{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,29]],"date-time":"2025-12-29T13:46:50Z","timestamp":1767016010029},"reference-count":38,"publisher":"Springer Science and Business Media LLC","issue":"12","license":[{"start":{"date-parts":[[2019,6,17]],"date-time":"2019-06-17T00:00:00Z","timestamp":1560729600000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2019,6,17]],"date-time":"2019-06-17T00:00:00Z","timestamp":1560729600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Neural Comput &amp; Applic"],"published-print":{"date-parts":[[2020,6]]},"DOI":"10.1007\/s00521-019-04290-x","type":"journal-article","created":{"date-parts":[[2019,6,17]],"date-time":"2019-06-17T18:10:26Z","timestamp":1560795026000},"page":"8011-8029","update-policy":"http:\/\/dx.doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":24,"title":["Short-term solar power prediction using multi-kernel-based random vector functional link with water cycle algorithm-based parameter optimization"],"prefix":"10.1007","volume":"32","author":[{"given":"Irani","family":"Majumder","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"P. K.","family":"Dash","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ranjeeta","family":"Bisoi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2019,6,17]]},"reference":[{"issue":"1","key":"4290_CR1","doi-asserted-by":"publisher","first-page":"2","DOI":"10.1109\/JSTARS.2009.2020300","volume":"2","author":"E Lorenz","year":"2009","unstructured":"Lorenz E, Hurka J, Heinemann D, Beyer HG (2009) Irradiance forecasting for the power prediction of grid-connected photovoltaic systems. IEEE J Sel Top Appl Earth Obs Remote Sens 2(1):2\u201310","journal-title":"IEEE J Sel Top Appl Earth Obs Remote Sens"},{"key":"4290_CR2","doi-asserted-by":"publisher","first-page":"78","DOI":"10.1016\/j.solener.2016.06.069","volume":"136","author":"J Antonanzas","year":"2016","unstructured":"Antonanzas J, Osorio N, Escobar R, Urraca R, Martinez-de-Pison FJ, Antonanzas-Torres F (2016) Review of photovoltaic power forecasting. Sol Energy 136:78\u2013111","journal-title":"Sol Energy"},{"issue":"3","key":"4290_CR3","doi-asserted-by":"publisher","first-page":"256","DOI":"10.1016\/j.epsr.2009.09.006","volume":"80","author":"SS Pappas","year":"2010","unstructured":"Pappas SS, Ekonomou L, Karampelas P, Karamousantas DC, Katsikas SK, Chatzarakis GE, Skafidas PD (2010) Electricity demand load forecasting of the Hellenic power system using an ARMA model. Electr Power Syst Res 80(3):256\u2013264","journal-title":"Electr Power Syst Res"},{"issue":"4","key":"4290_CR4","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","journal-title":"Appl Energy"},{"issue":"3","key":"4290_CR5","doi-asserted-by":"publisher","first-page":"775","DOI":"10.1109\/TEC.2009.2025431","volume":"24","author":"JW Taylor","year":"2009","unstructured":"Taylor JW, McSharry PE, Buizza R (2009) Wind power density forecasting using ensemble predictions and time series models. IEEE Trans Energy Convers 24(3):775\u2013782","journal-title":"IEEE Trans Energy Convers"},{"issue":"11","key":"4290_CR6","doi-asserted-by":"publisher","first-page":"3606","DOI":"10.1016\/j.apenergy.2010.05.012","volume":"87","author":"Z Tan","year":"2010","unstructured":"Tan Z, Zhang J, Wang J, Xu J (2010) Day-ahead electricity price forecasting using wavelet transform combined with ARIMA and GARCH models. Appl Energy 87(11):3606\u20133610","journal-title":"Appl Energy"},{"issue":"2","key":"4290_CR7","doi-asserted-by":"publisher","first-page":"867","DOI":"10.1109\/TPWRS.2005.846044","volume":"20","author":"RC Garcia","year":"2005","unstructured":"Garcia RC, Contreras J, Van Akkeren M, Garcia JBC (2005) A GARCH forecasting model to predict day-ahead electricity prices. IEEE Trans Power Syst 20(2):867\u2013874","journal-title":"IEEE Trans Power Syst"},{"issue":"10","key":"4290_CR8","doi-asserted-by":"publisher","first-page":"1772","DOI":"10.1016\/j.solener.2010.07.002","volume":"84","author":"L Mart\u00edn","year":"2010","unstructured":"Mart\u00edn L, Zarzalejo LF, Polo J, Navarro A, Marchante R, Cony M (2010) Prediction of global solar irradiance based on time series analysis: application to solar thermal power plants energy production planning. Sol Energy 84(10):1772\u20131781","journal-title":"Sol Energy"},{"key":"4290_CR9","doi-asserted-by":"publisher","first-page":"247","DOI":"10.1016\/j.apenergy.2018.01.035","volume":"213","author":"LM Halabi","year":"2018","unstructured":"Halabi LM, Mekhilef S, Hossain M (2018) Performance evaluation of hybrid adaptive neuro-fuzzy inference system models for predicting monthly global solar radiation. Appl Energy 213:247\u2013261","journal-title":"Appl Energy"},{"key":"4290_CR10","doi-asserted-by":"publisher","first-page":"871","DOI":"10.1016\/j.renene.2018.08.044","volume":"132","author":"L Benali","year":"2019","unstructured":"Benali L, Notton G, Fouilloy A, Voyant C, Dizene R (2019) Solar radiation forecasting using artificial neural network and random forest methods: application to normal beam, horizontal diffuse and global components. Renew Energy 132:871\u2013884","journal-title":"Renew Energy"},{"issue":"5","key":"4290_CR11","doi-asserted-by":"publisher","first-page":"807","DOI":"10.1016\/j.solener.2010.02.006","volume":"84","author":"A Mellit","year":"2010","unstructured":"Mellit A, Pavan AM (2010) A 24-h forecast of solar irradiance using artificial neural network: application for performance prediction of a grid-connected PV plant at Trieste, Italy. Solar Energy 84(5):807\u2013821","journal-title":"Solar Energy"},{"issue":"4","key":"4290_CR12","doi-asserted-by":"publisher","first-page":"915","DOI":"10.1016\/j.rser.2008.02.002","volume":"13","author":"M Lei","year":"2009","unstructured":"Lei M, Shiyan L, Chuanwen J, Hongling L, Yan Z (2009) A review on the forecasting of wind speed and generated power. Renew Sustain Energy Rev 13(4):915\u2013920","journal-title":"Renew Sustain Energy Rev"},{"key":"4290_CR13","doi-asserted-by":"publisher","first-page":"357","DOI":"10.1016\/j.renene.2017.11.011","volume":"118","author":"AT Eseye","year":"2018","unstructured":"Eseye AT, Zhang J, Zheng D (2018) Short-term photovoltaic solar power forecasting using a hybrid Wavelet-PSO-SVM model based on SCADA and Meteorological information. Renew Energy 118:357\u2013367","journal-title":"Renew Energy"},{"key":"4290_CR14","doi-asserted-by":"publisher","first-page":"118","DOI":"10.1016\/j.renene.2012.10.009","volume":"52","author":"J Zeng","year":"2013","unstructured":"Zeng J, Qiao W (2013) Short-term solar power prediction using a support vector machine. Renew Energy 52:118\u2013127","journal-title":"Renew Energy"},{"issue":"3","key":"4290_CR15","doi-asserted-by":"publisher","first-page":"791","DOI":"10.1109\/TSTE.2015.2406814","volume":"6","author":"L Yang","year":"2015","unstructured":"Yang L, He M, Zhang J, Vittal V (2015) Support-vector-machine-enhanced markov model for short-term wind power forecast. IEEE Trans Sustain Energy 6(3):791\u2013799","journal-title":"IEEE Trans Sustain Energy"},{"issue":"7","key":"4290_CR16","doi-asserted-by":"publisher","first-page":"1009","DOI":"10.1049\/iet-rpg.2018.5649","volume":"13","author":"MN Akhter","year":"2019","unstructured":"Akhter MN, Mekhilef S, Mokhlis H, Shah NM (2019) Review on forecasting of photovoltaic power generation based on machine learning and metaheuristic techniques. IET Renew Power Gen 13(7):1009\u20131023","journal-title":"IET Renew Power Gen"},{"issue":"1","key":"4290_CR17","doi-asserted-by":"publisher","first-page":"341","DOI":"10.1016\/j.energy.2012.01.006","volume":"39","author":"C Voyant","year":"2012","unstructured":"Voyant C, Muselli M, Paoli C, Nivet ML (2012) Numerical weather prediction (NWP) and hybrid ARMA\/ANN model to predict global radiation. Energy 39(1):341\u2013355","journal-title":"Energy"},{"issue":"12","key":"4290_CR18","doi-asserted-by":"publisher","first-page":"2732","DOI":"10.1016\/j.renene.2010.04.022","volume":"35","author":"E Cadenas","year":"2010","unstructured":"Cadenas E, Rivera W (2010) Wind speed forecasting in three different regions of Mexico, using a hybrid ARIMA\u2013ANN model. Renew Energy 35(12):2732\u20132738","journal-title":"Renew Energy"},{"key":"4290_CR19","doi-asserted-by":"publisher","first-page":"1094","DOI":"10.1016\/j.ins.2015.09.025","volume":"367","author":"L Zhang","year":"2016","unstructured":"Zhang L, Suganthan PN (2016) A comprehensive evaluation of random vector functional link networks. Inf Sci 367:1094\u20131105","journal-title":"Inf Sci"},{"key":"4290_CR20","doi-asserted-by":"publisher","first-page":"182","DOI":"10.1016\/j.knosys.2018.01.015","volume":"145","author":"X Qiu","year":"2018","unstructured":"Qiu X, Suganthan PN, Amaratunga GA (2018) Ensemble incremental learning random vector functional link network for short-term electric load forecasting. Knowl Based Syst 145:182\u2013196","journal-title":"Knowl Based Syst"},{"key":"4290_CR21","doi-asserted-by":"publisher","first-page":"1109","DOI":"10.1016\/j.asoc.2018.03.013","volume":"70","author":"PA Henr\u00edquez","year":"2018","unstructured":"Henr\u00edquez PA, Ruz GA (2018) A non-iterative method for pruning hidden neurons in neural networks with random weights. Appl Soft Comput 70:1109\u20131121","journal-title":"Appl Soft Comput"},{"key":"4290_CR22","doi-asserted-by":"publisher","first-page":"58","DOI":"10.1016\/j.asoc.2015.01.050","volume":"30","author":"A Sadollah","year":"2015","unstructured":"Sadollah A, Eskandar H, Bahreininejad A, Kim JH (2015) Water cycle algorithm with evaporation rate for solving constrained and unconstrained optimization problems. Appl Soft Comput 30:58\u201371","journal-title":"Appl Soft Comput"},{"key":"4290_CR23","first-page":"1","volume":"2018","author":"R Bisoi","year":"2018","unstructured":"Bisoi R, Dash PK, Das PP (2018) Short-term electricity price forecasting and classification in smart grids using optimized multikernel extreme learning machine. Neural Comput Appl 2018:1\u201324","journal-title":"Neural Comput Appl"},{"key":"4290_CR24","doi-asserted-by":"publisher","first-page":"69","DOI":"10.1016\/j.compstruc.2016.01.008","volume":"167","author":"A Kaveh","year":"2016","unstructured":"Kaveh A, Bakhshpoori T (2016) Water evaporation optimization: a novel physically inspired optimization algorithm. Comput Struct 167:69\u201385","journal-title":"Comput Struct"},{"key":"4290_CR25","doi-asserted-by":"publisher","first-page":"146","DOI":"10.1016\/j.ins.2016.01.039","volume":"364","author":"L Zhang","year":"2016","unstructured":"Zhang L, Suganthan PN (2016) A survey of randomized algorithms for training neural networks. Inf Sci 364:146\u2013155","journal-title":"Inf Sci"},{"key":"4290_CR26","doi-asserted-by":"publisher","unstructured":"Mellit A, Kalogirou SA (2018) A survey on the application of artificial intelligence techniques for photovoltaic systems. In: Kalogirou SA (ed) McEvoy\u2019s handbook of photovoltaics. Academic Press, Cambridge, pp 735\u2013761. \nhttps:\/\/doi.org\/10.1016\/B978-0-12-809921-6.00019-7","DOI":"10.1016\/B978-0-12-809921-6.00019-7"},{"key":"4290_CR27","doi-asserted-by":"publisher","first-page":"367","DOI":"10.1016\/j.renene.2019.02.087","volume":"140","author":"W VanDeventer","year":"2019","unstructured":"VanDeventer W, Jamei E, Thirunavukkarasu GS, Seyedmahmoudian M, Soon TK, Horan B, Stojcevski A (2019) Short-term PV power forecasting using hybrid GASVM technique. Renew Energy 140:367\u2013379","journal-title":"Renew Energy"},{"issue":"7","key":"4290_CR28","doi-asserted-by":"publisher","first-page":"2017","DOI":"10.1016\/j.solener.2012.04.004","volume":"86","author":"HT Pedro","year":"2012","unstructured":"Pedro HT, Coimbra CF (2012) Assessment of forecasting techniques for solar power production with no exogenous inputs. Sol Energy 86(7):2017\u20132028","journal-title":"Sol Energy"},{"issue":"4","key":"4290_CR29","doi-asserted-by":"publisher","first-page":"3341","DOI":"10.1109\/TSG.2016.2628061","volume":"9","author":"H Jiang","year":"2016","unstructured":"Jiang H, Zhang Y, Muljadi E, Zhang JJ, Gao DW (2016) A short-term and high-resolution distribution system load forecasting approach using support vector regression with hybrid parameters optimization. IEEE Trans Smart Grid 9(4):3341\u20133350","journal-title":"IEEE Trans Smart Grid"},{"issue":"5","key":"4290_CR30","doi-asserted-by":"publisher","first-page":"2997","DOI":"10.1016\/j.asoc.2012.11.033","volume":"13","author":"A Khare","year":"2013","unstructured":"Khare A, Rangnekar S (2013) A review of particle swarm optimization and its applications in solar photovoltaic system. Appl Soft Comput 13(5):2997\u20133006","journal-title":"Appl Soft Comput"},{"key":"4290_CR31","doi-asserted-by":"publisher","first-page":"226","DOI":"10.1016\/j.knosys.2013.11.015","volume":"56","author":"C Ren","year":"2014","unstructured":"Ren C, An N, Wang J, Li L, Hu B, Shang D (2014) Optimal parameters selection for BP neural network based on particle swarm optimization: a case study of wind speed forecasting. Knowl Based Syst 56:226\u2013239","journal-title":"Knowl Based Syst"},{"key":"4290_CR32","doi-asserted-by":"publisher","first-page":"627","DOI":"10.1016\/j.energy.2015.01.006","volume":"81","author":"J Wang","year":"2015","unstructured":"Wang J, Jiang H, Wu Y, Dong Y (2015) Forecasting solar radiation using an optimized hybrid model by Cuckoo Search algorithm. Energy 81:627\u2013644","journal-title":"Energy"},{"key":"4290_CR33","doi-asserted-by":"publisher","first-page":"652","DOI":"10.1016\/j.enconman.2013.11.018","volume":"78","author":"S Berrazouane","year":"2014","unstructured":"Berrazouane S, Mohammedi K (2014) Parameter optimization via cuckoo optimization algorithm of fuzzy controller for energy management of a hybrid power system. Energy Convers Manag 78:652\u2013660","journal-title":"Energy Convers Manag"},{"key":"4290_CR34","doi-asserted-by":"publisher","first-page":"360","DOI":"10.1016\/j.enconman.2017.04.007","volume":"143","author":"C Zhang","year":"2017","unstructured":"Zhang C, Zhou J, Li C, Fu W, Peng T (2017) A compound structure of ELM based on feature selection and parameter optimization using hybrid backtracking search algorithm for wind speed forecasting. Energy Convers Manag 143:360\u2013376","journal-title":"Energy Convers Manag"},{"key":"4290_CR35","doi-asserted-by":"publisher","first-page":"912","DOI":"10.1016\/j.rser.2017.08.017","volume":"81","author":"UK Das","year":"2018","unstructured":"Das UK, Tey KS, Seyedmahmoudian M, Mekhilef S, Idris MYI, Van Deventer W, Stojcevski A (2018) Forecasting of photovoltaic power generation and model optimization: a review. Renew Sustain Energy Rev 81:912\u2013928","journal-title":"Renew Sustain Energy Rev"},{"key":"4290_CR36","doi-asserted-by":"publisher","first-page":"91","DOI":"10.1016\/j.solener.2019.03.098","volume":"184","author":"MR Douiri","year":"2019","unstructured":"Douiri MR (2019) Particle swarm optimized neuro-fuzzy system for photovoltaic power forecasting model. Sol Energy 184:91\u2013104","journal-title":"Sol Energy"},{"key":"4290_CR37","doi-asserted-by":"crossref","unstructured":"Zadorozhnyi O, Benecke G, Mandt S, Scheffer T, Kloft M (2016) Huber-norm regularization for linear prediction models. In: Joint European conference on machine learning and knowledge discovery in databases. Springer, Cham, pp 714\u2013730","DOI":"10.1007\/978-3-319-46128-1_45"},{"key":"4290_CR38","unstructured":"https:\/\/www.nrel.gov\/grid\/solar-power-data.html\n\n. 2 Nov 2019"}],"container-title":["Neural Computing and Applications"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-019-04290-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/article\/10.1007\/s00521-019-04290-x\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-019-04290-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2020,6,15]],"date-time":"2020-06-15T23:25:56Z","timestamp":1592263556000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/s00521-019-04290-x"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,6,17]]},"references-count":38,"journal-issue":{"issue":"12","published-print":{"date-parts":[[2020,6]]}},"alternative-id":["4290"],"URL":"https:\/\/doi.org\/10.1007\/s00521-019-04290-x","relation":{},"ISSN":["0941-0643","1433-3058"],"issn-type":[{"value":"0941-0643","type":"print"},{"value":"1433-3058","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,6,17]]},"assertion":[{"value":"22 January 2019","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"1 June 2019","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"17 June 2019","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Compliance with ethical standards"}},{"value":"The authors declare that there is no conflict of interest for this paper with any person or any organization.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}