"},{"issue":"1","key":"10.1016\/j.array.2026.100987_b44","doi-asserted-by":"crossref","first-page":"156","DOI":"10.2307\/2642053","article-title":"U.S.-EU \u2018safe harbor\u2019 data privacy arrangement","volume":"95","author":"Murphy","year":"2001","journal-title":"Am J Int Law"},{"key":"10.1016\/j.array.2026.100987_b45","series-title":"Federal law no. 149-FZ of July 27, 2006, on information, informational technologies, and the protection of information","year":"2006"},{"key":"10.1016\/j.array.2026.100987_b46","unstructured":"Google Spain SL and Google Inc. v Agencia espa\u00f1ola de protecci\u00f3n de datos (AEPD) and mario costeja gonz\u00e1lez. Reports of cases - ECLI:EU:C:2014:317, 2014, [Accessed 08 February 2025]."},{"key":"10.1016\/j.array.2026.100987_b47","series-title":"Official journal of the European Union L 119","year":"2016"},{"key":"10.1016\/j.array.2026.100987_b48","article-title":"Loi n\u00b02016-1321 du 7 octobre 2016 pour une r\u00e9publique num\u00e9rique","year":"2016","journal-title":"J Off R\u00e9publique Fran\u00e7aise - N\u00b0235 Du 8 Octobre 2016"},{"key":"10.1016\/j.array.2026.100987_b49","series-title":"Official gazette of bermuda","article-title":"Personal information protection act 2016 (PIPA) - Bermuda","year":"2016"},{"key":"10.1016\/j.array.2026.100987_b50","series-title":"California consumer privacy act (CCPA)","year":"2018"},{"key":"10.1016\/j.array.2026.100987_b51","series-title":"Act on the protection of personal information (APPI) - Japan","year":"2020"},{"key":"10.1016\/j.array.2026.100987_b52","series-title":"Personal information protection law (PIPL) - China","year":"2021"},{"key":"10.1016\/j.array.2026.100987_b53","series-title":"Consumer privacy protection act (CPPA) - Canada","year":"2022"},{"key":"10.1016\/j.array.2026.100987_b54","series-title":"The personal information protection and electronic documents act (PIPEDA)","author":"Office of the Privacy Commissioner of Canada","year":"2023"},{"key":"10.1016\/j.array.2026.100987_b55","series-title":"Machine unlearning doesn\u2019t do what you think: Lessons for generative AI policy, research, and practice","author":"Cooper","year":"2024"},{"key":"10.1016\/j.array.2026.100987_b56","doi-asserted-by":"crossref","unstructured":"Koch Korbinian, Soll Marcus. No Matter How You Slice It: Machine Unlearning with SISA Comes at the Expense of Minority Classes. In: 2023 IEEE conference on secure and trustworthy machine learning. SaTML, 2023, p. 622\u201337.","DOI":"10.1109\/SaTML54575.2023.00047"},{"key":"10.1016\/j.array.2026.100987_b57","first-page":"16025","article-title":"Variational bayesian unlearning","volume":"33","author":"Nguyen","year":"2020","journal-title":"Adv Neural Inf Process Syst"},{"key":"10.1016\/j.array.2026.100987_b58","series-title":"Unlearning with fisher masking","author":"Liu","year":"2023"},{"key":"10.1016\/j.array.2026.100987_b59","doi-asserted-by":"crossref","unstructured":"Foster Jack, Schoepf Stefan, Brintrup Alexandra. Fast machine unlearning without retraining through selective synaptic dampening. In: Proceedings of the AAAI conference on artificial intelligence. vol. 38, 2024, p. 12043\u201351.","DOI":"10.1609\/aaai.v38i11.29092"},{"issue":"2","key":"10.1016\/j.array.2026.100987_b60","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3701763","article-title":"Recommendation unlearning via influence function","volume":"3","author":"Zhang","year":"2024","journal-title":"ACM Trans Recomm Syst"},{"key":"10.1016\/j.array.2026.100987_b61","series-title":"Algorithmic learning theory","first-page":"931","article-title":"Descent-to-delete: Gradient-based methods for machine unlearning","author":"Neel","year":"2021"},{"key":"10.1016\/j.array.2026.100987_b62","doi-asserted-by":"crossref","DOI":"10.1109\/TCDS.2024.3395663","article-title":"Machine unlearning for seizure prediction","author":"Shao","year":"2024","journal-title":"IEEE Trans Cogn Dev Syst"},{"key":"10.1016\/j.array.2026.100987_b63","series-title":"Machine unlearning of pre-trained large language models","author":"Yao","year":"2024"},{"key":"10.1016\/j.array.2026.100987_b64","article-title":"Machine unlearning: Taxonomy, metrics, applications, challenges, and prospects","author":"Li","year":"2025","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"key":"10.1016\/j.array.2026.100987_b65","series-title":"Machine unlearning: A comprehensive survey","author":"Wang","year":"2024"},{"key":"10.1016\/j.array.2026.100987_b66","series-title":"IEEE INFOCOM 2022-IEEE conference on computer communications","first-page":"1749","article-title":"The right to be forgotten in federated learning: An efficient realization with rapid retraining","author":"Liu","year":"2022"},{"key":"10.1016\/j.array.2026.100987_b67","series-title":"International conference on machine learning","first-page":"1092","article-title":"Machine unlearning for random forests","author":"Brophy","year":"2021"},{"key":"10.1016\/j.array.2026.100987_b68","doi-asserted-by":"crossref","unstructured":"Wang Junxiao, Guo Song, Xie Xin, Qi Heng. Federated unlearning via class-discriminative pruning. In: Proceedings of the ACM web conference 2022. 2022, p. 622\u201332.","DOI":"10.1145\/3485447.3512222"},{"key":"10.1016\/j.array.2026.100987_b69","series-title":"Evaluating machine unlearning via epistemic uncertainty","author":"Becker","year":"2022"},{"key":"10.1016\/j.array.2026.100987_b70","doi-asserted-by":"crossref","unstructured":"Golatkar Aditya, Achille Alessandro, Ravichandran Avinash, Polito Marzia, Soatto Stefano. Mixed-privacy forgetting in deep networks. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition. 2021, p. 792\u2013801.","DOI":"10.1109\/CVPR46437.2021.00085"},{"key":"10.1016\/j.array.2026.100987_b71","doi-asserted-by":"crossref","unstructured":"Sommer David M, Song Liwei, Wagh Sameer, Mittal Prateek. Athena: Probabilistic verification of machine unlearning. In: Proceedings on privacy enhancing technologies. 2022.","DOI":"10.56553\/popets-2022-0072"},{"key":"10.1016\/j.array.2026.100987_b72","series-title":"Federated unlearning","author":"Liu","year":"2020"},{"issue":"1","key":"10.1016\/j.array.2026.100987_b73","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3639304","article-title":"Machine unlearning in learned databases: An experimental analysis","volume":"2","author":"Kurmanji","year":"2024","journal-title":"Proc ACM Manag Data"},{"key":"10.1016\/j.array.2026.100987_b74","doi-asserted-by":"crossref","unstructured":"Yue Xinghui, Peng Jiale, Liu Meiqi, He Haitao, Zhang Yuqing. Research on Machine Unlearning Verification Based on Predictive Cross-Entropy. In: 2024 IEEE smart world congress. SWC, 2024, p. 814\u201320.","DOI":"10.1109\/SWC62898.2024.00139"},{"key":"10.1016\/j.array.2026.100987_b75","series-title":"Delta-influence: Unlearning poisons via influence functions","author":"Li","year":"2024"},{"key":"10.1016\/j.array.2026.100987_b76","doi-asserted-by":"crossref","first-page":"2345","DOI":"10.1109\/TIFS.2023.3265506","article-title":"Zero-shot machine unlearning","volume":"18","author":"Chundawat","year":"2023","journal-title":"IEEE Trans Inf Forensics Secur"},{"key":"10.1016\/j.array.2026.100987_b77","doi-asserted-by":"crossref","unstructured":"Chundawat Vikram S, Tarun Ayush K, Mandal Murari, Kankanhalli Mohan. Can bad teaching induce forgetting? unlearning in deep networks using an incompetent teacher. In: Proceedings of the AAAI conference on artificial intelligence. vol. 37, 2023, p. 7210\u20137.","DOI":"10.1609\/aaai.v37i6.25879"},{"key":"10.1016\/j.array.2026.100987_b78","series-title":"Machine learning based cyber system restoration for IEC 61850 based digital substations","author":"Park","year":"2024"},{"key":"10.1016\/j.array.2026.100987_b79","series-title":"ISO\/IEC 42001:2023 information technology\u2013artificial intelligence\u2013management system","author":"International Organization for Standardization","year":"2023"},{"key":"10.1016\/j.array.2026.100987_b80","series-title":"Regulation (EU) 2024\/1689 artificial intelligence act","author":"European Parliament and Council","year":"2024"},{"key":"10.1016\/j.array.2026.100987_b81","series-title":"Marketing submission recommendations for a predetermined change control plan for artificial intelligence\/machine learning-enabled device software functions","author":"U.S. Food and Drug Administration","year":"2024"},{"key":"10.1016\/j.array.2026.100987_b82","doi-asserted-by":"crossref","first-page":"708","DOI":"10.1109\/TIFS.2023.3328269","article-title":"Verifying in the dark: Verifiable machine unlearning by using invisible backdoor triggers","volume":"19","author":"Guo","year":"2024","journal-title":"IEEE Trans Inf Forensics Secur"},{"key":"10.1016\/j.array.2026.100987_b83","series-title":"Ensuring and facilitating the exercise of data subjects\u2019 rights","author":"Commission Nationale de l\u2019Informatique et des Libert\u00e9s (CNIL)","year":"2025"},{"key":"10.1016\/j.array.2026.100987_b84","unstructured":"CISA, NSA, FBI, et al. Principles for the secure integration of artificial intelligence in operational technology. Joint cybersecurity advisory, 2025."},{"key":"10.1016\/j.array.2026.100987_b85","series-title":"BSI announced as an accredited AI management system certification body","author":"BSI Group","year":"2024"},{"key":"10.1016\/j.array.2026.100987_b86","series-title":"New T\u00dcV S\u00dcD services for efficient implementation of the EU AI Act","author":"T\u00dcV S\u00dcD","year":"2025"},{"key":"10.1016\/j.array.2026.100987_b87","unstructured":"IEEE Standards Association. Joint specification V1.0 for the assessment of the trustworthiness of AI systems. Technical report, 2024."},{"key":"10.1016\/j.array.2026.100987_b88","series-title":"When to forget? Complexity trade-offs in machine unlearning","author":"Waerebeke","year":"2025"},{"key":"10.1016\/j.array.2026.100987_b89","unstructured":"Pawelczyk Martin, Di Jimmy Z, Lu Yiwei, Kamath Gautam, Sekhari Ayush, Neel Seth. Machine Unlearning Fails to Remove Data Poisoning Attacks. In: The thirteenth international conference on learning representations. 2025."},{"issue":"3","key":"10.1016\/j.array.2026.100987_b90","doi-asserted-by":"crossref","first-page":"2150","DOI":"10.1109\/TETCI.2024.3379240","article-title":"Machine unlearning: Solutions and challenges","volume":"8","author":"Xu","year":"2024","journal-title":"IEEE Trans Emerg Top Comput Intell"},{"key":"10.1016\/j.array.2026.100987_b91","series-title":"Proceedings of the 41st international conference on machine learning","article-title":"Verification of machine unlearning is fragile","author":"Zhang","year":"2024"},{"key":"10.1016\/j.array.2026.100987_b92","series-title":"Tight bounds for machine unlearning via differential privacy","author":"Huang","year":"2023"},{"key":"10.1016\/j.array.2026.100987_b93","first-page":"18075","article-title":"Remember what you want to forget: Algorithms for machine unlearning","volume":"34","author":"Sekhari","year":"2021","journal-title":"Adv Neural Inf Process Syst"},{"issue":"489","key":"10.1016\/j.array.2026.100987_b94","doi-asserted-by":"crossref","first-page":"375","DOI":"10.1198\/jasa.2009.tm08651","article-title":"A statistical framework for differential privacy","volume":"105","author":"Wasserman","year":"2010","journal-title":"J Amer Statist Assoc"},{"key":"10.1016\/j.array.2026.100987_b95","series-title":"Advances in cryptology-EUROCRYPT 2006: 24th annual international conference on the theory and applications of cryptographic techniques, st. Petersburg, Russia, May 28-June 1, 2006. Proceedings 25","first-page":"486","article-title":"Our data, ourselves: Privacy via distributed noise generation","author":"Dwork","year":"2006"},{"key":"10.1016\/j.array.2026.100987_b96","series-title":"Theory of cryptography: Third theory of cryptography conference, TCC 2006, New York, NY, USA, March 4-7, 2006. Proceedings 3","first-page":"265","article-title":"Calibrating noise to sensitivity in private data analysis","author":"Dwork","year":"2006"},{"issue":"2","key":"10.1016\/j.array.2026.100987_b97","doi-asserted-by":"crossref","first-page":"280","DOI":"10.1214\/19-STS742","article-title":"Comparative study of differentially private data synthesis methods","volume":"35","author":"Bowen","year":"2020","journal-title":"Statist Sci"},{"key":"10.1016\/j.array.2026.100987_b98","series-title":"Local and central differential privacy for robustness and privacy in federated learning","author":"Naseri","year":"2020"},{"key":"10.1016\/j.array.2026.100987_b99","series-title":"Reviewing and improving the Gaussian mechanism for differential privacy","author":"Zhao","year":"2019"},{"key":"10.1016\/j.array.2026.100987_b100","series-title":"International conference on machine learning","first-page":"2597","article-title":"Optimal differential privacy composition for exponential mechanisms","author":"Dong","year":"2020"},{"key":"10.1016\/j.array.2026.100987_b101","series-title":"2013 IEEE global conference on signal and information processing","first-page":"245","article-title":"Stochastic gradient descent with differentially private updates","author":"Song","year":"2013"},{"key":"10.1016\/j.array.2026.100987_b102","series-title":"2013 IEEE 54th annual symposium on foundations of computer science","first-page":"429","article-title":"Local privacy and statistical minimax rates","author":"Duchi","year":"2013"},{"key":"10.1016\/j.array.2026.100987_b103","unstructured":"Wang Yue, Wu Xintao, Hu Donghui. Using randomized response for differential privacy preserving data collection. In: EDBT\/iCDT workshops. vol. 1558, 2016, p. 0090\u20136778."},{"key":"10.1016\/j.array.2026.100987_b104","series-title":"The 22nd international conference on artificial intelligence and statistics","first-page":"1120","article-title":"Hadamard response: Estimating distributions privately, efficiently, and with little communication","author":"Acharya","year":"2019"},{"key":"10.1016\/j.array.2026.100987_b105","doi-asserted-by":"crossref","unstructured":"Erlingsson \u00dalfar, Pihur Vasyl, Korolova Aleksandra. Rappor: Randomized aggregatable privacy-preserving ordinal response. In: Proceedings of the 2014 ACM SIGSAC conference on computer and communications security. 2014, p. 1054\u201367.","DOI":"10.1145\/2660267.2660348"},{"key":"10.1016\/j.array.2026.100987_b106","series-title":"Proceedings of the thirty-ninth AAAI conference on artificial intelligence and thirty-seventh conference on innovative applications of artificial intelligence and fifteenth symposium on educational advances in artificial intelligence","article-title":"On effects of steering latent representation for large language model unlearning","author":"Huu-Tien","year":"2025"},{"key":"10.1016\/j.array.2026.100987_b107","series-title":"Machine unlearning for LLMs","author":"Martineau","year":"2024"},{"key":"10.1016\/j.array.2026.100987_b108","series-title":"Proceedings of the 63rd annual meeting of the association for computational linguistics (volume 1: long papers)","first-page":"28280","article-title":"Opt-out: Investigating entity-level unlearning for large language models via optimal transport","author":"Choi","year":"2025"},{"issue":"2","key":"10.1016\/j.array.2026.100987_b109","doi-asserted-by":"crossref","first-page":"181","DOI":"10.1038\/s42256-025-00985-0","article-title":"Rethinking machine unlearning for large language models","volume":"7","author":"Liu","year":"2025","journal-title":"Nat Mach Intell"},{"issue":"12","key":"10.1016\/j.array.2026.100987_b110","doi-asserted-by":"crossref","first-page":"399","DOI":"10.1007\/s10462-025-11376-7","article-title":"A survey on large language models unlearning: taxonomy, evaluations, and future directions","volume":"58","author":"Le-Khac","year":"2025","journal-title":"Artif Intell Rev"},{"key":"10.1016\/j.array.2026.100987_b111","series-title":"Proceedings of the 62nd annual meeting of the association for computational linguistics (volume 1: long papers)","first-page":"8403","article-title":"Machine unlearning of pre-trained large language models","author":"Yao","year":"2024"},{"key":"10.1016\/j.array.2026.100987_b112","unstructured":"Zhang Ruiqi, Lin Licong, Bai Yu, Mei Song. Negative Preference Optimization: From Catastrophic Collapse to Effective Unlearning. In: First conference on language modeling. 2024."},{"key":"10.1016\/j.array.2026.100987_b113","series-title":"2025 IEEE conference on secure and trustworthy machine learning","first-page":"520","article-title":"Position: LLM unlearning benchmarks are weak measures of progress","author":"Thaker","year":"2025"},{"key":"10.1016\/j.array.2026.100987_b114","series-title":"Machine unlearning in 2024","author":"Liu","year":"2024"},{"key":"10.1016\/j.array.2026.100987_b115","unstructured":"Maini Pratyush, Feng Zhili, Schwarzschild Avi, Lipton Zachary Chase, Kolter J Zico. TOFU: A Task of Fictitious Unlearning for LLMs. In: First conference on language modeling. 2024."},{"key":"10.1016\/j.array.2026.100987_b116","unstructured":"Li Nathaniel, Pan Alexander, Gopal Anjali, Yue Summer, Berrios Daniel, Gatti Alice, Li Justin D, Dombrowski Ann-Kathrin, Goel Shashwat, Mukobi Gabriel, Helm-Burger Nathan, Lababidi Rassin, Justen Lennart, Liu Andrew Bo, Chen Michael, Barrass Isabelle, Zhang Oliver, Zhu Xiaoyuan, Tamirisa Rishub, Bharathi Bhrugu, Herbert-Voss Ariel, Breuer Cort B, Zou Andy, Mazeika Mantas, Wang Zifan, Oswal Palash, Lin Weiran, Hunt Adam Alfred, Tienken-Harder Justin, Shih Kevin Y, Talley Kemper, Guan John, Steneker Ian, Campbell David, Jokubaitis Brad, Basart Steven, Fitz Stephen, Kumaraguru Ponnurangam, Karmakar Kallol Krishna, Tupakula Uday, Varadharajan Vijay, Shoshitaishvili Yan, Ba Jimmy, Esvelt Kevin M, Wang Alexandr, Hendrycks Dan. The WMDP Benchmark: Measuring and Reducing Malicious Use with Unlearning. In: Forty-first international conference on machine learning. 2024."},{"key":"10.1016\/j.array.2026.100987_b117","unstructured":"Zhang Zhiwei, Wang Fali, Li Xiaomin, Wu Zongyu, Tang Xianfeng, Liu Hui, He Qi, Yin Wenpeng, Wang Suhang. Catastrophic Failure of LLM Unlearning via Quantization. In: The thirteenth international conference on learning representations. 2025."},{"issue":"6","key":"10.1016\/j.array.2026.100987_b118","doi-asserted-by":"crossref","first-page":"1544","DOI":"10.1016\/j.joule.2024.05.009","article-title":"Exploring the capabilities and limitations of large language models in the electric energy sector","volume":"8","author":"Majumder","year":"2024","journal-title":"Joule"},{"key":"10.1016\/j.array.2026.100987_b119","series-title":"Large language models in power systems toward cognitive intelligence: A literature review","author":"Fan","year":"2026"},{"issue":"1","key":"10.1016\/j.array.2026.100987_b120","doi-asserted-by":"crossref","first-page":"8925","DOI":"10.1038\/s41598-025-91940-x","article-title":"A large language model for advanced power dispatch","volume":"15","author":"Cheng","year":"2025","journal-title":"Sci Rep"},{"issue":"1","key":"10.1016\/j.array.2026.100987_b121","article-title":"Large language model for secure operation of power systems","volume":"1","author":"Xiang","year":"2025","journal-title":"Smart Energy Syst Res"},{"key":"10.1016\/j.array.2026.100987_b122","series-title":"Time series forecasting based on optimized LLM for fault prediction in distribution power grid insulators","author":"Matos-Carvalho","year":"2025"},{"issue":"1","key":"10.1016\/j.array.2026.100987_b123","doi-asserted-by":"crossref","DOI":"10.47960\/3029-3200.2025.1.1.10","article-title":"Large language models in power systems: Enhancing control and decision-making","volume":"1","author":"Bernadi\u0107","year":"2025","journal-title":"Int J Innov Solutions Eng"},{"issue":"1","key":"10.1016\/j.array.2026.100987_b124","article-title":"A unified method to revoke the private data of patients in intelligent healthcare with audit to forget","volume":"14","author":"Zhou","year":"2023","journal-title":"Nat Commun"},{"key":"10.1016\/j.array.2026.100987_b125","series-title":"Medical image computing and computer assisted intervention \u2013 MICCAI 2022","first-page":"632","article-title":"Why patient data cannot be easily forgotten?","author":"Su","year":"2022"},{"key":"10.1016\/j.array.2026.100987_b126","series-title":"\u201cForgetting\u201d in machine learning and beyond: A survey","author":"Sha","year":"2024"},{"key":"10.1016\/j.array.2026.100987_b127","article-title":"A survey on machine unlearning: Techniques and new emerged privacy risks","volume":"90","author":"Liu","year":"2025","journal-title":"J Inf Secur Appl"},{"key":"10.1016\/j.array.2026.100987_b128","series-title":"An introduction to machine unlearning","author":"Mercuri","year":"2022"},{"key":"10.1016\/j.array.2026.100987_b129","series-title":"From machine learning to machine unlearning: Complying with GDPR\u2019s right to be forgotten while maintaining business value of predictive models","author":"Yang","year":"2024"},{"issue":"1","key":"10.1016\/j.array.2026.100987_b130","doi-asserted-by":"crossref","first-page":"83","DOI":"10.1007\/s43681-023-00398-y","article-title":"To be forgotten or to be fair: unveiling fairness implications of machine unlearning methods","volume":"4","author":"Zhang","year":"2024","journal-title":"AI Ethics"},{"key":"10.1016\/j.array.2026.100987_b131","series-title":"A survey of machine unlearning","author":"Nguyen","year":"2022"},{"key":"10.1016\/j.array.2026.100987_b132","doi-asserted-by":"crossref","DOI":"10.1016\/j.eswa.2023.121025","article-title":"Selective and collaborative influence function for efficient recommendation unlearning","volume":"234","author":"Li","year":"2023","journal-title":"Expert Syst Appl"},{"key":"10.1016\/j.array.2026.100987_b133","series-title":"Recommendation unlearning via matrix correction","author":"Liu","year":"2023"},{"issue":"3","key":"10.1016\/j.array.2026.100987_b134","doi-asserted-by":"crossref","first-page":"1428","DOI":"10.1109\/TSC.2025.3553709","article-title":"Federated learning with blockchain-enhanced machine unlearning: A trustworthy approach","volume":"18","author":"Zuo","year":"2025","journal-title":"IEEE Trans Serv Comput"},{"issue":"5","key":"10.1016\/j.array.2026.100987_b135","doi-asserted-by":"crossref","first-page":"298","DOI":"10.1109\/MNET.211.2000526","article-title":"Security in IoT-driven mobile edge computing: New paradigms, challenges, and opportunities","volume":"35","author":"Garg","year":"2021","journal-title":"IEEE Netw"},{"key":"10.1016\/j.array.2026.100987_b136","series-title":"Verifiable unlearning on edge","author":"Maheri","year":"2025"},{"key":"10.1016\/j.array.2026.100987_b137","series-title":"Heterogeneous decentralized machine unlearning with seed model distillation","author":"Ye","year":"2023"},{"key":"10.1016\/j.array.2026.100987_b138","series-title":"Hierarchy-aware multimodal unlearning for medical AI","author":"Wu","year":"2026"},{"issue":"12","key":"10.1016\/j.array.2026.100987_b139","doi-asserted-by":"crossref","first-page":"22081","DOI":"10.1109\/JIOT.2024.3378329","article-title":"Toward efficient and robust federated unlearning in IoT networks","volume":"11","author":"Yuan","year":"2024","journal-title":"IEEE Internet Things J"},{"key":"10.1016\/j.array.2026.100987_b140","series-title":"31st USENIX security symposium","first-page":"4007","article-title":"On the necessity of auditable algorithmic definitions for machine unlearning","author":"Thudi","year":"2022"},{"key":"10.1016\/j.array.2026.100987_b141","doi-asserted-by":"crossref","unstructured":"Cooper A Feder, Choquette-Choo Christopher A, Bogen Miranda, Klyman Kevin, Jagielski Matthew, Filippova Katja, Liu Ken, Chouldechova Alexandra, Hayes Jamie, Huang Yangsibo, Triantafillou Eleni, Kairouz Peter, Mitchell Nicole Elyse, Mireshghallah Niloofar, Jacobs Abigail Z, Grimmelmann James, Shmatikov Vitaly, Sa Christopher De, Shumailov Ilia, Terzis Andreas, Barocas Solon, Vaughan Jennifer Wortman, danah boyd, Choi Yejin, Koyejo Sanmi, Delgado Fernando, Liang Percy, Ho Daniel E, Samuelson Pamela, Brundage Miles, Bau David, Neel Seth, Wallach Hanna, Cyphert Amy B, Lemley Mark, Papernot Nicolas, Lee Katherine. Machine Unlearning Doesn\u2019t Do What You Think: Lessons for Generative AI Policy and Research. In: The thirty-ninth annual conference on neural information processing systems position paper track. 2025.","DOI":"10.2139\/ssrn.5060253"},{"key":"10.1016\/j.array.2026.100987_b142","series-title":"Proceedings of the 2022 2nd international conference on business administration and data science","first-page":"466","article-title":"Research on the path of smart grid data assetization","author":"Chen","year":"2023"},{"issue":"9","key":"10.1016\/j.array.2026.100987_b143","doi-asserted-by":"crossref","first-page":"3261","DOI":"10.1108\/K-12-2020-0910","article-title":"Smart grid reliability evaluation and assessment","volume":"52","author":"Mashal","year":"2022","journal-title":"Kybernetes"},{"key":"10.1016\/j.array.2026.100987_b144","doi-asserted-by":"crossref","first-page":"59564","DOI":"10.1109\/ACCESS.2020.3041178","article-title":"Smart grid big data analytics: Survey of technologies, techniques, and applications","volume":"9","author":"Syed","year":"2021","journal-title":"IEEE Access"},{"issue":"4","key":"10.1016\/j.array.2026.100987_b145","doi-asserted-by":"crossref","first-page":"508","DOI":"10.1049\/iet-stg.2019.0298","article-title":"Adapting big data standards, maturity models to smart grid distributed generation: critical review","volume":"3","author":"Sundararajan","year":"2020","journal-title":"IET Smart Grid"},{"issue":"2","key":"10.1016\/j.array.2026.100987_b146","doi-asserted-by":"crossref","first-page":"001","DOI":"10.30574\/wjarr.2023.18.2.0783","article-title":"Investigating the impact of cyber security risks and reliability scenarios under the influence of IoT on the smart grid environment","volume":"18","author":"Khan","year":"2023","journal-title":"World J Adv Res Rev"},{"issue":"2","key":"10.1016\/j.array.2026.100987_b147","doi-asserted-by":"crossref","first-page":"588","DOI":"10.1108\/IJQRM-10-2020-0346","article-title":"Implementing asset data management in power companies","volume":"39","author":"Gavrikova","year":"2021","journal-title":"Int J Qual Reliab Manag"},{"key":"10.1016\/j.array.2026.100987_b148","series-title":"Smart cities - Their framework and applications","article-title":"Data compression strategies for use in advanced metering infrastructure networks","author":"Hsu","year":"2021"},{"key":"10.1016\/j.array.2026.100987_b149","doi-asserted-by":"crossref","first-page":"34","DOI":"10.1016\/j.is.2018.05.007","article-title":"Scalable prediction-based online anomaly detection for smart meter data","volume":"77","author":"Liu","year":"2018","journal-title":"Inf Syst"},{"issue":"2","key":"10.1016\/j.array.2026.100987_b150","doi-asserted-by":"crossref","first-page":"535","DOI":"10.36478\/jeasci.2020.535.547","article-title":"A review on smart energy grid technology: Features and specifications","volume":"15","author":"A. Mahmoud","year":"2019","journal-title":"J Eng Appl Sci"},{"issue":"1","key":"10.1016\/j.array.2026.100987_b151","doi-asserted-by":"crossref","DOI":"10.1186\/s42162-023-00266-3","article-title":"The determinants of reliable smart grid from experts\u2019 perspective","volume":"6","author":"Mashal","year":"2023","journal-title":"Energy Inform"},{"key":"10.1016\/j.array.2026.100987_b152","doi-asserted-by":"crossref","DOI":"10.3389\/fenrg.2023.1254371","article-title":"Smart grid energy storage capacity planning and scheduling optimization through PSO-GRU and multihead-attention","volume":"11","author":"Xiao","year":"2023","journal-title":"Front Energy Res"},{"key":"10.1016\/j.array.2026.100987_b153","series-title":"A review of fault-tolerant data aggregation schemes for smart grid environment","author":"Rani","year":"2024"},{"issue":"17","key":"10.1016\/j.array.2026.100987_b154","doi-asserted-by":"crossref","first-page":"6140","DOI":"10.3390\/en15176140","article-title":"Edge computing for IoT-enabled smart grid: The future of energy","volume":"15","author":"Minh","year":"2022","journal-title":"Energies"},{"issue":"1","key":"10.1016\/j.array.2026.100987_b155","doi-asserted-by":"crossref","DOI":"10.1186\/s40537-019-0270-8","article-title":"Evaluation of big data frameworks for analysis of smart grids","volume":"6","author":"Ansari","year":"2019","journal-title":"J Big Data"},{"key":"10.1016\/j.array.2026.100987_b156","series-title":"Proceedings of the 2nd international symposium on computer science and intelligent control","first-page":"1","article-title":"On the challenges and opportunities of smart meters in smart homes and smart grids","author":"Al-Waisi","year":"2018"},{"key":"10.1016\/j.array.2026.100987_b157","series-title":"2024: proceedings of social science and humanities research association","first-page":"196","article-title":"Copyright issue in artificial intelligence applications of smart production and autonomous systems","author":"Alparslan","year":"2024"},{"issue":"3","key":"10.1016\/j.array.2026.100987_b158","doi-asserted-by":"crossref","first-page":"3272","DOI":"10.1002\/er.7381","article-title":"Smart contract formation enabling energy-as-a-service in a virtual power plant","volume":"46","author":"Mishra","year":"2021","journal-title":"Int J Energy Res"},{"key":"10.1016\/j.array.2026.100987_b159","series-title":"Information technology - New generations","first-page":"199","article-title":"Evaluation of cybersecurity threats on smart metering system","author":"Tweneboah-Koduah","year":"2017"},{"issue":"1","key":"10.1016\/j.array.2026.100987_b160","article-title":"Optimum systems integration architecture for monitoring to manage an electricity utility","volume":"24","author":"Pokane","year":"2022","journal-title":"SA J Inf Manag"},{"issue":"2","key":"10.1016\/j.array.2026.100987_b161","doi-asserted-by":"crossref","first-page":"1243","DOI":"10.1109\/JIOT.2020.3026692","article-title":"Efficient privacy-preserving electricity theft detection with dynamic billing and load monitoring for AMI networks","volume":"8","author":"Ibrahem","year":"2021","journal-title":"IEEE Internet Things J"},{"issue":"14","key":"10.1016\/j.array.2026.100987_b162","first-page":"1","article-title":"A concise model to evaluate security of SCADA systems based on security standards","volume":"111","author":"Aghajanzadeh","year":"2015","journal-title":"Int J Comput Appl"},{"issue":"2","key":"10.1016\/j.array.2026.100987_b163","doi-asserted-by":"crossref","DOI":"10.3390\/technologies5020012","article-title":"Research and application of a SCADA system for a microgrid","volume":"5","author":"Li","year":"2017","journal-title":"Technologies"},{"key":"10.1016\/j.array.2026.100987_b164","doi-asserted-by":"crossref","first-page":"01004","DOI":"10.1051\/e3sconf\/201911401004","article-title":"System approach to development of intellectual information mobile system for electric power metering","volume":"114","author":"Vorozhtsova","year":"2019","journal-title":"E3S Web Conf"},{"issue":"1","key":"10.1016\/j.array.2026.100987_b165","article-title":"A SCADA\/PMU hybrid measurement state estimation method considering load uncertainty","volume":"983","author":"Huang","year":"2022","journal-title":"IOP Conf Ser: Earth Environ Sci"},{"issue":"1","key":"10.1016\/j.array.2026.100987_b166","doi-asserted-by":"crossref","first-page":"161","DOI":"10.12700\/APH.17.1.2020.1.9","article-title":"Active and reactive power losses in distribution transformers","volume":"17","author":"Kolcun","year":"2020","journal-title":"Acta Polytech Hung"},{"key":"10.1016\/j.array.2026.100987_b167","series-title":"2015 IEEE sensors applications symposium","first-page":"1","article-title":"Non-intrusive zigbee power meter for load monitoring in smart buildings","author":"Balsamo","year":"2015"},{"key":"10.1016\/j.array.2026.100987_b168","doi-asserted-by":"crossref","first-page":"280","DOI":"10.1016\/j.rser.2016.04.046","article-title":"Households\u2019 willingness to pay for reliable electricity services in Ghana","volume":"62","author":"Taale","year":"2016","journal-title":"Renew Sustain Energy Rev"},{"key":"10.1016\/j.array.2026.100987_b169","doi-asserted-by":"crossref","first-page":"02003","DOI":"10.1051\/e3sconf\/202343302003","article-title":"Improving net energy metering (NEM) actual load prediction accuracy using an adaptive learning rate LSTM model for residential use case","volume":"433","author":"Kunalan","year":"2023","journal-title":"E3S Web Conf"},{"issue":"2","key":"10.1016\/j.array.2026.100987_b170","doi-asserted-by":"crossref","first-page":"796","DOI":"10.3390\/app11020796","article-title":"Impact of dataset size on classification performance: An empirical evaluation in the medical domain","volume":"11","author":"Althnian","year":"2021","journal-title":"Appl Sci"},{"issue":"1","key":"10.1016\/j.array.2026.100987_b171","doi-asserted-by":"crossref","first-page":"103","DOI":"10.3849\/aimt.01794","article-title":"Detection of malicious network activity by artificial neural network","volume":"18","author":"Tur\u010dan\u00edk","year":"2023","journal-title":"Adv Mil Technol"},{"key":"10.1016\/j.array.2026.100987_b172","series-title":"Synthetic data for artificial intelligence and machine learning: Tools, techniques, and applications","first-page":"23","article-title":"Cyber creative GAN for novel malicious packets","author":"Pavlik","year":"2023"},{"key":"10.1016\/j.array.2026.100987_b173","series-title":"Machine unlearning via GAN","author":"Chen","year":"2021"},{"key":"10.1016\/j.array.2026.100987_b174","series-title":"Proceedings of the 2021 ACM SIGSAC conference on computer and communications security","first-page":"896","article-title":"When machine unlearning jeopardizes privacy","author":"Chen","year":"2021"},{"issue":"11","key":"10.1016\/j.array.2026.100987_b175","first-page":"12945","article-title":"Wind prediction under random data corruption (student abstract)","volume":"36","author":"Flansburg","year":"2022","journal-title":"Proc AAAI Conf Artif Intell"},{"key":"10.1016\/j.array.2026.100987_b176","series-title":"Detecting backdoor attacks on deep neural networks by activation clustering","author":"Chen","year":"2018"},{"key":"10.1016\/j.array.2026.100987_b177","series-title":"2020 IEEE symposium on security and privacy","first-page":"1190","article-title":"Throwing darts in the dark? Detecting bots with limited data using neural data augmentation","author":"Jan","year":"2020"},{"key":"10.1016\/j.array.2026.100987_b178","series-title":"Computer security \u2013 ESORICS 2020","first-page":"480","article-title":"Data poisoning attacks against federated learning systems","author":"Tolpegin","year":"2020"},{"key":"10.1016\/j.array.2026.100987_b179","series-title":"ECML PKDD 2018 workshops","first-page":"5","article-title":"Label sanitization against label flipping poisoning attacks","author":"Paudice","year":"2019"},{"key":"10.1016\/j.array.2026.100987_b180","series-title":"Proceedings 2024 network and distributed system security symposium","article-title":"Low-quality training data only? A robust framework for detecting encrypted malicious network traffic","author":"Qing","year":"2024"},{"issue":"21","key":"10.1016\/j.array.2026.100987_b181","doi-asserted-by":"crossref","first-page":"8928","DOI":"10.3390\/s23218928","article-title":"AI and blockchain-based secure data dissemination architecture for IoT-enabled critical infrastructure","volume":"23","author":"Rathod","year":"2023","journal-title":"Sensors"},{"key":"10.1016\/j.array.2026.100987_b182","series-title":"Proceedings of the 2019 ACM SIGSAC conference on computer and communications security","article-title":"Latent backdoor attacks on deep neural networks","author":"Yao","year":"2019"},{"key":"10.1016\/j.array.2026.100987_b183","series-title":"2019 IEEE power & energy society general meeting","first-page":"1","article-title":"Enabling cyberattack-resilient load forecasting through adversarial machine learning","author":"Tang","year":"2019"},{"key":"10.1016\/j.array.2026.100987_b184","series-title":"Adaptive autonomous secure cyber systems","first-page":"23","article-title":"Defending against machine learning based inference attacks via adversarial examples: Opportunities and challenges","author":"Jia","year":"2020"},{"key":"10.1016\/j.array.2026.100987_b185","series-title":"Witches\u2019 brew: Industrial scale data poisoning via gradient matching","author":"Geiping","year":"2021"},{"issue":"5","key":"10.1016\/j.array.2026.100987_b186","doi-asserted-by":"crossref","DOI":"10.1088\/1742-5468\/ab11e3","article-title":"On the number of limit cycles in asymmetric neural networks","volume":"2019","author":"Hwang","year":"2019","journal-title":"J Stat Mech Theory Exp"},{"key":"10.1016\/j.array.2026.100987_b187","doi-asserted-by":"crossref","first-page":"06002","DOI":"10.1051\/matecconf\/20165506002","article-title":"Impact of wind speed correlation on the operation of energy storage systems","volume":"55","author":"Davril","year":"2016","journal-title":"MATEC Web Conf"},{"issue":"21","key":"10.1016\/j.array.2026.100987_b188","doi-asserted-by":"crossref","first-page":"5627","DOI":"10.1049\/iet-gtd.2018.5221","article-title":"Energy storage management strategy in distribution networks utilised by photovoltaic resources","volume":"12","author":"Azizivahed","year":"2018","journal-title":"IET Gener Transm Distribution"},{"issue":"1","key":"10.1016\/j.array.2026.100987_b189","article-title":"Predefined time backstepping control of photovoltaic grid connected inverter","volume":"2708","author":"Xu","year":"2024","journal-title":"J Phys: Conf Ser"},{"issue":"9","key":"10.1016\/j.array.2026.100987_b190","doi-asserted-by":"crossref","first-page":"9700","DOI":"10.1109\/TPEL.2020.2965941","article-title":"Unified modular state-space modeling of grid-connected voltage-source converters","volume":"35","author":"Yang","year":"2020","journal-title":"IEEE Trans Power Electron"},{"key":"10.1016\/j.array.2026.100987_b191","series-title":"2018 IEEE international conference on power electronics, drives and energy systems","first-page":"1","article-title":"Impact of PLL on harmonic stability of renewable dominated power system: Modeling and analysis","author":"Priyamvada","year":"2018"},{"issue":"1","key":"10.1016\/j.array.2026.100987_b192","doi-asserted-by":"crossref","first-page":"310","DOI":"10.1109\/TIE.2014.2334665","article-title":"Analysis of phase-locked loop low-frequency stability in three-phase grid-connected power converters considering impedance interactions","volume":"62","author":"Dong","year":"2015","journal-title":"IEEE Trans Ind Electron"},{"key":"10.1016\/j.array.2026.100987_b193","series-title":"Voltage stability enhancement in lebanese islanded hybrid microgrid: The role of shunt capacitor banks and synchronous generator droop control","author":"Koubayssi","year":"2024"},{"issue":"8","key":"10.1016\/j.array.2026.100987_b194","doi-asserted-by":"crossref","first-page":"9459","DOI":"10.1109\/TPEL.2023.3270640","article-title":"Stability constraints on reliability-oriented control of AC microgrids \u2013 Theoretical margin and solutions","volume":"38","author":"Song","year":"2023","journal-title":"IEEE Trans Power Electron"},{"key":"10.1016\/j.array.2026.100987_b195","doi-asserted-by":"crossref","DOI":"10.4108\/ew.4896","article-title":"The residual life prediction of power grid transformers based on GA-ELM computational model and digital twin data","volume":"11","author":"Wang","year":"2024","journal-title":"EAI Endorsed Trans Energy Web"},{"key":"10.1016\/j.array.2026.100987_b196","doi-asserted-by":"crossref","DOI":"10.1088\/1757-899X\/124\/1\/012056","article-title":"Mathematical simulation of the operating emergency conditions for the purpose of energy efficiency increase of thermal power plants management","volume":"124","author":"Gazizova","year":"2016","journal-title":"IOP Conf Ser: Mater Sci Eng"},{"key":"10.1016\/j.array.2026.100987_b197","doi-asserted-by":"crossref","first-page":"39041","DOI":"10.1109\/ACCESS.2024.3367727","article-title":"Robust H-infinity filter and PSO-SVM based monitoring of power quality disturbances system","volume":"12","author":"Ray","year":"2024","journal-title":"IEEE Access"},{"key":"10.1016\/j.array.2026.100987_b198","series-title":"Proceedings of the 53rd hawaii international conference on system sciences","doi-asserted-by":"crossref","DOI":"10.24251\/HICSS.2020.367","article-title":"A study of the impact of reduced inertia in power systems","author":"Agrawal","year":"2020"},{"issue":"15","key":"10.1016\/j.array.2026.100987_b199","doi-asserted-by":"crossref","first-page":"7375","DOI":"10.3390\/app12157375","article-title":"A probabilistic framework for the robust stability and performance analysis of grid-tied voltage source converters","volume":"12","author":"Gholami-Khesht","year":"2022","journal-title":"Appl Sci"},{"key":"10.1016\/j.array.2026.100987_b200","series-title":"2018 IEEE power & energy society general meeting","article-title":"Frequency response study of U.S. western interconnection under extra-high photovoltaic generation penetrations","author":"Tan","year":"2018"},{"key":"10.1016\/j.array.2026.100987_b201","doi-asserted-by":"crossref","first-page":"29591","DOI":"10.1109\/ACCESS.2023.3260778","article-title":"Dynamics and stability of power systems with high shares of grid-following inverter-based resources: A tutorial","volume":"11","author":"Sajadi","year":"2023","journal-title":"IEEE Access"},{"key":"10.1016\/j.array.2026.100987_b202","doi-asserted-by":"crossref","DOI":"10.1109\/TPEL.2016.2542244","article-title":"Couplings in phase domain impedance modelling of grid-connected converters","author":"Bakhshizadeh","year":"2016","journal-title":"IEEE Trans Power Electron"},{"key":"10.1016\/j.array.2026.100987_b203","series-title":"2018 power systems computation conference","article-title":"Foundations and challenges of low-inertia systems (invited paper)","author":"Milano","year":"2018"},{"key":"10.1016\/j.array.2026.100987_b204","series-title":"2020 IEEE international conference on big data (big data)","first-page":"878","article-title":"Drift-aware multi-memory model for imbalanced data streams","author":"Abolfazli","year":"2020"},{"issue":"1","key":"10.1016\/j.array.2026.100987_b205","first-page":"105","article-title":"Comprehensive analysis of power system: Exploring load factor, power balance, active load variation, and increment factors with iterative implications","volume":"15","author":"Bislimi","year":"2024","journal-title":"Int J Electr Comput Eng Syst"},{"key":"10.1016\/j.array.2026.100987_b206","series-title":"Findings of the association for computational linguistics: ACL 2025","first-page":"20582","article-title":"CLEAR: Character unlearning in textual and visual modalities","author":"Dontsov","year":"2025"},{"key":"10.1016\/j.array.2026.100987_b207","doi-asserted-by":"crossref","DOI":"10.1016\/j.apenergy.2025.126670","article-title":"A systematic review of transformers and large language models in the energy sector: towards agentic digital twins","volume":"401","author":"Antonesi","year":"2025","journal-title":"Appl Energy"},{"key":"10.1016\/j.array.2026.100987_b208","series-title":"Online learning and unlearning","author":"Hu","year":"2025"},{"key":"10.1016\/j.array.2026.100987_b209","series-title":"Proceedings of the 34th international conference on neural information processing systems","article-title":"Variational Bayesian unlearning","author":"Nguyen","year":"2020"},{"key":"10.1016\/j.array.2026.100987_b210","series-title":"Bayesian inference forgetting","author":"Fu","year":"2021"},{"issue":"1","key":"10.1016\/j.array.2026.100987_b211","doi-asserted-by":"crossref","first-page":"9614","DOI":"10.1038\/s41467-025-64601-w","article-title":"Bayesian continual learning and forgetting in neural networks","volume":"16","author":"Bonnet","year":"2025","journal-title":"Nat Commun"},{"key":"10.1016\/j.array.2026.100987_b212","series-title":"Adaptive filter theory","author":"Haykin","year":"2013"},{"key":"10.1016\/j.array.2026.100987_b213","doi-asserted-by":"crossref","unstructured":"Lai Brian, Bernstein Dennis S. Adaptive Kalman Filtering Developed from Recursive Least Squares Forgetting Algorithms. In: 2024 American control conference. ACC, 2024, p. 4378\u201383.","DOI":"10.23919\/ACC60939.2024.10644929"},{"issue":"9","key":"10.1016\/j.array.2026.100987_b214","article-title":"Vehicle state estimation by integrating the recursive least squares method with a variable forgetting factor with an adaptive iterative extended Kalman filter","volume":"15","author":"Chen","year":"2024","journal-title":"World Electr Veh J"},{"key":"10.1016\/j.array.2026.100987_b215","series-title":"Efficient machine unlearning via influence approximation","author":"Liu","year":"2025"},{"key":"10.1016\/j.array.2026.100987_b216","series-title":"Machine unlearning for streaming forgetting","author":"Shen","year":"2025"},{"key":"10.1016\/j.array.2026.100987_b217","series-title":"Proceedings of the 37th international conference on neural information processing systems","article-title":"Towards unbounded machine unlearning","author":"Kurmanji","year":"2023"},{"key":"10.1016\/j.array.2026.100987_b218","doi-asserted-by":"crossref","first-page":"54","DOI":"10.1016\/j.neunet.2019.01.012","article-title":"Continual lifelong learning with neural networks: A review","volume":"113","author":"Parisi","year":"2019","journal-title":"Neural Netw"},{"issue":"2, Part A","key":"10.1016\/j.array.2026.100987_b219","doi-asserted-by":"crossref","DOI":"10.1016\/j.ipm.2025.104417","article-title":"Core unlearning: A multi-modal gradient-efficient architecture for exact and approximate model rewriting","volume":"63","author":"Iqbal","year":"2026","journal-title":"Inf Process Manage"},{"key":"10.1016\/j.array.2026.100987_b220","series-title":"Road vehicles \u2014 Safety and artificial intelligence","author":"ISO","year":"2024"},{"key":"10.1016\/j.array.2026.100987_b221","article-title":"The price of unlearning: Identifying unlearning risk in edge computing","author":"Zhang","year":"2024","journal-title":"ACM Trans Multimed Comput Commun Appl"},{"key":"10.1016\/j.array.2026.100987_b222","series-title":"Blockchain: Blueprint for a new economy","author":"Swan","year":"2015"},{"key":"10.1016\/j.array.2026.100987_b223","doi-asserted-by":"crossref","unstructured":"Zheng Zibin, Xie Shaoan, Dai Hongning, Chen Xiangping, Wang Huaimin. An Overview of Blockchain Technology: Architecture, Consensus, and Future Trends. In: 2017 IEEE international congress on big data. BigData Congress, 2017, p. 557\u201364.","DOI":"10.1109\/BigDataCongress.2017.85"},{"key":"10.1016\/j.array.2026.100987_b224","series-title":"Blockchain technology overview","author":"Yaga","year":"2018"},{"key":"10.1016\/j.array.2026.100987_b225","series-title":"Proceedings of the thirteenth EuroSys conference","article-title":"Hyperledger fabric: a distributed operating system for permissioned blockchains","author":"Androulaki","year":"2018"}],"container-title":["Array"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S2590005626003103?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S2590005626003103?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,7,26]],"date-time":"2026-07-26T14:05:12Z","timestamp":1785074712000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S2590005626003103"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,9]]},"references-count":225,"alternative-id":["S2590005626003103"],"URL":"https:\/\/doi.org\/10.1016\/j.array.2026.100987","relation":{},"ISSN":["2590-0056"],"issn-type":[{"value":"2590-0056","type":"print"}],"subject":[],"published":{"date-parts":[[2026,9]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"A survey of machine unlearning for electric power systems: From privacy compliance to resilient grid operations","name":"articletitle","label":"Article Title"},{"value":"Array","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.array.2026.100987","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 The Authors. Published by Elsevier Inc.","name":"copyright","label":"Copyright"}],"article-number":"100987"}}