{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,29]],"date-time":"2026-09-29T17:22:23Z","timestamp":1790702543791,"version":"4.1.0"},"reference-count":31,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2025,8,22]],"date-time":"2025-08-22T00:00:00Z","timestamp":1755820800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Natural Science Foundation of China","award":["62103132"],"award-info":[{"award-number":["62103132"]}]},{"name":"National Natural Science Foundation of China","award":["62003132"],"award-info":[{"award-number":["62003132"]}]},{"name":"National Natural Science Foundation of China","award":["BK20241779"],"award-info":[{"award-number":["BK20241779"]}]},{"name":"National Natural Science Foundation of China","award":["202402AF080006"],"award-info":[{"award-number":["202402AF080006"]}]},{"name":"Natural Science Foundation of Jiangsu Province","award":["62103132"],"award-info":[{"award-number":["62103132"]}]},{"name":"Natural Science Foundation of Jiangsu Province","award":["62003132"],"award-info":[{"award-number":["62003132"]}]},{"name":"Natural Science Foundation of Jiangsu Province","award":["BK20241779"],"award-info":[{"award-number":["BK20241779"]}]},{"name":"Natural Science Foundation of Jiangsu Province","award":["202402AF080006"],"award-info":[{"award-number":["202402AF080006"]}]},{"name":"Major Special Science and Technology Project of Yunnan Province","award":["62103132"],"award-info":[{"award-number":["62103132"]}]},{"name":"Major Special Science and Technology Project of Yunnan Province","award":["62003132"],"award-info":[{"award-number":["62003132"]}]},{"name":"Major Special Science and Technology Project of Yunnan Province","award":["BK20241779"],"award-info":[{"award-number":["BK20241779"]}]},{"name":"Major Special Science and Technology Project of Yunnan Province","award":["202402AF080006"],"award-info":[{"award-number":["202402AF080006"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Entropy"],"abstract":"<jats:p>To address the impact of distributed energy resource volatility on distribution network fault restoration, this paper proposes a strategy that incorporates net load forecasting. A Bayesian-optimized long short-term memory neural network is used to accurately predict the net load within fault-affected areas, achieving an R2 of 0.9569 and an RMSE of 12.15 kW. Based on the forecasting results, a fast restoration optimization model is established, with objectives to maximize critical load recovery, minimize switching operations, and reduce network losses. The model is solved using a genetic algorithm enhanced with quantum particle swarm optimization (GA-QPSO), a hybrid metaheuristic known for its superior global exploration and local refinement capabilities. GA-QPSO has been successfully applied in various power system optimization problems, including service restoration, network reconfiguration, and distributed generation planning, owing to its effectiveness in navigating large, complex solution spaces. Simulation results on the IEEE 33-bus system show that the proposed method reduces network losses by 33.2%, extends the power supply duration from 60 to 120 min, and improves load recovery from 72.7% to 75.8%, demonstrating enhanced accuracy and efficiency of the restoration process.<\/jats:p>","DOI":"10.3390\/e27090888","type":"journal-article","created":{"date-parts":[[2025,8,22]],"date-time":"2025-08-22T12:31:26Z","timestamp":1755865886000},"page":"888","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Fault Recovery Strategy with Net Load Forecasting Using Bayesian Optimized LSTM for Distribution Networks"],"prefix":"10.3390","volume":"27","author":[{"given":"Zekai","family":"Ding","sequence":"first","affiliation":[{"name":"College of Artificial Intelligence and Automation, Hohai University, Nanjing 210024, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yundi","family":"Chu","sequence":"additional","affiliation":[{"name":"College of Artificial Intelligence and Automation, Hohai University, Nanjing 210024, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2025,8,22]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Xie, M., Lin, S., Dong, K., and Zhang, S. (2023). Short-Term Prediction of Multi-Energy Loads Based on Copula Correlation Analysis and Model Fusions. Entropy, 25.","DOI":"10.20944\/preprints202308.0309.v1"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Wang, F., Cetinay, H., He, Z., Liu, L., Van Mieghem, P., and Kooij, R.E. (2023). Recovering Power Grids Using Strategies Based on Network Metrics and Greedy Algorithms. Entropy, 25.","DOI":"10.3390\/e25101455"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"109153","DOI":"10.1016\/j.ijepes.2023.109153","article-title":"Coordinated restoration method of transmission and distribution network considering distribution network fault repair scenario","volume":"151","author":"Bai","year":"2023","journal-title":"Int. J. Electr. Power Energy Syst."},{"key":"ref_4","first-page":"100976","article-title":"Optimal restoration algorithm for active distribution network considering island mode of the microgrids, e-Prime\u2014Advances in Electrical Engineering","volume":"12","author":"Quintero","year":"2025","journal-title":"Electron. Energy"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"6014","DOI":"10.1109\/TPWRS.2023.3341725","article-title":"Power System Recovery from Momentary Cessation with Transient Stability Improvement","volume":"39","author":"Savastianov","year":"2024","journal-title":"IEEE Trans. Power Syst."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"132708","DOI":"10.1109\/ACCESS.2023.3336163","article-title":"Optimum Cable Sizing for Offshore Wind Farms Using Binary Integer Programming","volume":"11","author":"Bae","year":"2023","journal-title":"IEEE Access"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"101575","DOI":"10.1016\/j.segan.2024.101575","article-title":"Emergency power supply scheme and fault repair strategy for distribution networks considering electric -traffic synergy","volume":"40","author":"Yang","year":"2024","journal-title":"Sustain. Energy Grids Netw."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Wang, W., Zhang, K., Lv, S., Zheng, Z., Li, G., and Bai, L. (2025). Research and Application of New Fast Self-Healing Technology of Distribution Network Based on Line Topology and Beat Frequency Attenuation. Results in Engineering., 106641.","DOI":"10.1016\/j.rineng.2025.106641"},{"key":"ref_9","first-page":"16","article-title":"Dynamic Fault Recovery Strategy for Distribution Network Based on Improved Simulated Annealing Genetic Algorithm","volume":"52","author":"Wang","year":"2024","journal-title":"Smart Grid"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"111675","DOI":"10.1016\/j.epsr.2025.111675","article-title":"Distributed coordinated service restoration strategy for transmission and distribution networks based on distribution network partition","volume":"246","author":"Zhao","year":"2025","journal-title":"Electr. Power Syst. Res."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"4644","DOI":"10.1109\/TPWRS.2017.2681459","article-title":"Optimal Allocation of Dynamic Var Sources Using the Voronoi Diagram Method Integrating Linear Programing","volume":"32","author":"Huang","year":"2017","journal-title":"IEEE Trans. Power Syst."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"110181","DOI":"10.1016\/j.ijepes.2024.110181","article-title":"A multi-period restoration approach for resilience increase of active distribution networks by considering fault rapid recovery and component repair","volume":"161","author":"Chen","year":"2024","journal-title":"Int. J. Electr. Power Energy Syst."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"2604","DOI":"10.1016\/j.egypro.2017.12.199","article-title":"Reliability improvement using ant colony optimization applied to placement of sectionalizing switches","volume":"142","author":"Zhang","year":"2017","journal-title":"Energy Procedia"},{"key":"ref_14","first-page":"24","article-title":"Dynamic fault recovery method of a photovoltaic distribution network considering switch state set adjustment","volume":"49","author":"Liu","year":"2021","journal-title":"Power Syst. Prot. Control"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"14","DOI":"10.1016\/j.ref.2018.07.009","article-title":"Stability assessment and optimal state-feedback system design for PQ-Vf coordination in a PV\/DFIG\/DSG based microgrid","volume":"27","author":"Nayak","year":"2018","journal-title":"Renew. Energy Focus"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"106182","DOI":"10.1016\/j.asoc.2020.106182","article-title":"A multi-objective evolutionary approach for planning and optimal condition restoration of secondary distribution networks","volume":"90","author":"Micheloud","year":"2020","journal-title":"Appl. Soft Comput."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"100182","DOI":"10.1016\/j.dajour.2023.100182","article-title":"A hybrid moth\u2013flame algorithm with particle swarm optimization with application in power transmission and distribution","volume":"6","author":"Shaikh","year":"2023","journal-title":"Decis. Anal. J."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"2517","DOI":"10.1016\/j.eswa.2014.11.003","article-title":"A hybrid evolutionary computation approach with its application for optimizing text document clustering","volume":"42","author":"Song","year":"2015","journal-title":"Expert Syst. Appl."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"239","DOI":"10.1016\/j.cjche.2024.07.011","article-title":"Town gas daily load forecasting based on machine learning combinatorial algorithms: A case study in North Chin","volume":"75","author":"Xu","year":"2024","journal-title":"Chin. J. Chem. Eng."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"9230","DOI":"10.1007\/s10489-024-05626-4","article-title":"Achieving accurate and balanced regional electric vehicle charging load forecasting with a dynamic road network: A case study of Lanzhou City","volume":"54","author":"Li","year":"2024","journal-title":"Appl. Intell."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"124786","DOI":"10.1016\/j.apenergy.2024.124786","article-title":"Learning global and local features of power load series through transformer and 2D-CNN: An image-based multi-step forecasting approach incorporating phase space reconstruction","volume":"378","author":"Tang","year":"2025","journal-title":"Appl. Energy"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"111330","DOI":"10.1016\/j.epsr.2024.111330","article-title":"Improved composite model using metaheuristic optimization algorithm for short-term power load forecasting","volume":"241","author":"Hu","year":"2025","journal-title":"Electr. Power Syst. Res."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"136265","DOI":"10.1016\/j.energy.2025.136265","article-title":"An Informer-BiGRU-temporal attention multi-step wind speed prediction model based on spatial-temporal dimension denoising and combined VMD decomposition","volume":"326","author":"Fu","year":"2025","journal-title":"Energy"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Liu, X., Song, J., Tao, H., Wang, P., Mo, H., and Du, W. (2025). Quarter-Hourly Power Load Forecasting Based on a Hybrid CNN-BiLSTM-Attention Model with CEEMDAN, K-Means, and VMD. Energies, 18.","DOI":"10.3390\/en18112675"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"136117","DOI":"10.1016\/j.energy.2025.136117","article-title":"Efficient techno-economic optimization of integrated energy systems using Bayesian optimization","volume":"326","author":"Griffith","year":"2025","journal-title":"Energy"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Lin, S., Wang, Y., Wei, H., Wang, X., and Wang, Z. (2025). Hybrid Method for Oil Price Prediction Based on Feature Selection and XGBOOST-LSTM. Energies, 18.","DOI":"10.3390\/en18092246"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"577","DOI":"10.1038\/s41597-022-01696-6","article-title":"Solar and wind power data from the Chinese State Grid Renewable Energy Generation Forecasting Competition","volume":"9","author":"Chen","year":"2022","journal-title":"Sci. Data"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Duan, Y., Gao, C., Xu, Z., Ren, S., and Wu, D. (2025). Multi-Objective Optimization for the Low-Carbon Operation of Integrated Energy Systems Based on an Improved Genetic Algorithm. Energies, 18.","DOI":"10.3390\/en18092283"},{"key":"ref_29","first-page":"136","article-title":"Coordination of Distribution Network Fault Reconfiguration and Islanded Operation Based on Hybrid Algorithm","volume":"39","author":"Zhou","year":"2015","journal-title":"Power Syst. Technol."},{"key":"ref_30","first-page":"128","article-title":"Operation Strategy for Distribution Network Fault Restoration Considering Islanding and Reconfiguration","volume":"39","author":"Ma","year":"2018","journal-title":"Electr. Power Constr."},{"key":"ref_31","first-page":"7463","article-title":"A Post-Disaster Centralized Matching and Decentralized Dispatch Strategy for Emergency Resources in Urban Power Network","volume":"39","author":"Wan","year":"2024","journal-title":"Trans. China Electrotech. Soc."}],"container-title":["Entropy"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1099-4300\/27\/9\/888\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,9]],"date-time":"2025-10-09T18:33:48Z","timestamp":1760034828000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1099-4300\/27\/9\/888"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,8,22]]},"references-count":31,"journal-issue":{"issue":"9","published-online":{"date-parts":[[2025,9]]}},"alternative-id":["e27090888"],"URL":"https:\/\/doi.org\/10.3390\/e27090888","relation":{},"ISSN":["1099-4300"],"issn-type":[{"value":"1099-4300","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,8,22]]}}}