{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,13]],"date-time":"2026-04-13T13:48:19Z","timestamp":1776088099541,"version":"3.50.1"},"reference-count":56,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","license":[{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/legalcode"}],"funder":[{"name":"Politecnico di Milano for providing Open Access within the CRUI CARE Agreement"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Access"],"published-print":{"date-parts":[[2024]]},"DOI":"10.1109\/access.2024.3511113","type":"journal-article","created":{"date-parts":[[2024,12,4]],"date-time":"2024-12-04T19:13:36Z","timestamp":1733339616000},"page":"184230-184256","source":"Crossref","is-referenced-by-count":5,"title":["Nature-Inspired Driven Deep-AI Algorithms for Wind Speed Prediction"],"prefix":"10.1109","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5322-9808","authenticated-orcid":false,"given":"Muhammad","family":"Dilshad Sabir","sequence":"first","affiliation":[{"name":"Department of Electrical and Computer Engineering, COMSATS University Islamabad, Islamabad, Pakistan"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3659-3824","authenticated-orcid":false,"given":"Laiq","family":"Khan","sequence":"additional","affiliation":[{"name":"Department of Electrical and Computer Engineering, COMSATS University Islamabad, Islamabad, Pakistan"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8969-8596","authenticated-orcid":false,"given":"Kamran","family":"Hafeez","sequence":"additional","affiliation":[{"name":"Department of Electrical and Computer Engineering, COMSATS University Islamabad, Islamabad, Pakistan"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7330-6129","authenticated-orcid":false,"given":"Zahid","family":"Ullah","sequence":"additional","affiliation":[{"name":"Dipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano, Milan, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1341-8276","authenticated-orcid":false,"given":"Stanislaw","family":"Czapp","sequence":"additional","affiliation":[{"name":"Faculty of Electrical and Control Engineering, Gdansk University of Technology, Gdansk, Poland"}]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2019.2906402"},{"issue":"4","key":"ref2","doi-asserted-by":"crossref","first-page":"420","DOI":"10.1016\/j.esd.2011.09.001","article-title":"Selection of renewable energy technologies for a developing county: A case of Pakistan","volume":"15","author":"Amer","year":"2011","journal-title":"Energy Sustain. Develop."},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1063\/5.0206835"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1088\/1748-9326\/3\/2\/025001"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.3390\/en16031515"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.3390\/s22239314"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1016\/j.prime.2024.100755"},{"issue":"2","key":"ref8","doi-asserted-by":"crossref","first-page":"64","DOI":"10.1016\/S1755-0084(09)70092-4","article-title":"The importance of wind forecasting","volume":"10","author":"Lerner","year":"2009","journal-title":"Renew. Energy Focus"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1049\/iet-rpg.2019.1058"},{"key":"ref10","doi-asserted-by":"crossref","first-page":"155","DOI":"10.1016\/j.rser.2015.07.062","article-title":"Critical aspects of wind energy systems in smart grid applications","volume":"52","author":"Colak","year":"2015","journal-title":"Renew. Sustain. Energy Rev."},{"key":"ref11","first-page":"8079","article-title":"Analysis of the influence of the wind speed profile on wind power production","volume-title":"Energy Rep.","volume":"8","author":"Lopez-Villalobos","year":"2022"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2024.3373312"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.3390\/en16145498"},{"key":"ref14","doi-asserted-by":"crossref","first-page":"842","DOI":"10.1016\/j.renene.2019.05.039","article-title":"Smart wind speed deep learning based multi-step forecasting model using singular spectrum analysis, convolutional gated recurrent unit network and support vector regression","volume":"143","author":"Liu","year":"2019","journal-title":"Renew. Energy"},{"key":"ref15","doi-asserted-by":"crossref","first-page":"840","DOI":"10.1016\/j.energy.2018.09.118","article-title":"Deep belief network based k-means cluster approach for short-term wind power forecasting","volume":"165","author":"Wang","year":"2018","journal-title":"Energy"},{"key":"ref16","doi-asserted-by":"crossref","DOI":"10.1016\/j.enconman.2020.113731","article-title":"An improved residual-based convolutional neural network for very short-term wind power forecasting","volume":"228","author":"Yildiz","year":"2021","journal-title":"Energy Convers. Manage."},{"key":"ref17","doi-asserted-by":"crossref","first-page":"270","DOI":"10.1016\/j.apenergy.2019.04.047","article-title":"Wind speed prediction method using shared weight long short-term memory network and Gaussian process regression","volume":"247","author":"Zhang","year":"2019","journal-title":"Appl. Energy"},{"key":"ref18","doi-asserted-by":"crossref","first-page":"393","DOI":"10.1016\/j.neucom.2019.08.108","article-title":"Wind speed forecasting using deep neural network with feature selection","volume":"397","author":"Liu","year":"2020","journal-title":"Neurocomputing"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1109\/EI2.2018.8582536"},{"issue":"10","key":"ref20","doi-asserted-by":"crossref","first-page":"3693","DOI":"10.3390\/su10103693","article-title":"Wind speed forecasting method using EEMD and the combination forecasting method based on GPR and LSTM","volume":"10","author":"Huang","year":"2018","journal-title":"Sustainability"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1088\/1755-1315\/186\/5\/012020"},{"key":"ref22","doi-asserted-by":"crossref","DOI":"10.1016\/j.energy.2020.117081","article-title":"Wind power forecasting using attention-based gated recurrent unit network","volume":"196","author":"Niu","year":"2020","journal-title":"Energy"},{"issue":"7","key":"ref23","doi-asserted-by":"crossref","first-page":"1772","DOI":"10.3390\/en13071772","article-title":"Multi-step short-term wind speed prediction using a residual dilated causal convolutional network with nonlinear attention","volume":"13","author":"Shivam","year":"2020","journal-title":"Energies"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1007\/s10489-022-04312-7"},{"key":"ref25","doi-asserted-by":"crossref","first-page":"13","DOI":"10.1127\/metz\/2021\/1080","article-title":"LiDAR-based minute-scale offshore wind speed forecasts analysed under different atmospheric conditions","volume":"31","author":"Theuer","year":"2021","journal-title":"Meteorologische Zeitschrift"},{"key":"ref26","article-title":"An adaptive deep learning framework for day-ahead forecasting of photovoltaic power generation","volume":"52","author":"Luo","year":"2022","journal-title":"Sustain. Energy Technol. Assessments"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1007\/s11356-022-18655-8"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1109\/TSTE.2011.2114680"},{"key":"ref29","doi-asserted-by":"crossref","DOI":"10.1016\/j.engappai.2020.104133","article-title":"Current status of hybrid structures in wind forecasting","volume":"99","author":"Ahmadi","year":"2021","journal-title":"Eng. Appl. Artif. Intell."},{"key":"ref30","article-title":"Wind speed prediction system based on data pre-processing strategy and multi-objective dragonfly optimization algorithm","volume":"47","author":"Zhang","year":"2021","journal-title":"Sustain. Energy Technol. Assessments"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1007\/s10462-024-10728-z"},{"key":"ref32","doi-asserted-by":"crossref","DOI":"10.1016\/j.apenergy.2021.116842","article-title":"Wind speed forecasting system based on gated recurrent units and convolutional spiking neural networks","volume":"292","author":"Wei","year":"2021","journal-title":"Appl. Energy"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2020.3011060"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1007\/s11042-017-4443-1"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1155\/2021\/1802492"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-99079-4_19"},{"key":"ref37","author":"Karabo\u00c3Ya","year":"2005","journal-title":"An Idea Based on Honey Bee Swarm for Numerical Optimization"},{"issue":"12","key":"ref38","doi-asserted-by":"crossref","first-page":"4831","DOI":"10.1016\/j.cnsns.2012.05.010","article-title":"Krill herd: A new bio-inspired optimization algorithm","volume":"17","author":"Gandomi","year":"2012","journal-title":"Commun. Nonlinear Sci. Numer. Simul."},{"key":"ref39","doi-asserted-by":"crossref","first-page":"371","DOI":"10.1016\/j.neucom.2015.06.083","article-title":"Binary grey wolf optimization approaches for feature selection","volume":"172","author":"Emary","year":"2016","journal-title":"Neurocomputing"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.1109\/TSTE.2019.2890875"},{"key":"ref41","doi-asserted-by":"publisher","DOI":"10.1109\/TII.2016.2543004"},{"key":"ref42","doi-asserted-by":"crossref","first-page":"116","DOI":"10.1016\/j.rser.2017.02.043","article-title":"Short-term scheduling of hydro-based power plants considering application of heuristic algorithms: A comprehensive review","volume":"74","author":"Nazari-Heris","year":"2017","journal-title":"Renew. Sustain. Energy Rev."},{"key":"ref43","doi-asserted-by":"publisher","DOI":"10.1098\/rspa.1998.0193"},{"key":"ref44","doi-asserted-by":"publisher","DOI":"10.1142\/s1793536909000047"},{"key":"ref45","doi-asserted-by":"publisher","DOI":"10.1109\/ICASSP.2011.5947265"},{"issue":"2117","key":"ref46","first-page":"1291","article-title":"Multivariate empirical mode decomposition","volume":"466","author":"Rehman","year":"2010","journal-title":"Proc. Roy. Soc. A, Math., Phys. Eng. Sci."},{"key":"ref47","doi-asserted-by":"publisher","DOI":"10.1109\/LSP.2007.904710"},{"key":"ref48","doi-asserted-by":"publisher","DOI":"10.1103\/PhysRevE.92.032916"},{"key":"ref49","doi-asserted-by":"crossref","DOI":"10.1016\/j.energy.2021.120647","article-title":"Ensemble of machine learning and spatiotemporal parameters to forecast very short-term solar irradiation to compute photovoltaic generators\u2019 output power","volume":"229","author":"Rodr\u00edguez","year":"2021","journal-title":"Energy"},{"key":"ref50","doi-asserted-by":"publisher","DOI":"10.1016\/s1001-6058(13)60338-8"},{"key":"ref51","doi-asserted-by":"publisher","DOI":"10.1109\/IAS.2019.8911916"},{"key":"ref52","doi-asserted-by":"crossref","first-page":"585","DOI":"10.1016\/j.enconman.2015.07.001","article-title":"A new method to adequate assessment of wind farms\u2019 power output","volume":"103","author":"Zolfaghari","year":"2015","journal-title":"Energy Convers. Manage."},{"key":"ref53","doi-asserted-by":"crossref","DOI":"10.1016\/j.apor.2021.102937","article-title":"Offshore wind speed forecasting at different heights by using ensemble empirical mode decomposition and deep learning models","volume":"117","author":"Saxena","year":"2021","journal-title":"Appl. Ocean Res."},{"issue":"10","key":"ref54","doi-asserted-by":"crossref","first-page":"206","DOI":"10.3390\/computers12100206","article-title":"The potential of machine learning for wind speed and direction short-term forecasting: A systematic review","volume":"12","author":"Alves","year":"2023","journal-title":"Computers"},{"key":"ref55","first-page":"9919","article-title":"Short-term photovoltaic power prediction based on modal reconstruction and hybrid deep learning model","volume-title":"Energy Rep.","volume":"8","author":"Li","year":"2022"},{"key":"ref56","first-page":"1","article-title":"Adam: A method for stochastic optimization","volume-title":"Proc. 3rd Int. Conf. Learn. Represent.","author":"Kingma"}],"container-title":["IEEE Access"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx8\/6287639\/10380310\/10777018.pdf?arnumber=10777018","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,12,17]],"date-time":"2024-12-17T05:45:55Z","timestamp":1734414355000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/10777018\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024]]},"references-count":56,"URL":"https:\/\/doi.org\/10.1109\/access.2024.3511113","relation":{},"ISSN":["2169-3536"],"issn-type":[{"value":"2169-3536","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024]]}}}