{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,11]],"date-time":"2025-09-11T19:03:28Z","timestamp":1757617408545,"version":"3.44.0"},"reference-count":34,"publisher":"Springer Science and Business Media LLC","issue":"2","license":[{"start":{"date-parts":[[2025,2,12]],"date-time":"2025-02-12T00:00:00Z","timestamp":1739318400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,2,12]],"date-time":"2025-02-12T00:00:00Z","timestamp":1739318400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"name":"2022 Industrial  Technology Infrastructure Public Service Platform","award":["2022\u2013232-223","2022\u2013232-223"],"award-info":[{"award-number":["2022\u2013232-223","2022\u2013232-223"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Earth Sci Inform"],"published-print":{"date-parts":[[2025,6]]},"DOI":"10.1007\/s12145-025-01772-6","type":"journal-article","created":{"date-parts":[[2025,2,12]],"date-time":"2025-02-12T16:30:08Z","timestamp":1739377808000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["A lightweight dual-layer convolutional model for wind power forecasting"],"prefix":"10.1007","volume":"18","author":[{"given":"Yongsheng","family":"Ye","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Liu","family":"Liu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yang","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xun","family":"Liu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yanlong","family":"Xu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yuchen","family":"Liu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lili","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,2,12]]},"reference":[{"key":"1772_CR1","doi-asserted-by":"publisher","first-page":"758","DOI":"10.1016\/j.renene.2019.01.031","volume":"136","author":"SSN Aasim","year":"2019","unstructured":"Aasim SSN, Mohapatra A (2019) Repeated wavelet transform-based ARIMA model for very short-term wind speed forecasting. Renew Energy 136:758\u2013768","journal-title":"Renew Energy"},{"issue":"1","key":"1772_CR2","doi-asserted-by":"publisher","first-page":"432","DOI":"10.1214\/17-AOAS1099","volume":"12","author":"J Bessac","year":"2018","unstructured":"Bessac J, Constantinescu E, Anitescu M (2018) Stochastic simulation of predictive space\u2013time scenarios of wind speed using observations and physical model outputs. Ann Appl Stat 12(1):432\u2013458","journal-title":"Ann Appl Stat"},{"key":"1772_CR3","doi-asserted-by":"publisher","first-page":"109420","DOI":"10.1016\/j.ijepes.2023.109420","volume":"154","author":"H Chen","year":"2023","unstructured":"Chen H, Wu H, Kan T, Zhang J, Li H (2023) Low-carbon economic dispatch of integrated energy system containing electric hydrogen production based on VMD-GRU short-term wind power prediction. Int J Electr Power Energy Syst 154:109420","journal-title":"Int J Electr Power Energy Syst"},{"issue":"4","key":"1772_CR4","doi-asserted-by":"publisher","first-page":"2342","DOI":"10.1109\/TII.2021.3097716","volume":"18","author":"A Dolatabadi","year":"2022","unstructured":"Dolatabadi A, Abdeltawab H, Mohamed YA-RI (2022) Deep spatial-temporal 2-D CNN-BLSTM model for ultrashort-Term LiDAR-assisted wind turbine\u2019s power and fatigue load forecasting. IEEE Trans Ind Informat 18(4):2342\u20132353","journal-title":"IEEE Trans Ind Informat"},{"issue":"Part A","key":"1772_CR5","doi-asserted-by":"publisher","first-page":"241","DOI":"10.1016\/j.renene.2016.10.030","volume":"102","author":"Q Dong","year":"2017","unstructured":"Dong Q, Sun Y, Li P (2017) A novel forecasting model based on a hybrid processing strategy and an optimized local linear fuzzy neural network to make wind power forecasting: a case study of wind farms in China. Renew Energy 102(Part A):241\u2013257","journal-title":"Renew Energy"},{"issue":"3","key":"1772_CR6","doi-asserted-by":"publisher","first-page":"531","DOI":"10.1109\/TSP.2013.2288675","volume":"62","author":"K Dragomiretskiy","year":"2014","unstructured":"Dragomiretskiy K, Zosso D (2014) Variational mode decomposition. IEEE Trans Signal Process 62(3):531\u2013544","journal-title":"IEEE Trans Signal Process"},{"key":"1772_CR7","doi-asserted-by":"publisher","first-page":"116022","DOI":"10.1016\/j.enconman.2022.116022","volume":"268","author":"AA Ewees","year":"2022","unstructured":"Ewees AA, Al-qaness MAA, Abualigah L, Abd Elaziz M (2022) HBO-LSTM: Optimized long short-term memory with heap-based optimizer for wind power forecasting. Energy Convers Manag 268:116022","journal-title":"Energy Convers Manag"},{"issue":"1","key":"1772_CR8","first-page":"263","volume":"50","author":"Z Fan","year":"2024","unstructured":"Fan Z, Du J (2024) Transformer oil dissolved gas volume fraction prediction based on relevant variational mode decomposition and CNN-LSTM. High Volt Technol 50(1):263\u2013273","journal-title":"High Volt Technol"},{"issue":"4","key":"1772_CR9","doi-asserted-by":"publisher","first-page":"3396","DOI":"10.1109\/TGRS.2020.3008286","volume":"59","author":"H Gao","year":"2021","unstructured":"Gao H, Yang Y, Li C, Gao L, Zhang B (2021) Multiscale residual network with mixed depthwise convolution for hyperspectral image classification. IEEE Trans Geosci Remote Sens 59(4):3396\u20133408","journal-title":"IEEE Trans Geosci Remote Sens"},{"key":"1772_CR10","doi-asserted-by":"publisher","first-page":"124179","DOI":"10.1016\/j.energy.2022.124179","volume":"253","author":"MJ Gong","year":"2022","unstructured":"Gong MJ, Zhao Y, Sun JW, Han CT, Sun GN, Yan B (2022) Load forecasting of district heating system based on Informer. Energy 253:124179","journal-title":"Energy"},{"key":"1772_CR11","doi-asserted-by":"publisher","first-page":"121638","DOI":"10.1016\/j.apenergy.2023.121638","volume":"349","author":"MA Houran","year":"2023","unstructured":"Houran MA, Salman Bukhari SM, Zafar MH, Mansoor M, Chen W (2023) COA-CNN-LSTM: coati optimization algorithm-based hybrid deep learning model for PV\/wind power forecasting in smart grid applications. Appl Energy 349:121638","journal-title":"Appl Energy"},{"key":"1772_CR12","doi-asserted-by":"publisher","unstructured":"Howard AG, Zhu M, Chen B, Kalenichenko D, Wang W, Weyand T, Andreetto M, Adam H (2017) MobileNets: Efficient convolutional neural networks for mobile vision applications. arXiv e-prints. https:\/\/doi.org\/10.48550\/arXiv.1704.04861","DOI":"10.48550\/arXiv.1704.04861"},{"issue":"1","key":"1772_CR13","doi-asserted-by":"publisher","first-page":"126","DOI":"10.1007\/s40565-015-0171-6","volume":"5","author":"Y Jiang","year":"2017","unstructured":"Jiang Y, Chen X, Yu K, Liao Y (2017) Short-term wind power forecasting using hybrid method based on enhanced boosting algorithm. J Mod Power Syst Clean Energy 5(1):126\u2013133","journal-title":"J Mod Power Syst Clean Energy"},{"issue":"11","key":"1772_CR14","doi-asserted-by":"publisher","first-page":"1430","DOI":"10.1049\/iet-rpg.2016.0972","volume":"11","author":"O Karaku\u015f","year":"2017","unstructured":"Karaku\u015f O, Kuruo\u011flu EE, Alt\u0131nkaya MA (2017) One-day ahead wind speed\/power prediction based on polynomial autoregressive model. IET Renew Power Gener 11(11):1430\u20131439","journal-title":"IET Renew Power Gener"},{"issue":"6","key":"1772_CR15","doi-asserted-by":"publisher","first-page":"2770","DOI":"10.1109\/TII.2017.2730846","volume":"13","author":"M Khodayar","year":"2017","unstructured":"Khodayar M, Kaynak O, Khodayar ME (2017) Rough deep neural architecture for short-term wind speed forecasting. IEEE Trans Ind Informat 13(6):2770\u20132779","journal-title":"IEEE Trans Ind Informat"},{"key":"1772_CR16","doi-asserted-by":"publisher","first-page":"120","DOI":"10.1016\/j.enconman.2018.04.021","volume":"166","author":"H Liu","year":"2018","unstructured":"Liu H, Mi X, Li Y (2018) Smart deep learning-based wind speed prediction model using wavelet packet decomposition, convolutional neural network, and convolutional long short-term memory network. Energy Convers Manag 166:120\u2013131","journal-title":"Energy Convers Manag"},{"key":"1772_CR17","doi-asserted-by":"publisher","first-page":"106056","DOI":"10.1016\/j.ijepes.2020.106056","volume":"121","author":"M Liu","year":"2020","unstructured":"Liu M, Cao Z, Zhang J, Wang L, Huang C, Luo X (2020) Short-term wind speed forecasting based on the Jaya-SVM model. Int J Electr Power Energy Syst 121:106056","journal-title":"Int J Electr Power Energy Syst"},{"issue":"02","key":"1772_CR18","first-page":"439","volume":"67","author":"H Liu","year":"2024","unstructured":"Liu H, Lei D, Yuan J et al (2024) TEC prediction of the ionosphere based on attention mechanism LSTM. Chin J Geophys 67(02):439\u2013451","journal-title":"Chin J Geophys"},{"key":"1772_CR19","doi-asserted-by":"publisher","first-page":"117446","DOI":"10.1016\/j.apenergy.2021.117446","volume":"301","author":"P Lu","year":"2021","unstructured":"Lu P, Ye L, Zhao Y, Dai B, Pei M, Tang Y (2021) Review of meta-heuristic algorithms for wind power prediction: methodologies, applications, and challenges. Appl Energy 301:117446","journal-title":"Appl Energy"},{"issue":"9","key":"1772_CR20","doi-asserted-by":"publisher","first-page":"6474","DOI":"10.1109\/TII.2021.3130237","volume":"18","author":"L Lv","year":"2022","unstructured":"Lv L, Wu Z, Zhang J, Zhang L, Tan Z, Tian Z (2022) A VMD and LSTM based hybrid model of load forecasting for power grid Security. IEEE Trans Ind Informat 18(9):6474\u20136482","journal-title":"IEEE Trans Ind Informat"},{"key":"1772_CR21","doi-asserted-by":"publisher","first-page":"75","DOI":"10.1016\/j.enconman.2016.02.013","volume":"114","author":"A Meng","year":"2016","unstructured":"Meng A, Ge J, Yin H, Chen S (2016) Wind speed forecasting based on wavelet packet decomposition and artificial neural networks trained by crisscross optimization algorithm. Energy Convers Manag 114:75\u201388","journal-title":"Energy Convers Manag"},{"issue":"5","key":"1772_CR22","doi-asserted-by":"publisher","first-page":"471","DOI":"10.1002\/we.237","volume":"10","author":"HA Nielsen","year":"2007","unstructured":"Nielsen HA, Nielsen TS, Madsen H, San Isidro MJ, Marti I (2007) Optimal combination of wind power forecasts. Wind Energ 10(5):471\u2013482","journal-title":"Wind Energ"},{"issue":"1","key":"1772_CR23","doi-asserted-by":"publisher","first-page":"141","DOI":"10.1007\/s12145-024-01560-8","volume":"18","author":"X Qin","year":"2025","unstructured":"Qin X, Yuan L, Dong X, Zhang S, Shi H (2025) Short term wind speed prediction based on CEESMDAN and improved seagull optimization kernel extreme learning machine. Earth Sci Inform 18(1):141","journal-title":"Earth Sci Inform"},{"issue":"23","key":"1772_CR24","doi-asserted-by":"publisher","first-page":"6039","DOI":"10.1109\/TSP.2019.2951223","volume":"67","author":"NU Rehman","year":"2019","unstructured":"Rehman NU, Aftab H (2019) Multivariate variational mode decomposition. IEEE Trans Signal Process 67(23):6039\u20136052","journal-title":"IEEE Trans Signal Process"},{"key":"1772_CR25","doi-asserted-by":"publisher","first-page":"143759","DOI":"10.1109\/ACCESS.2020.3009537","volume":"8","author":"M Sajjad","year":"2020","unstructured":"Sajjad M, Khan ZA, Ullah A, Hussain T, Ullah W, Lee MY, Baik SW (2020) A novel CNN-GRU-based hybrid approach for short-term residential load forecasting. IEEE Access 8:143759\u2013143768","journal-title":"IEEE Access"},{"issue":"2\u20131","key":"1772_CR26","first-page":"1","volume":"5","author":"A Shokri Gazafroudi","year":"2015","unstructured":"Shokri Gazafroudi A (2015) Assessing the impact of load and renewable energies\u2019 uncertainty on a hybrid system. Int J Energy Power Eng 5(2\u20131):1\u20138","journal-title":"Int J Energy Power Eng"},{"issue":"14","key":"1772_CR27","doi-asserted-by":"publisher","first-page":"10757","DOI":"10.3390\/su151410757","volume":"15","author":"WC Tsai","year":"2023","unstructured":"Tsai WC, Hong CM, Tu CS, Lin WM, Chen CH (2023) A review of modern wind power generation forecasting technologies. Sustainability 15(14):10757","journal-title":"Sustainability"},{"key":"1772_CR28","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.apenergy.2018.12.076","volume":"237","author":"H Wang","year":"2019","unstructured":"Wang H, Han S, Liu Y, Yan J, Li L (2019) Sequence transfer correction algorithm for numerical weather prediction wind speed and its application in a wind power forecasting system. Appl Energy 237:1\u201310","journal-title":"Appl Energy"},{"key":"1772_CR29","doi-asserted-by":"publisher","first-page":"107776","DOI":"10.1016\/j.epsr.2022.107776","volume":"206","author":"B Xiong","year":"2022","unstructured":"Xiong B, Lou L, Meng X, Wang X, Ma H, Wang Z (2022) Short-term wind power forecasting based on attention mechanism and deep learning. Electr Power Syst Res 206:107776","journal-title":"Electr Power Syst Res"},{"key":"1772_CR30","doi-asserted-by":"publisher","first-page":"126419","DOI":"10.1016\/j.energy.2022.126419","volume":"266","author":"J Xiong","year":"2023","unstructured":"Xiong J, Peng T, Tao Z, Zhang C, Song S, Nazir MS (2023) A dual-scale deep learning model based on ELM-BiLSTM and improved reptile search algorithm for wind power prediction. Energy 266:126419","journal-title":"Energy"},{"issue":"10","key":"1772_CR31","doi-asserted-by":"publisher","first-page":"12113","DOI":"10.1109\/TPAMI.2023.3275156","volume":"45","author":"P Xu","year":"2023","unstructured":"Xu P, Zhu X, Clifton DA (2023) Multimodal learning with transformers: a survey. IEEE Trans Pattern Anal Mach Intell 45(10):12113\u201312132","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"1772_CR32","doi-asserted-by":"publisher","first-page":"119515","DOI":"10.1016\/j.energy.2020.119515","volume":"218","author":"M Yang","year":"2021","unstructured":"Yang M, Shi C, Liu H (2021) Day-ahead wind power forecasting based on the clustering of equivalent power curves. Energy 218:119515","journal-title":"Energy"},{"key":"1772_CR33","doi-asserted-by":"publisher","DOI":"10.1016\/j.engappai.2023.105982","volume":"121","author":"Z Zhao","year":"2023","unstructured":"Zhao Z, Yun S, Jia L, Guo J, Meng Y, He N, Li X, Shi J, Yang L (2023) Hybrid VMD-CNN-GRU-based model for short-term forecasting of wind power considering spatio-temporal features. Eng Appl Artif Intell 121:105982","journal-title":"Eng Appl Artif Intell"},{"issue":"12","key":"1772_CR34","first-page":"11106","volume":"35","author":"H Zhou","year":"2021","unstructured":"Zhou H, Zhang S, Peng J, Zhang S, Li J, Xiong H, Zhang W (2021) Informer: beyond efficient transformer for long sequence time-series forecasting. Proc AAAI Conf Artif Intell 35(12):11106\u201311115","journal-title":"Proc AAAI Conf Artif Intell"}],"container-title":["Earth Science Informatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s12145-025-01772-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s12145-025-01772-6\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s12145-025-01772-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,6]],"date-time":"2025-09-06T04:50:55Z","timestamp":1757134255000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s12145-025-01772-6"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,2,12]]},"references-count":34,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2025,6]]}},"alternative-id":["1772"],"URL":"https:\/\/doi.org\/10.1007\/s12145-025-01772-6","relation":{},"ISSN":["1865-0473","1865-0481"],"issn-type":[{"type":"print","value":"1865-0473"},{"type":"electronic","value":"1865-0481"}],"subject":[],"published":{"date-parts":[[2025,2,12]]},"assertion":[{"value":"9 December 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"2 February 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"12 February 2025","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":"Competing interests"}}],"article-number":"253"}}