{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,23]],"date-time":"2025-12-23T16:54:29Z","timestamp":1766508869019,"version":"3.48.0"},"reference-count":55,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,3,1]],"date-time":"2026-03-01T00:00:00Z","timestamp":1772323200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,3,1]],"date-time":"2026-03-01T00:00:00Z","timestamp":1772323200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,3,1]],"date-time":"2026-03-01T00:00:00Z","timestamp":1772323200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2026,3,1]],"date-time":"2026-03-01T00:00:00Z","timestamp":1772323200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2026,3,1]],"date-time":"2026-03-01T00:00:00Z","timestamp":1772323200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2026,3,1]],"date-time":"2026-03-01T00:00:00Z","timestamp":1772323200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,3,1]],"date-time":"2026-03-01T00:00:00Z","timestamp":1772323200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62073084"],"award-info":[{"award-number":["62073084"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003453","name":"Natural Science Foundation of Guangdong Province","doi-asserted-by":"publisher","award":["2023A1515012824"],"award-info":[{"award-number":["2023A1515012824"]}],"id":[{"id":"10.13039\/501100003453","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Expert Systems with Applications"],"published-print":{"date-parts":[[2026,3]]},"DOI":"10.1016\/j.eswa.2025.130313","type":"journal-article","created":{"date-parts":[[2025,11,10]],"date-time":"2025-11-10T12:10:12Z","timestamp":1762776612000},"page":"130313","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"PD","title":["Wind power forecasting through dynamic feature transfer learning and hierarchical information Interactive Network"],"prefix":"10.1016","volume":"299","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8235-3752","authenticated-orcid":false,"given":"Jingmin","family":"Fan","sequence":"first","affiliation":[]},{"given":"Mingwei","family":"Zhong","sequence":"additional","affiliation":[]}],"member":"78","reference":[{"issue":"1","key":"10.1016\/j.eswa.2025.130313_b0005","doi-asserted-by":"crossref","DOI":"10.1016\/j.heliyon.2023.e12802","article-title":"Fuzzy-based prediction of solar pv and wind power generation for microgrid modeling using particle swarm optimization","volume":"9","author":"Teferra","year":"2023","journal-title":"Heliyon"},{"key":"10.1016\/j.eswa.2025.130313_b0010","doi-asserted-by":"crossref","first-page":"3166","DOI":"10.1016\/j.egyr.2023.09.176","article-title":"Research on power fluctuation strategy of hybrid energy storage to suppress wind-photovoltaic hybrid power system","volume":"10","author":"Zhang","year":"2023","journal-title":"Energy Reports"},{"key":"10.1016\/j.eswa.2025.130313_b0015","unstructured":"[link].URL https:\/\/gwec.net\/globalwindreport2023\/."},{"key":"10.1016\/j.eswa.2025.130313_b0020","doi-asserted-by":"crossref","DOI":"10.1016\/j.rser.2021.111758","article-title":"A review of very short-term wind and solar power forecasting","volume":"153","author":"Tawn","year":"2022","journal-title":"Renewable and Sustainable Energy Reviews"},{"key":"10.1016\/j.eswa.2025.130313_b0025","doi-asserted-by":"crossref","DOI":"10.1016\/j.jclepro.2020.120617","article-title":"A review on the complementarity between gridconnected solar and wind power systems","author":"Weschenfelder","year":"2020","journal-title":"Journal of Cleaner Production 257"},{"issue":"3","key":"10.1016\/j.eswa.2025.130313_b0030","doi-asserted-by":"crossref","first-page":"1668","DOI":"10.1016\/j.energy.2010.12.063","article-title":"A corrected hybrid approach for wind speed prediction in hexi corridor of china","volume":"36","author":"Guo","year":"2011","journal-title":"Energy"},{"issue":"4","key":"10.1016\/j.eswa.2025.130313_b0035","doi-asserted-by":"crossref","first-page":"1405","DOI":"10.1016\/j.apenergy.2010.10.031","article-title":"Arma based approaches for forecasting the tuple of wind speed and direction","volume":"88","author":"Erdem","year":"2011","journal-title":"Applied Energy"},{"key":"10.1016\/j.eswa.2025.130313_b0040","doi-asserted-by":"crossref","first-page":"56","DOI":"10.1016\/j.apenergy.2016.11.111","article-title":"Deep learning based ensemble approach for probabilistic wind power forecasting","volume":"188","author":"Zhi Wang","year":"2017","journal-title":"Applied Energy"},{"key":"10.1016\/j.eswa.2025.130313_b0045","doi-asserted-by":"crossref","DOI":"10.1016\/j.energy.2024.131459","article-title":"An online long-term load forecasting method: Hierarchical highway network based on crisscross feature collaboration","volume":"299","author":"Fan","year":"2024","journal-title":"Energy"},{"key":"10.1016\/j.eswa.2025.130313_b0050","doi-asserted-by":"crossref","first-page":"1208","DOI":"10.1016\/j.renene.2020.08.077","article-title":"Deterministic and probabilistic wind speed forecasting with de-noising-reconstruction strategy and quantile regression based algorithm","volume":"162","author":"Hu","year":"2020","journal-title":"Renewable Energy"},{"key":"10.1016\/j.eswa.2025.130313_b0055","doi-asserted-by":"crossref","first-page":"554","DOI":"10.1016\/j.enconman.2017.06.021","article-title":"Non-parametric hybrid models for wind speed forecasting","volume":"148","author":"Han","year":"2017","journal-title":"Energy Conversion and Management"},{"key":"10.1016\/j.eswa.2025.130313_b0060","doi-asserted-by":"crossref","DOI":"10.1016\/j.energy.2020.119708","article-title":"Well production forecasting based on arima-lstm model considering manual operations","volume":"220","author":"Fan","year":"2021","journal-title":"Energy"},{"key":"10.1016\/j.eswa.2025.130313_b0065","doi-asserted-by":"crossref","first-page":"154","DOI":"10.1016\/j.apenergy.2012.03.054","article-title":"Wind speed and wind energy forecast through kalman filtering of numerical weather prediction model output","volume":"99","author":"Cassola","year":"2012","journal-title":"Applied Energy"},{"key":"10.1016\/j.eswa.2025.130313_b0070","doi-asserted-by":"crossref","DOI":"10.1016\/j.apenergy.2024.123745","article-title":"Infocavb-memoryformer: Forecasting of wind and photovoltaic power through the interaction of data reconstruction and data augmentation","volume":"371","author":"Zhong","year":"2024","journal-title":"Applied Energy"},{"key":"10.1016\/j.eswa.2025.130313_b0075","doi-asserted-by":"crossref","DOI":"10.1016\/j.seta.2021.101354","article-title":"Solar power generation prediction based on deep learning","volume":"47","author":"Chang","year":"2021","journal-title":"Sustainable Energy Technologies and Assessments"},{"key":"10.1016\/j.eswa.2025.130313_b0080","doi-asserted-by":"crossref","DOI":"10.1016\/j.jclepro.2020.123948","article-title":"Photovoltaic power forecasting based on a support vector machine with improved ant colony optimization","volume":"277","author":"Pan","year":"2020","journal-title":"Journal of Cleaner Production"},{"issue":"12","key":"10.1016\/j.eswa.2025.130313_b0085","doi-asserted-by":"crossref","DOI":"10.1016\/j.heliyon.2023.e23071","article-title":"A widely applicable and robust lightgbm - artificial neural network forecasting model for short-term wind power density","volume":"9","author":"Zeng","year":"2023","journal-title":"Heliyon"},{"key":"10.1016\/j.eswa.2025.130313_b0090","doi-asserted-by":"crossref","DOI":"10.1016\/j.energy.2022.126419","article-title":"A dual-scale deep learning model based on elm-bilstm and improved reptile search algorithm for wind power prediction","volume":"266","author":"Xiong","year":"2023","journal-title":"Energy"},{"key":"10.1016\/j.eswa.2025.130313_b0095","doi-asserted-by":"crossref","first-page":"207","DOI":"10.1016\/j.ref.2023.06.009","article-title":"Improving solar pv prediction performance with rf-catboost ensemble: A robust and complementary approach","volume":"46","author":"Banik","year":"2023","journal-title":"Renewable Energy Focus"},{"key":"10.1016\/j.eswa.2025.130313_b0100","doi-asserted-by":"crossref","first-page":"513","DOI":"10.1016\/j.rser.2019.03.040","article-title":"Machine-learning methods for integrated renewable power generation: A comparative study of artificial neural networks, support vector regression, and gaussian process regression","volume":"108","author":"Sharifzadeh","year":"2019","journal-title":"Renewable and Sustainable Energy Reviews"},{"key":"10.1016\/j.eswa.2025.130313_b0105","doi-asserted-by":"crossref","DOI":"10.1016\/j.solcom.2023.100061","article-title":"Review of deep learning techniques for power generation prediction of industrial solar photovoltaic plants","volume":"8","author":"Chandel","year":"2023","journal-title":"Solar Compass"},{"key":"10.1016\/j.eswa.2025.130313_b0110","article-title":"A cnn encoder decoder lstm model for sustainable wind power predictive analytics","volume":"38","author":"Garg","year":"2023","journal-title":"Sustainable Computing: Informatics and Systems"},{"key":"10.1016\/j.eswa.2025.130313_b0115","doi-asserted-by":"crossref","DOI":"10.1016\/j.energy.2023.129189","article-title":"A comprehensive approach for pv wind forecasting by using a hyperparameter tuned gcvcnn-mrnn deep learning model","volume":"283","author":"Mirza","year":"2023","journal-title":"Energy"},{"key":"10.1016\/j.eswa.2025.130313_b0120","doi-asserted-by":"crossref","DOI":"10.1016\/j.energy.2019.07.168","article-title":"Day-ahead power forecasting in a large-scale photovoltaic plant based on weather classification using lstm","volume":"187","author":"Gao","year":"2019","journal-title":"Energy"},{"key":"10.1016\/j.eswa.2025.130313_b0125","doi-asserted-by":"crossref","DOI":"10.1016\/j.energy.2022.124661","article-title":"Lowess smoothing and random forest based gru model: A short-term photovoltaic power generation forecasting method","volume":"256","author":"Dai","year":"2022","journal-title":"Energy"},{"key":"10.1016\/j.eswa.2025.130313_b0130","doi-asserted-by":"crossref","DOI":"10.1016\/j.apenergy.2023.122159","article-title":"Short-term prediction of integrated energy load aggregation using a bi-directional simple recurrent unit network with feature-temporal attention mechanism ensemble learning model","volume":"355","author":"Yan","year":"2024","journal-title":"Applied Energy"},{"key":"10.1016\/j.eswa.2025.130313_b0135","series-title":"The 41st International ACM SIGIR Conference on Research & Development in Information Retrieval","first-page":"95","article-title":"Modeling long- and short-term temporal patterns with deep neural networks","author":"Lai","year":"2018"},{"key":"10.1016\/j.eswa.2025.130313_b0140","doi-asserted-by":"crossref","DOI":"10.1016\/j.apenergy.2020.115098","article-title":"A novel wavenets long short term memory paradigm for wind power prediction","volume":"269","author":"Shahid","year":"2020","journal-title":"Applied Energy"},{"key":"10.1016\/j.eswa.2025.130313_b0145","doi-asserted-by":"crossref","DOI":"10.1016\/j.energy.2023.129171","article-title":"Short-term wind power forecasting model based on temporal convolutional network and informer","volume":"283","author":"Gong","year":"2023","journal-title":"Energy"},{"key":"10.1016\/j.eswa.2025.130313_b0150","doi-asserted-by":"crossref","unstructured":"H. Wang, G. Liu, P. Hu, Tdan: Transferable domain adversarial network for link prediction in heterogeneous social networks, ACM Trans. Knowl. Discov. Data 18 (1) (sep 2023). doi: 10.1145\/3610229. URL https:\/\/doi.org\/10.1145\/3610229.","DOI":"10.1145\/3610229"},{"key":"10.1016\/j.eswa.2025.130313_b0155","doi-asserted-by":"crossref","DOI":"10.1016\/j.energy.2023.130077","article-title":"Alleviating distribution shift and mining hidden temporal variations for ultra-short-term wind power forecasting","volume":"290","author":"Wei","year":"2024","journal-title":"Energy"},{"key":"10.1016\/j.eswa.2025.130313_b0160","doi-asserted-by":"crossref","first-page":"1120","DOI":"10.1016\/j.renene.2022.02.098","article-title":"Self-calibrated hybrid weather forecasters for solar thermal and photovoltaic power plants","volume":"188","author":"Hassan","year":"2022","journal-title":"Renewable Energy"},{"key":"10.1016\/j.eswa.2025.130313_b0165","doi-asserted-by":"crossref","DOI":"10.1016\/j.apenergy.2023.122266","article-title":"Wind power forecasting: A temporal domain generalization approach incorporating hybrid model and adversarial relationship-based training","volume":"355","author":"Tang","year":"2024","journal-title":"Applied Energy"},{"key":"10.1016\/j.eswa.2025.130313_b0170","doi-asserted-by":"crossref","first-page":"380","DOI":"10.1016\/j.isatra.2022.07.028","article-title":"Priori-guided and data-driven hybrid model for wind power forecasting","volume":"134","author":"Huang","year":"2023","journal-title":"ISA Transactions"},{"key":"10.1016\/j.eswa.2025.130313_b0175","doi-asserted-by":"crossref","DOI":"10.1016\/j.rser.2022.112519","article-title":"Uncovering wind power forecasting uncertainty sources and their propagation through the whole modelling chain","volume":"165","author":"Yan","year":"2022","journal-title":"Renewable and Sustainable Energy Reviews"},{"key":"10.1016\/j.eswa.2025.130313_b0180","doi-asserted-by":"crossref","DOI":"10.1016\/j.rser.2022.113046","article-title":"A hybrid framework for forecasting power generation of multiple renewable energy sources","volume":"172","author":"Zheng","year":"2023","journal-title":"Renewable and Sustainable Energy Reviews"},{"key":"10.1016\/j.eswa.2025.130313_b0185","doi-asserted-by":"crossref","DOI":"10.1016\/j.apenergy.2023.120815","article-title":"Series-wise attention network for wind power forecasting considering temporal lag of numerical weather prediction","volume":"336","author":"Liu","year":"2023","journal-title":"Applied Energy"},{"key":"10.1016\/j.eswa.2025.130313_b0190","doi-asserted-by":"crossref","DOI":"10.1016\/j.energy.2023.129588","article-title":"Dttm: A deep temporal transfer model for ultra-short-term online wind power forecasting","volume":"286","author":"Zhong","year":"2024","journal-title":"Energy"},{"issue":"24","key":"10.1016\/j.eswa.2025.130313_b0195","doi-asserted-by":"crossref","DOI":"10.3390\/electronics11244125","article-title":"A study on the wind power forecasting model using transfer learning approach","volume":"11","author":"Oh","year":"2022","journal-title":"Electronics"},{"issue":"11","key":"10.1016\/j.eswa.2025.130313_b0200","doi-asserted-by":"crossref","DOI":"10.3390\/su15119131","article-title":"Al Barakeh, transfer learning for renewable energy systems: A survey","volume":"15","author":"Al-Hajj","year":"2023","journal-title":"Sustainability"},{"key":"10.1016\/j.eswa.2025.130313_b0205","series-title":"2023 Panda Forum on Power and Energy (PandaFPE)","first-page":"1580","article-title":"A review of transfer learning approaches for load, solar and wind power predictions","author":"Luo","year":"2023"},{"key":"10.1016\/j.eswa.2025.130313_b0210","doi-asserted-by":"crossref","DOI":"10.1016\/j.apenergy.2023.121049","article-title":"An online transfer learning model for wind turbine power prediction based on spatial feature construction and system-wide update","volume":"340","author":"Liu","year":"2023","journal-title":"Applied Energy"},{"key":"10.1016\/j.eswa.2025.130313_b0215","doi-asserted-by":"crossref","DOI":"10.1016\/j.energy.2022.126504","article-title":"Dual-meta pool method for wind farm power forecasting with small sample data","volume":"267","author":"Liu","year":"2023","journal-title":"Energy"},{"key":"10.1016\/j.eswa.2025.130313_b0220","doi-asserted-by":"crossref","DOI":"10.1016\/j.apenergy.2023.122185","article-title":"Bayesian averagingenabled transfer learning method for probabilistic wind power forecasting of newly built wind farms","volume":"355","author":"Hu","year":"2024","journal-title":"Applied Energy"},{"key":"10.1016\/j.eswa.2025.130313_b0225","doi-asserted-by":"crossref","unstructured":"Z. Cai, L. Ye, L. Li, T. Feng, Z. Jian, Y. Li, Y. Zhao, Wind power loss forecasting and early warning based on transfer learning under extreme weather, 2022 IEEE 6th Conference on Energy Internet and Energy System Integration (EI2) (2022) 2911\u20132915doi:10.1109\/ EI256261.2022.10116290.","DOI":"10.1109\/EI256261.2022.10116290"},{"key":"10.1016\/j.eswa.2025.130313_b0230","doi-asserted-by":"crossref","DOI":"10.1016\/j.energy.2023.129639","article-title":"A privacy-preserving framework integrating federated learning and transfer learning for wind power forecasting","volume":"286","author":"Tang","year":"2024","journal-title":"Energy"},{"key":"10.1016\/j.eswa.2025.130313_b0235","doi-asserted-by":"crossref","DOI":"10.1016\/j.energy.2024.131966","article-title":"Hybrid model based on similar power extraction and improved temporal convolutional network for probabilistic wind power forecasting","volume":"304","author":"Chen","year":"2024","journal-title":"Energy"},{"key":"10.1016\/j.eswa.2025.130313_b0240","article-title":"Multi-step wind speed forecasting based on hybrid multi-stage decomposition model and long short-term memory neural network","volume":"213","author":"Moreno","year":"2020","journal-title":"Energy Conversion and Management"},{"key":"10.1016\/j.eswa.2025.130313_b0245","doi-asserted-by":"crossref","DOI":"10.1016\/j.renene.2024.120499","article-title":"A novel temporal\u2013spatial graph neural network for wind power forecasting considering blockage effects","volume":"227","author":"Qiu","year":"2024","journal-title":"Renewable Energy"},{"key":"10.1016\/j.eswa.2025.130313_b0250","doi-asserted-by":"crossref","DOI":"10.1016\/j.apenergy.2024.123487","article-title":"Optimizing multi-step wind power forecasting: Integrating advanced deep neural networks with stacking-based probabilistic learning","volume":"369","author":"de Azevedo Takara","year":"2024","journal-title":"Applied Energy"},{"key":"10.1016\/j.eswa.2025.130313_b0255","doi-asserted-by":"crossref","DOI":"10.1016\/j.eswa.2024.124766","article-title":"Interpretable multi-graph convolution network integrating spatio-temporal attention and dynamic combination for wind power forecasting","volume":"255","author":"Zhao","year":"2024","journal-title":"Expert Systems with Applications"},{"key":"10.1016\/j.eswa.2025.130313_b0260","article-title":"Short-term wind speed forecasting based on adaptive secondary decomposition and robust temporal convolutional network","volume":"288","author":"Zhang","year":"2024","journal-title":"Energy"},{"key":"10.1016\/j.eswa.2025.130313_b0265","unstructured":"[link]. URL https:\/\/www.elia.be\/en\/grid-data\/power-generation\/wind-power-generation#."},{"issue":"5","key":"10.1016\/j.eswa.2025.130313_b0270","doi-asserted-by":"crossref","first-page":"1400","DOI":"10.3390\/s25051400","article-title":"Deep learning approach for automatic heartbeat classification","volume":"25","author":"Guerra","year":"2025","journal-title":"Sensors"},{"key":"10.1016\/j.eswa.2025.130313_b0275","unstructured":"Fill, Jonas. \u201cDevelopment of the bayesian recurrent neural network architectures for hydrological time series forecasting.\u201d (2021)."}],"container-title":["Expert Systems with Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0957417425039284?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0957417425039284?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2025,12,23]],"date-time":"2025-12-23T15:49:14Z","timestamp":1766504954000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0957417425039284"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,3]]},"references-count":55,"alternative-id":["S0957417425039284"],"URL":"https:\/\/doi.org\/10.1016\/j.eswa.2025.130313","relation":{},"ISSN":["0957-4174"],"issn-type":[{"type":"print","value":"0957-4174"}],"subject":[],"published":{"date-parts":[[2026,3]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Wind power forecasting through dynamic feature transfer learning and hierarchical information Interactive Network","name":"articletitle","label":"Article Title"},{"value":"Expert Systems with Applications","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.eswa.2025.130313","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2025 Elsevier Ltd. All rights are reserved, including those for text and data mining, AI training, and similar technologies.","name":"copyright","label":"Copyright"}],"article-number":"130313"}}