{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,7]],"date-time":"2026-02-07T19:33:31Z","timestamp":1770492811545,"version":"3.49.0"},"reference-count":23,"publisher":"Springer Science and Business Media LLC","issue":"6","license":[{"start":{"date-parts":[[2022,1,10]],"date-time":"2022-01-10T00:00:00Z","timestamp":1641772800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2022,1,10]],"date-time":"2022-01-10T00:00:00Z","timestamp":1641772800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Supercomput"],"published-print":{"date-parts":[[2022,4]]},"DOI":"10.1007\/s11227-021-04244-y","type":"journal-article","created":{"date-parts":[[2022,1,10]],"date-time":"2022-01-10T00:03:16Z","timestamp":1641772996000},"page":"8560-8576","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["A novel elephant herd optimization model with a deep extreme Learning machine for solar radiation prediction using weather forecasts"],"prefix":"10.1007","volume":"78","author":[{"given":"K. Nageswara","family":"Reddy","sequence":"first","affiliation":[]},{"given":"M.","family":"Thillaikarasi","sequence":"additional","affiliation":[]},{"given":"B. Siva","family":"Kumar","sequence":"additional","affiliation":[]},{"given":"T.","family":"Suresh","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,1,10]]},"reference":[{"key":"4244_CR1","doi-asserted-by":"publisher","first-page":"42","DOI":"10.1016\/j.enconman.2015.02.020","volume":"95","author":"H Jiang","year":"2015","unstructured":"Jiang H, Dong Y, Wang J, Li Y (2015) Intelligent optimization models based on hard-ridge penalty and RBF for forecasting global solar radiation. Energy Convers Manage 95:42\u201358","journal-title":"Energy Convers Manage"},{"issue":"5","key":"4244_CR2","doi-asserted-by":"publisher","first-page":"5043","DOI":"10.1016\/j.eswa.2011.11.036","volume":"39","author":"M Ozgoren","year":"2012","unstructured":"Ozgoren M, Bilgili M, Sahin B (2012) Estimation of global solar radiation using ANN over Turkey. Expert Syst Appl 39(5):5043\u20135051","journal-title":"Expert Syst Appl"},{"key":"4244_CR3","doi-asserted-by":"publisher","first-page":"542","DOI":"10.1016\/j.energy.2014.09.064","volume":"77","author":"HAN Hejase","year":"2014","unstructured":"Hejase HAN, Al-Shamisi MH, Assi AH (2014) Modeling of global horizontal irradiance in the United Arab Emirates with artifcial neural networks. Energy 77:542\u2013552","journal-title":"Energy"},{"key":"4244_CR4","doi-asserted-by":"publisher","first-page":"1298","DOI":"10.1016\/j.jclepro.2016.07.049","volume":"135","author":"C Renno","year":"2016","unstructured":"Renno C, Petito F, Gatto A (2016) ANN model for predicting the direct normal irradiance and the global radiation for a solar application to a residential building. J Clean Prod 135:1298\u20131316","journal-title":"J Clean Prod"},{"issue":"2","key":"4244_CR5","doi-asserted-by":"publisher","first-page":"274","DOI":"10.1002\/joc.2267","volume":"32","author":"W Wu","year":"2012","unstructured":"Wu W, Liu H-B (2012) Assessment of monthly solar radiation estimates using support vector machines and air temperatures. Int J Climatol 32(2):274\u2013285","journal-title":"Int J Climatol"},{"key":"4244_CR6","doi-asserted-by":"publisher","first-page":"311","DOI":"10.1016\/j.enconman.2013.06.034","volume":"75","author":"J-L Chen","year":"2013","unstructured":"Chen J-L, Li G-S, Wu S-J (2013) Assessing the potential of support vector machine for estimating daily solar radiation using sunshine duration. Energy Convers Manage 75:311\u2013318","journal-title":"Energy Convers Manage"},{"issue":"2\u20133","key":"4244_CR7","doi-asserted-by":"publisher","first-page":"161","DOI":"10.1016\/j.applthermaleng.2004.06.017","volume":"25","author":"S Cao","year":"2005","unstructured":"Cao S, Cao J (2005) Forecast of solar irradiance using recurrent neural networks combined with wavelet analysis. Appl Termal Eng 25(2\u20133):161\u2013172","journal-title":"Appl Termal Eng"},{"key":"4244_CR8","doi-asserted-by":"publisher","first-page":"411","DOI":"10.1016\/j.renene.2017.08.061","volume":"115","author":"A Rohani","year":"2018","unstructured":"Rohani A, Taki M, Abdollahpour M (2018) A novel sof computing model (Gaussian process regression with K-fold cross validation) for daily and monthly solar radiation forecasting (Part: I). J Renew Energy 115:411\u2013422","journal-title":"J Renew Energy"},{"issue":"6","key":"4244_CR9","doi-asserted-by":"publisher","first-page":"211","DOI":"10.1140\/epjp\/i2018-12029-7","volume":"133","author":"M Guermoui","year":"2018","unstructured":"Guermoui M, Gairaa K, Rabehi A, Djafer D, Benkaciali S (2018) Estimation of the daily global solar radiation based on the Gaussian process regression methodology in the Saharan climate. Te Eur Phys J Plus 133(6):211","journal-title":"Te Eur Phys J Plus"},{"issue":"9","key":"4244_CR10","doi-asserted-by":"publisher","first-page":"4603","DOI":"10.1016\/j.proeng.2011.08.864","volume":"150","author":"J Wang","year":"2011","unstructured":"Wang J, Xie Y, Zhu C, Xu X (2011) Solar radiation prediction based on phase space reconstruction of wavelet neural network. Proc Eng 150(9):4603\u20134607","journal-title":"Proc Eng"},{"key":"4244_CR11","doi-asserted-by":"publisher","first-page":"288","DOI":"10.1016\/j.energy.2016.11.061","volume":"119","author":"S Monjoly","year":"2017","unstructured":"Monjoly S, Andr\u2019e M, Calif R, Soubdhan T (2017) Hourly forecasting of global solar radiation based on multiscale decomposition methods: a hybrid approach. Energy 119:288\u2013298","journal-title":"Energy"},{"issue":"10","key":"4244_CR12","doi-asserted-by":"publisher","first-page":"222","DOI":"10.3390\/electronics7100222","volume":"7","author":"M Fayaz","year":"2018","unstructured":"Fayaz M, Kim D (2018) A prediction methodology of energy consumption based on deep extreme learning machine and comparative analysis in residential buildings. Electronics 7(10):222","journal-title":"Electronics"},{"key":"4244_CR13","doi-asserted-by":"publisher","first-page":"24196","DOI":"10.1109\/ACCESS.2018.2830651","volume":"6","author":"SK Lakshmanaprabu","year":"2018","unstructured":"Lakshmanaprabu SK, Shankar K, Khanna A, Gupta D, Rodrigues JJ, Pinheiro PR, De Albuquerque VHC (2018) Effective features to classify big data using social internet of things. IEEE access 6:24196\u201324204","journal-title":"IEEE access"},{"key":"4244_CR14","unstructured":"https:\/\/www.kaggle.com\/dronio\/SolarEnergy"},{"issue":"8","key":"4244_CR15","doi-asserted-by":"publisher","first-page":"7159","DOI":"10.1007\/s13369-019-03841-7","volume":"44","author":"K Basaran","year":"2019","unstructured":"Basaran K, \u00d6z\u00e7ift A, K\u0131l\u0131n\u00e7 D (2019) A new approach for prediction of solar radiation with using ensemble learning algorithm. Arab J Sci Eng 44(8):7159\u20137171","journal-title":"Arab J Sci Eng"},{"key":"4244_CR16","doi-asserted-by":"publisher","first-page":"151","DOI":"10.1016\/j.neucom.2017.05.104","volume":"326","author":"A Torres-Barr\u00e1n","year":"2019","unstructured":"Torres-Barr\u00e1n A, Alonso \u00c1, Dorronsoro JR (2019) Regression tree ensembles for wind energy and solar radiation prediction. Neurocomputing 326:151\u2013160","journal-title":"Neurocomputing"},{"issue":"10","key":"4244_CR17","doi-asserted-by":"publisher","first-page":"1856","DOI":"10.3390\/en12101856","volume":"12","author":"M Husein","year":"2019","unstructured":"Husein M, Chung IY (2019) Day-ahead solar irradiance forecasting for microgrids using a long short-term memory recurrent neural network: A deep learning approach. Energies 12(10):1856","journal-title":"Energies"},{"key":"4244_CR18","doi-asserted-by":"publisher","first-page":"461","DOI":"10.1016\/j.energy.2018.01.177","volume":"148","author":"X Qing","year":"2018","unstructured":"Qing X, Niu Y (2018) Hourly day-ahead solar irradiance prediction using weather forecasts by LSTM. Energy 148:461\u2013468","journal-title":"Energy"},{"key":"4244_CR19","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s12517-020-05355-1","volume":"13","author":"B Mohammadi","year":"2020","unstructured":"Mohammadi B, Aghashariatmadari Z (2020) Estimation of solar radiation using neighboring stations through hybrid support vector regression boosted by Krill Herd algorithm. Arab J Geosci 13:1\u201316","journal-title":"Arab J Geosci"},{"key":"4244_CR20","doi-asserted-by":"publisher","first-page":"9629","DOI":"10.1007\/s11227-020-03219-9","volume":"76","author":"R Moreno","year":"2020","unstructured":"Moreno R, Arias E, Cazorla D et al (2020) Seeking the best Weather Research and Forecasting model performance: an empirical score approach. J Supercomput 76:9629\u20139653. https:\/\/doi.org\/10.1007\/s11227-020-03219-9","journal-title":"J Supercomput"},{"key":"4244_CR21","doi-asserted-by":"publisher","first-page":"868","DOI":"10.1007\/s11227-017-2166-8","volume":"74","author":"M Denham","year":"2018","unstructured":"Denham M, Lamperti E, Areta J (2018) Weather radar data processing on graphic cards. J Supercomput 74:868\u2013885. https:\/\/doi.org\/10.1007\/s11227-017-2166-8","journal-title":"J Supercomput"},{"key":"4244_CR22","doi-asserted-by":"publisher","DOI":"10.1007\/s11227-021-03898-y","author":"J Luo","year":"2021","unstructured":"Luo J, Zhao C, Chen Q et al (2021) Using deep belief network to construct the agricultural information system based on Internet of Things. J Supercomput. https:\/\/doi.org\/10.1007\/s11227-021-03898-y","journal-title":"J Supercomput"},{"key":"4244_CR23","doi-asserted-by":"publisher","first-page":"1354","DOI":"10.1007\/s11227-020-03329-4","volume":"77","author":"Z Wang","year":"2021","unstructured":"Wang Z, Guo N, Wang S et al (2021) Prediction of highway asphalt pavement performance based on Markov chain and artificial neural network approach. J Supercomput 77:1354\u20131376. https:\/\/doi.org\/10.1007\/s11227-020-03329-4","journal-title":"J Supercomput"}],"container-title":["The Journal of Supercomputing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11227-021-04244-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11227-021-04244-y\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11227-021-04244-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,4,1]],"date-time":"2022-04-01T14:02:49Z","timestamp":1648821769000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11227-021-04244-y"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,1,10]]},"references-count":23,"journal-issue":{"issue":"6","published-print":{"date-parts":[[2022,4]]}},"alternative-id":["4244"],"URL":"https:\/\/doi.org\/10.1007\/s11227-021-04244-y","relation":{},"ISSN":["0920-8542","1573-0484"],"issn-type":[{"value":"0920-8542","type":"print"},{"value":"1573-0484","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,1,10]]},"assertion":[{"value":"30 November 2021","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"10 January 2022","order":2,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}