{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,20]],"date-time":"2026-05-20T08:05:26Z","timestamp":1779264326265,"version":"3.51.4"},"reference-count":43,"publisher":"Springer Science and Business Media LLC","issue":"8","license":[{"start":{"date-parts":[[2026,5,20]],"date-time":"2026-05-20T00:00:00Z","timestamp":1779235200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,5,20]],"date-time":"2026-05-20T00:00:00Z","timestamp":1779235200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["72471097"],"award-info":[{"award-number":["72471097"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["72401101"],"award-info":[{"award-number":["72401101"]}],"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":["2024A1515010941"],"award-info":[{"award-number":["2024A1515010941"]}],"id":[{"id":"10.13039\/501100003453","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003453","name":"Natural Science Foundation of Guangdong Province","doi-asserted-by":"publisher","award":["2023A1515110580"],"award-info":[{"award-number":["2023A1515110580"]}],"id":[{"id":"10.13039\/501100003453","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Supercomput"],"DOI":"10.1007\/s11227-026-08589-0","type":"journal-article","created":{"date-parts":[[2026,5,20]],"date-time":"2026-05-20T07:05:24Z","timestamp":1779260724000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["A novel spatiotemporal model with static-dynamic graph learning for multi-node wind speed forecasting"],"prefix":"10.1007","volume":"82","author":[{"given":"Dabin","family":"Zhang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ni","family":"Hong","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Boting","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Huanling","family":"Hu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Liling","family":"Zeng","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2026,5,20]]},"reference":[{"key":"8589_CR1","doi-asserted-by":"publisher","DOI":"10.1016\/j.energy.2021.121808","volume":"238","author":"X Chen","year":"2022","unstructured":"Chen X, Yu R, Ullah S et al (2022) A novel loss function of deep learning in wind speed forecasting. Energy 238:121808. https:\/\/doi.org\/10.1016\/j.energy.2021.121808","journal-title":"Energy"},{"key":"8589_CR2","first-page":"14","volume-title":"Bp statistical review of world energy","author":"S Dale","year":"2021","unstructured":"Dale S et al (2021) Bp statistical review of world energy. BP Plc, London, pp 14\u201316"},{"key":"8589_CR3","unstructured":"Global Wind Energy Council (2024) Global wind report 2024. https:\/\/gwec.net\/globalwindreport2024. Accessed 16 April 2024"},{"key":"8589_CR4","unstructured":"International Energy Agency (2024) Renewables 2024: analysis and forecast to 2030. IEA Publications. https:\/\/www.iea.org\/reports\/renewables-2024. Accessed 9 Oct 2024"},{"key":"8589_CR5","doi-asserted-by":"publisher","DOI":"10.1016\/j.apenergy.2019.114137","volume":"259","author":"Z Liu","year":"2020","unstructured":"Liu Z, Jiang P, Zhang L et al (2020) A combined forecasting model for time series: application to short-term wind speed forecasting. Appl Energy 259:114137. https:\/\/doi.org\/10.1016\/j.apenergy.2019.114137","journal-title":"Appl Energy"},{"issue":"3","key":"8589_CR6","doi-asserted-by":"publisher","first-page":"836","DOI":"10.1109\/60.790962","volume":"14","author":"M Alexiadis","year":"1999","unstructured":"Alexiadis M, Dokopoulos P, Sahsamanoglou H (1999) Wind speed and power forecasting based on spatial correlation models. IEEE Trans Energy Convers 14(3):836\u2013842. https:\/\/doi.org\/10.1109\/60.790962","journal-title":"IEEE Trans Energy Convers"},{"key":"8589_CR7","doi-asserted-by":"publisher","DOI":"10.1016\/j.seta.2022.102535","volume":"53","author":"Y Dong","year":"2022","unstructured":"Dong Y, Li J, Liu Z et al (2022) Ensemble wind speed forecasting system based on optimal model adaptive selection strategy: case study in China. Sustain Energy Technol Assess 53:102535. https:\/\/doi.org\/10.1016\/j.seta.2022.102535","journal-title":"Sustain Energy Technol Assess"},{"issue":"4","key":"8589_CR8","doi-asserted-by":"publisher","first-page":"1405","DOI":"10.1016\/j.apenergy.2010.10.031","volume":"88","author":"E Erdem","year":"2011","unstructured":"Erdem E, Shi J (2011) Arma based approaches for forecasting the tuple of wind speed and direction. Appl Energy 88(4):1405\u20131414. https:\/\/doi.org\/10.1016\/j.apenergy.2010.10.031","journal-title":"Appl Energy"},{"key":"8589_CR9","doi-asserted-by":"publisher","DOI":"10.1016\/j.enconman.2021.113917","volume":"233","author":"MD Liu","year":"2021","unstructured":"Liu MD, Ding L, Bai YL (2021) Application of hybrid model based on empirical mode decomposition, novel recurrent neural networks and the arima to wind speed prediction. Energy Convers Manag 233:113917. https:\/\/doi.org\/10.1016\/j.enconman.2021.113917","journal-title":"Energy Convers Manag"},{"key":"8589_CR10","doi-asserted-by":"publisher","first-page":"134","DOI":"10.1016\/j.enconman.2018.02.012","volume":"163","author":"J Wang","year":"2018","unstructured":"Wang J, Yang W, Du P et al (2018) A novel hybrid forecasting system of wind speed based on a newly developed multi-objective sine cosine algorithm. Energy Convers Manag 163:134\u2013150. https:\/\/doi.org\/10.1016\/j.enconman.2018.02.012","journal-title":"Energy Convers Manag"},{"key":"8589_CR11","doi-asserted-by":"publisher","first-page":"311","DOI":"10.1016\/j.ijepes.2014.07.029","volume":"64","author":"L Xiao","year":"2015","unstructured":"Xiao L, Wang J, Yang X et al (2015) A hybrid model based on data preprocessing for electrical power forecasting. Int J Electr Power Energy Syst 64:311\u2013327. https:\/\/doi.org\/10.1016\/j.ijepes.2014.07.029","journal-title":"Int J Electr Power Energy Syst"},{"key":"8589_CR12","doi-asserted-by":"publisher","DOI":"10.1016\/j.jclepro.2020.123739","volume":"279","author":"L Li","year":"2021","unstructured":"Li L, Cen ZY, Tseng ML et al (2021) Improving short-term wind power prediction using hybrid improved cuckoo search arithmetic-support vector regression machine. J Clean Prod 279:123739. https:\/\/doi.org\/10.1016\/j.jclepro.2020.123739","journal-title":"J Clean Prod"},{"key":"8589_CR13","doi-asserted-by":"publisher","DOI":"10.1016\/j.enconman.2021.115102","volume":"252","author":"L Hua","year":"2022","unstructured":"Hua L, Zhang C, Peng T et al (2022) Integrated framework of extreme learning machine (elm) based on improved atom search optimization for short-term wind speed prediction. Energy Convers Manag 252:115102. https:\/\/doi.org\/10.1016\/j.enconman.2021.115102","journal-title":"Energy Convers Manag"},{"key":"8589_CR14","doi-asserted-by":"publisher","first-page":"879","DOI":"10.1016\/j.renene.2018.07.060","volume":"131","author":"MA Chitsazan","year":"2019","unstructured":"Chitsazan MA, Fadali MS, Trzynadlowski AM (2019) Wind speed and wind direction forecasting using echo state network with nonlinear functions. Renew Energy 131:879\u2013889. https:\/\/doi.org\/10.1016\/j.renene.2018.07.060","journal-title":"Renew Energy"},{"key":"8589_CR15","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijepes.2020.106056","volume":"121","author":"M Liu","year":"2020","unstructured":"Liu M, Cao Z, Zhang J et al (2020) Short-term wind speed forecasting based on the jaya-SVM model. Int J Electr Power Energy Syst 121:106056. https:\/\/doi.org\/10.1016\/j.ijepes.2020.106056","journal-title":"Int J Electr Power Energy Syst"},{"key":"8589_CR16","doi-asserted-by":"publisher","DOI":"10.1016\/j.renene.2023.119101","volume":"216","author":"H Hu","year":"2023","unstructured":"Hu H, Wang L, Zhang D et al (2023) Rolling decomposition method in fusion with echo state network for wind speed forecasting. Renew Energy 216:119101. https:\/\/doi.org\/10.1016\/j.renene.2023.119101","journal-title":"Renew Energy"},{"key":"8589_CR17","doi-asserted-by":"publisher","first-page":"1508","DOI":"10.1016\/j.egyr.2021.12.062","volume":"8","author":"L Wang","year":"2022","unstructured":"Wang L, Guo Y, Fan M et al (2022) Wind speed prediction using measurements from neighboring locations and combining the extreme learning machine and the AdaBoost algorithm. Energy Rep 8:1508\u20131518. https:\/\/doi.org\/10.1016\/j.egyr.2021.12.062","journal-title":"Energy Rep"},{"key":"8589_CR18","doi-asserted-by":"publisher","DOI":"10.1016\/j.enconman.2019.111799","volume":"198","author":"H Wang","year":"2019","unstructured":"Wang H, Lei Z, Zhang X et al (2019) A review of deep learning for renewable energy forecasting. Energy Convers Manag 198:111799","journal-title":"Energy Convers Manag"},{"issue":"2","key":"8589_CR19","doi-asserted-by":"publisher","first-page":"670","DOI":"10.1016\/j.enconman.2019.111799","volume":"10","author":"M Khodayar","year":"2018","unstructured":"Khodayar M, Wang J (2018) Spatio-temporal graph deep neural network for short-term wind speed forecasting. IEEE Trans Sustain Energy 10(2):670\u2013681. https:\/\/doi.org\/10.1016\/j.enconman.2019.111799","journal-title":"IEEE Trans Sustain Energy"},{"issue":"21","key":"8589_CR20","doi-asserted-by":"publisher","DOI":"10.3390\/app112110335","volume":"11","author":"WH Lin","year":"2021","unstructured":"Lin WH, Wang P, Chao KM et al (2021) Wind power forecasting with deep learning networks: time-series forecasting. Appl Sci Basel 11(21):10335. https:\/\/doi.org\/10.3390\/app112110335","journal-title":"Appl Sci Basel"},{"key":"8589_CR21","doi-asserted-by":"publisher","unstructured":"Li M, Zhu Z (2021) Spatial-temporal fusion graph neural networks for traffic flow forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 4189\u20134196. https:\/\/doi.org\/10.1609\/aaai.v35i5.16542.","DOI":"10.1609\/aaai.v35i5.16542"},{"issue":"2","key":"8589_CR22","doi-asserted-by":"publisher","first-page":"670","DOI":"10.1109\/TSTE.2018.2844102","volume":"10","author":"M Khodayar","year":"2018","unstructured":"Khodayar M, Wang J (2018) Spatio-temporal graph deep neural network for short-term wind speed forecasting. IEEE Trans Sustain Energy 10(2):670\u2013681. https:\/\/doi.org\/10.1109\/TSTE.2018.2844102","journal-title":"IEEE Trans Sustain Energy"},{"key":"8589_CR23","doi-asserted-by":"publisher","first-page":"1014","DOI":"10.1016\/j.renene.2021.08.066","volume":"180","author":"X Geng","year":"2021","unstructured":"Geng X, Xu L, He X et al (2021) Graph optimization neural network with spatio-temporal correlation learning for multi-node offshore wind speed forecasting. Renew Energy 180:1014\u20131025. https:\/\/doi.org\/10.1016\/j.renene.2021.08.066","journal-title":"Renew Energy"},{"key":"8589_CR24","doi-asserted-by":"publisher","DOI":"10.1016\/j.energy.2024.130404","volume":"292","author":"S Lin","year":"2024","unstructured":"Lin S, Wang S, Xu X et al (2024) Gaoformer: An adaptive spatiotemporal feature fusion transformer utilizing GAT and optimizable graph matrixes for offshore wind speed prediction. Energy 292:130404. https:\/\/doi.org\/10.1016\/j.energy.2024.130404","journal-title":"Energy"},{"key":"8589_CR25","doi-asserted-by":"publisher","unstructured":"Wu Z, Pan S, Long G et al (2019) Graph wavenet for deep spatial-temporal graph modeling. arXiv preprint arXiv:190600121. https:\/\/doi.org\/10.48550\/arXiv.1906.00121.","DOI":"10.48550\/arXiv.1906.00121"},{"key":"8589_CR26","doi-asserted-by":"publisher","unstructured":"Wu Z, Pan S, Long G et al (2020) Connecting the dots: multivariate time series forecasting with graph neural networks. In: Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp 753\u2013763. https:\/\/doi.org\/10.1145\/3394486.3403118.","DOI":"10.1145\/3394486.3403118"},{"key":"8589_CR27","first-page":"17804","volume":"33","author":"L Bai","year":"2020","unstructured":"Bai L, Yao L, Li C et al (2020) Adaptive graph convolutional recurrent network for traffic forecasting. Adv Neural Inf Process Syst 33:17804\u201317815","journal-title":"Adv Neural Inf Process Syst"},{"key":"8589_CR28","doi-asserted-by":"publisher","unstructured":"Rathore N, Rathore P, Basak A et al (2021) Multi scale graph wavenet for wind speed forecasting. In: 2021 IEEE International Conference on Big Data (Big Data), IEEE, pp 4047\u20134053. https:\/\/doi.org\/10.1109\/BigData52589.2021.9671624.","DOI":"10.1109\/BigData52589.2021.9671624"},{"key":"8589_CR29","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2023.110294","volume":"141","author":"Z Gao","year":"2023","unstructured":"Gao Z, Li Z, Xu L et al (2023) Dynamic adaptive spatio-temporal graph neural network for multi-node offshore wind speed forecasting. Appl Soft Comput 141:110294. https:\/\/doi.org\/10.1016\/j.asoc.2023.110294","journal-title":"Appl Soft Comput"},{"issue":"1","key":"8589_CR30","doi-asserted-by":"publisher","DOI":"10.1038\/s41467-025-58456-4","volume":"16","author":"Z Zhang","year":"2025","unstructured":"Zhang Z, Lin L, Gao S et al (2025) A machine learning model for hub-height short-term wind speed prediction. Nat Commun 16(1):3195","journal-title":"Nat Commun"},{"key":"8589_CR31","doi-asserted-by":"publisher","DOI":"10.1016\/j.energy.2024.133206","volume":"310","author":"J He","year":"2024","unstructured":"He J, Hu Z, Wang S et al (2024) Windformer: A novel 4D high-resolution system for multi-step wind speed vector forecasting based on temporal shifted window multi-head self-attention. Energy 310:133206. https:\/\/doi.org\/10.1016\/j.energy.2024.133206","journal-title":"Energy"},{"key":"8589_CR32","doi-asserted-by":"publisher","unstructured":"Trebing K, Mehrkanoon S (2020) Wind speed prediction using multidimensional con-volutional neural networks. In: 2020 IEEE symposium series on computational intelligence (SSCI), IEEE, pp 713\u2013720. https:\/\/doi.org\/10.1109\/SSCI47803.2020.9308323.","DOI":"10.1109\/SSCI47803.2020.9308323"},{"key":"8589_CR33","unstructured":"Danish Energy Agency (2021) Wind energy in Denmark: technology and integration. Ministry of Climate, Energy and Utilities. https:\/\/ens.dk"},{"key":"8589_CR34","doi-asserted-by":"publisher","first-page":"55","DOI":"10.1016\/j.rser.2019.04.025","volume":"109","author":"M DeCastro","year":"2019","unstructured":"DeCastro M, Salvador S, G\u00b4omez-Gesteira M et al (2019) Europe, China and the United States: three different approaches to the development of offshore wind energy. Renew Sustain Energy Rev 109:55\u201370. https:\/\/doi.org\/10.1016\/j.rser.2019.04.025","journal-title":"Renew Sustain Energy Rev"},{"key":"8589_CR35","unstructured":"Dauphin YN, Fan A, Auli M et al (2017) Language modeling with gated convolutional networks. In: International Conference on Machine Learning, PMLR, pp 933\u2013941"},{"key":"8589_CR36","unstructured":"Dong Y, Cordonnier JB, Loukas A (2021) Attention is not all you need: pure attention loses rank doubly exponentially with depth. In: International Conference on Machine Learning, PMLR, pp 2793\u20132803"},{"key":"8589_CR37","unstructured":"Vaswani A, Shazeer N, Parmar N et al (2017) Attention is all you need. Advances in neural information processing systems 30"},{"key":"8589_CR38","first-page":"19314","volume":"33","author":"Y Chen","year":"2020","unstructured":"Chen Y, Wu L, Zaki M (2020) Iterative deep graph learning for graph neural networks: better and robust node embeddings. Adv Neural Inf Process Syst 33:19314\u201319326","journal-title":"Adv Neural Inf Process Syst"},{"key":"8589_CR39","doi-asserted-by":"crossref","unstructured":"He K, Fan H, Wu Y et al (2020) Momentum contrast for unsupervised visual representation learning. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp 9729\u20139738","DOI":"10.1109\/CVPR42600.2020.00975"},{"key":"8589_CR40","doi-asserted-by":"publisher","unstructured":"Kalchbrenner N, Espeholt L, Simonyan K et al (2016) Neural machine translation in linear time. arXiv preprint arXiv:161010099. https:\/\/doi.org\/10.48550\/arXiv.1610.10099","DOI":"10.48550\/arXiv.1610.10099"},{"key":"8589_CR41","doi-asserted-by":"crossref","unstructured":"Sta\u0144czyk T, Mehrkanoon S (2021) Deep graph convolutional networks for wind speed prediction. In: ESANN 2021 Proceedings-29th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning. i6doc.com publication, pp 147\u2013152","DOI":"10.14428\/esann\/2021.ES2021-25"},{"key":"8589_CR42","unstructured":"Danish Energy Agency (2025) Technology Brief: Update of Offshore Wind in the Technology Catalogue. https:\/\/www.ens.dk\/en\/our-services\/technology-catalogue. Accessed 26 April 2024"},{"key":"8589_CR43","doi-asserted-by":"publisher","DOI":"10.2172\/1902302","volume-title":"A systematic framework for projecting the future cost of offshore wind energy","author":"M Shields","year":"2022","unstructured":"Shields M, Beiter P, Nunemaker J (2022) A systematic framework for projecting the future cost of offshore wind energy. National Renewable Energy Laboratory (NREL), Golden. https:\/\/doi.org\/10.2172\/1902302"}],"container-title":["The Journal of Supercomputing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11227-026-08589-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11227-026-08589-0","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11227-026-08589-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,5,20]],"date-time":"2026-05-20T07:05:29Z","timestamp":1779260729000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11227-026-08589-0"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,5,20]]},"references-count":43,"journal-issue":{"issue":"8","published-online":{"date-parts":[[2026,6]]}},"alternative-id":["8589"],"URL":"https:\/\/doi.org\/10.1007\/s11227-026-08589-0","relation":{},"ISSN":["1573-0484"],"issn-type":[{"value":"1573-0484","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,5,20]]},"assertion":[{"value":"22 August 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"5 May 2026","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"20 May 2026","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare no competing interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}],"article-number":"432"}}