{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,8]],"date-time":"2026-05-08T09:03:07Z","timestamp":1778230987572,"version":"3.51.4"},"reference-count":36,"publisher":"Springer Science and Business Media LLC","issue":"12","license":[{"start":{"date-parts":[[2016,4,11]],"date-time":"2016-04-11T00:00:00Z","timestamp":1460332800000},"content-version":"tdm","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":[[2017,12]]},"DOI":"10.1007\/s00521-016-2310-z","type":"journal-article","created":{"date-parts":[[2017,10,10]],"date-time":"2017-10-10T07:28:51Z","timestamp":1507620531000},"page":"3981-3992","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":60,"title":["Day-ahead forecasting of solar photovoltaic output power using multilayer perceptron"],"prefix":"10.1007","volume":"28","author":[{"given":"R.","family":"Muhammad Ehsan","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sishaj P.","family":"Simon","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"P. R.","family":"Venkateswaran","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2016,4,11]]},"reference":[{"key":"2310_CR1","unstructured":"International Energy Agency, Technology Roadmap: Solar Photovoltaic Energy\u20142014 Edition. \n                        http:\/\/www.iea.org\/publications\/freepublications\/publication\/TechnologyRoadmapSolarPhotovoltaicEnergy_2014edition.pdf"},{"issue":"5","key":"2310_CR2","doi-asserted-by":"crossref","first-page":"444","DOI":"10.1016\/j.rser.2004.03.004","volume":"9","author":"T Muneer","year":"2005","unstructured":"Muneer T, Asif M, Munawwar S (2005) Sustainable production of solar electricity with particular reference to the Indian economy. Renew Sustain Energy Rev 9(5):444\u2013473","journal-title":"Renew Sustain Energy Rev"},{"issue":"7","key":"2310_CR3","doi-asserted-by":"crossref","first-page":"1388","DOI":"10.1016\/j.rser.2005.12.004","volume":"11","author":"M Asif","year":"2007","unstructured":"Asif M, Muneer T (2007) Energy supply, its demand and security issues for developed and emerging economies. Renew Sustain Energy Rev 11(7):1388\u20131413","journal-title":"Renew Sustain Energy Rev"},{"key":"2310_CR4","unstructured":"Solar Policies, Jawaharlal Nehru National Solar Mission, Ministry of New and Renewable Energy, Government of India. \n                        http:\/\/mnre.gov.in\/file-manager\/UserFiles\/guidelines_sbd_tariff_gridconnected_res\/salient_features_for_State-wise_solar_policies.pdf"},{"key":"2310_CR5","doi-asserted-by":"crossref","first-page":"1423","DOI":"10.1016\/j.rser.2014.09.027","volume":"41","author":"R Shah","year":"2015","unstructured":"Shah R, Mithulananthan N, Bansal RC, Ramachandaramurthy VK (2015) A review of key power system stability challenges for large-scale PV integration. Renew Sustain Energy Rev 41:1423\u20131436","journal-title":"Renew Sustain Energy Rev"},{"key":"2310_CR6","doi-asserted-by":"crossref","first-page":"192","DOI":"10.1016\/j.solener.2016.01.049","volume":"126","author":"CB Martinez-Anido","year":"2016","unstructured":"Martinez-Anido CB, Botor B, Florita AR, Draxl C, Lu S, Hamann HF, Hodge B-M (2016) The value of day-ahead solar power forecasting improvement. Sol Energy 126:192\u2013203","journal-title":"Sol Energy"},{"key":"2310_CR7","doi-asserted-by":"crossref","first-page":"304","DOI":"10.1016\/j.rser.2014.04.057","volume":"36","author":"T Ma","year":"2014","unstructured":"Ma T, Yang H, Lin L (2014) Solar photovoltaic system modeling and performance prediction. Renew Sustain Energy Rev 36:304\u2013315","journal-title":"Renew Sustain Energy Rev"},{"issue":"C","key":"2310_CR8","doi-asserted-by":"crossref","first-page":"511","DOI":"10.1016\/j.solener.2013.09.037","volume":"98","author":"N Fraidenraich","year":"2013","unstructured":"Fraidenraich N, Oliveira C, da Cunha AFV, Gordon JM, Vilela OC (2013) Analytical modeling of direct steam generation solar power plants. Sol Energy 98(C):511\u2013522","journal-title":"Sol Energy"},{"issue":"4","key":"2310_CR9","doi-asserted-by":"crossref","first-page":"483","DOI":"10.1016\/S0196-8904(03)00163-8","volume":"45","author":"AY Al-Hasan","year":"2004","unstructured":"Al-Hasan AY, Ghoneim AA, Abdullah AH (2004) Optimizing electrical load pattern in Kuwait using grid connected photovoltaic systems. Energy Convers Manag 45(4):483\u2013494","journal-title":"Energy Convers Manag"},{"issue":"1\u20132","key":"2310_CR10","doi-asserted-by":"crossref","first-page":"17","DOI":"10.1016\/S0306-2619(00)00005-2","volume":"67","author":"SA Kalogirou","year":"2000","unstructured":"Kalogirou SA (2000) Applications of artificial neural-networks for energy systems. Appl Energy 67(1\u20132):17\u201335","journal-title":"Appl Energy"},{"key":"2310_CR11","doi-asserted-by":"crossref","first-page":"2519","DOI":"10.1016\/S0196-8904(03)00009-8","volume":"44","author":"KS Reddy","year":"2012","unstructured":"Reddy KS, Ranjan M (2012) Solar resource estimation using artificial neural networks and comparison with other correlation models. Energy Convers Manag 44:2519\u20132530","journal-title":"Energy Convers Manag"},{"issue":"2","key":"2310_CR12","doi-asserted-by":"crossref","first-page":"406","DOI":"10.1016\/j.rser.2008.01.006","volume":"13","author":"A Mellit","year":"2009","unstructured":"Mellit A, Kalogirou SA, Hontoria L, Shaari S (2009) Artificial intelligence techniques for sizing photovoltaic systems: a review. Renew Sustain Energy Rev 13(2):406\u2013419","journal-title":"Renew Sustain Energy Rev"},{"issue":"6","key":"2310_CR13","doi-asserted-by":"crossref","first-page":"50","DOI":"10.1109\/MPE.2015.2461351","volume":"13","author":"A Tuohy","year":"2015","unstructured":"Tuohy A, Zack J, Haupt SE, Sharp J (2015) Solar forecasting: methods, challenges, and performance. IEEE Power Energy Mag 13(6):50\u201359","journal-title":"IEEE Power Energy Mag"},{"issue":"2","key":"2310_CR14","doi-asserted-by":"crossref","first-page":"725","DOI":"10.1016\/j.solener.2011.11.013","volume":"86","author":"E Izgi","year":"2012","unstructured":"Izgi E, Oztopal A, Yerli B, Kaymak MK, Sahin AD (2012) Short-mid-term solar power prediction by using artificial neural networks. Sol Energy 86(2):725\u2013733","journal-title":"Sol Energy"},{"key":"2310_CR15","doi-asserted-by":"crossref","first-page":"333","DOI":"10.1016\/j.apenergy.2014.05.055","volume":"130","author":"B Amrouche","year":"2014","unstructured":"Amrouche B, Le Pivert X (2014) Artificial neural network based daily local forecasting for global solar radiation. Appl Energy 130:333\u2013341","journal-title":"Appl Energy"},{"issue":"11","key":"2310_CR16","doi-asserted-by":"crossref","first-page":"1858","DOI":"10.1016\/j.renene.2006.10.003","volume":"32","author":"JH So","year":"2007","unstructured":"So JH, Jung YS, Yu GJ, Choi JY, Choi JH (2007) Performance results and analysis of 3\u00a0kW grid-connected PV systems. Renew Energy 32(11):1858\u20131872","journal-title":"Renew Energy"},{"issue":"1","key":"2310_CR17","doi-asserted-by":"crossref","first-page":"671","DOI":"10.1016\/j.energy.2015.07.089","volume":"90","author":"C Voyant","year":"2015","unstructured":"Voyant C, Soubdhan T, Lauret P, David M, Muselli M (2015) Statistical parameters as a means to a priori assess the accuracy of solar forecasting models. Energy 90(1):671\u2013679","journal-title":"Energy"},{"key":"2310_CR18","doi-asserted-by":"crossref","first-page":"118","DOI":"10.1016\/j.renene.2012.10.009","volume":"52","author":"J Zeng","year":"2012","unstructured":"Zeng J, Qiao W (2012) Short-term solar power prediction using a support vector machine. Renew Energy 52:118\u2013127","journal-title":"Renew Energy"},{"issue":"5","key":"2310_CR19","doi-asserted-by":"crossref","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. Sol Energy 84(5):807\u2013821","journal-title":"Sol Energy"},{"issue":"8","key":"2310_CR20","doi-asserted-by":"crossref","first-page":"2641","DOI":"10.1016\/j.ijhydene.2012.11.140","volume":"38","author":"AU Ch\u00e1vez-Ram\u00edrez","year":"2013","unstructured":"Ch\u00e1vez-Ram\u00edrez AU, Vallejo-Becerra V, Cruz JC, Ornelas R, Orozco G, Mu\u00f1oz-Guerrero R, Arriaga LG (2013) A hybrid power plant (solar\u2013wind\u2013hydrogen) model based in artificial intelligence for a remote-housing application in Mexico. Hydrogen Energy 38(8):2641\u20132655","journal-title":"Hydrogen Energy"},{"issue":"5","key":"2310_CR21","doi-asserted-by":"crossref","first-page":"441","DOI":"10.1016\/S0038-092X(02)00010-5","volume":"72","author":"L Hontoria","year":"2002","unstructured":"Hontoria L, Aguilera J, Zufiria P (2002) Generation of hourly irradiation synthetic series using neural network multilayer perceptron. Sol Energy 72(5):441\u2013446","journal-title":"Sol Energy"},{"issue":"2","key":"2310_CR22","doi-asserted-by":"crossref","first-page":"313","DOI":"10.1016\/j.solener.2004.08.018","volume":"78","author":"L Hontoria","year":"2005","unstructured":"Hontoria L, Aguilera J, Zufiria P (2005) A new approach for sizing stand-alone photovoltaic systems based in neural networks. Sol Energy 78(2):313\u2013319","journal-title":"Sol Energy"},{"issue":"9","key":"2310_CR23","doi-asserted-by":"crossref","first-page":"2131","DOI":"10.1016\/j.renene.2010.01.029","volume":"35","author":"Ali Rahimikhoob","year":"2010","unstructured":"Rahimikhoob Ali (2010) Estimating global solar radiation using artificial neural network and air temperature data in a semi-arid environment. Renew Energy 35(9):2131\u20132135","journal-title":"Renew Energy"},{"issue":"6","key":"2310_CR24","doi-asserted-by":"crossref","first-page":"1497","DOI":"10.1016\/j.enconman.2009.02.019","volume":"50","author":"A Azadeh","year":"2009","unstructured":"Azadeh A, Maghsoudi A, Sohrabkhani S (2009) An integrated artificial neural networks approach for predicting global radiation. Energy Convers Manag 50(6):1497\u20131505","journal-title":"Energy Convers Manag"},{"key":"2310_CR25","doi-asserted-by":"crossref","first-page":"311","DOI":"10.1016\/j.renene.2012.01.108","volume":"44","author":"LA Fernandez-Jimenez","year":"2012","unstructured":"Fernandez-Jimenez LA, Mu\u00f1oz-Jimenez A, Falces A, Mendoza-Villena M, Garcia-Garrido E, Lara-Santillan PM, Zorzano-Alba E, Zorzano-Santamaria PJ (2012) Short-term power forecasting system for photovoltaic plants. Renew Energy 44:311\u2013317","journal-title":"Renew Energy"},{"key":"2310_CR26","doi-asserted-by":"crossref","unstructured":"Yona A, Senjyu T, Saber AY, Funabashi T, Sekine H, Chul-Hwan K (2007) Application of neural network to one-day-ahead 24 hours generating power forecasting for photovoltaic system. In: International conference on intelligent systems applications to power systems, pp. 1\u20136","DOI":"10.1109\/ISAP.2007.4441657"},{"key":"2310_CR27","doi-asserted-by":"crossref","first-page":"71","DOI":"10.1016\/j.renene.2013.04.011","volume":"60","author":"A Mellit","year":"2013","unstructured":"Mellit A, Saglam S, Kalogirou SA (2013) Artificial neural network-based model for estimating the produced power of a photovoltaic module. Renew Energy 60:71\u201378","journal-title":"Renew Energy"},{"key":"2310_CR28","doi-asserted-by":"crossref","first-page":"389","DOI":"10.1016\/j.enconman.2014.05.090","volume":"85","author":"F Almonacid","year":"2014","unstructured":"Almonacid F, P\u00e9rez-Higueras PJ, Fern\u00e1ndez EF, Hontoria L (2014) A methodology based on dynamic artificial neural network for short-term forecasting of the power output of a PV generator. Energy Convers Manag 85:389\u2013398","journal-title":"Energy Convers Manag"},{"key":"2310_CR29","unstructured":"Artificial neural network, Wikipedia.org. \n                        http:\/\/en.wikipedia.org\/wiki\/Artificial_neural_network"},{"key":"2310_CR30","doi-asserted-by":"crossref","first-page":"610","DOI":"10.1016\/j.apenergy.2013.05.053","volume":"112","author":"M Piliougine","year":"2013","unstructured":"Piliougine M, Elizondo D, Mora-L\u00f3pez L, Sidrach-de-Cardona M (2013) Multilayer perceptron applied to the estimation of the influence of the solar spectral distribution on thin-film photovoltaic modules. Appl Energy 112:610\u2013617","journal-title":"Appl Energy"},{"issue":"5","key":"2310_CR31","doi-asserted-by":"crossref","first-page":"551","DOI":"10.1016\/0893-6080(90)90005-6","volume":"3","author":"Kurt Hornik","year":"1990","unstructured":"Hornik Kurt, Stinchcombe Maxwell, White Halbert (1990) Universal approximation of an unknown mapping and its derivatives using multilayer feedforward networks. Neural Netw 3(5):551\u2013560","journal-title":"Neural Netw"},{"key":"2310_CR32","isbn-type":"print","volume-title":"Neural and adaptive systems: fundamentals through simulations","author":"JC Principe","year":"2000","unstructured":"Principe JC, Euliano JR, Lefebvre WC (2000) Neural and adaptive systems: fundamentals through simulations. Wiley, New York. ISBN 0-471-35167-2","ISBN":"https:\/\/id.crossref.org\/isbn\/0471351672"},{"key":"2310_CR33","doi-asserted-by":"crossref","first-page":"294","DOI":"10.1016\/j.esd.2014.10.002","volume":"23C","author":"KA Kumar","year":"2014","unstructured":"Kumar KA, Sundareswaran K, Venkateswaran PR (2014) Performance study on a grid connected 20 kWp solar photovoltaic installation in an industry in Tiruchirappalli (India). Energy Sustain Dev 23C:294\u2013304","journal-title":"Energy Sustain Dev"},{"key":"2310_CR34","unstructured":"Surface meteorology and solar energy. A renewable energy resource web site (release 6.0) sponsored by NASA\u2019s Applied Science Program in the Science Mission Directorate, developed by POWER: Prediction of Worldwide Energy Resource Project. \n                        http:\/\/eosweb.larc.nasa.gov\/sse\/"},{"issue":"3","key":"2310_CR35","doi-asserted-by":"crossref","first-page":"917","DOI":"10.1109\/TSTE.2014.2313600","volume":"5","author":"H-T Yang","year":"2014","unstructured":"Yang H-T, Huang C-M, Huang Y-C, Pai Y-S (2014) A weather-based hybrid method for 1-day ahead hourly forecasting of PV power output. IEEE Trans Sustain Energy 5(3):917\u2013926","journal-title":"IEEE Trans Sustain Energy"},{"key":"2310_CR36","doi-asserted-by":"crossref","first-page":"660","DOI":"10.1016\/j.enbuild.2012.08.011","volume":"55","author":"SKH Chow","year":"2012","unstructured":"Chow SKH, Lee EWM, Li DHW (2012) Short-term prediction of photovoltaic energy generation by intelligent approach. Energy Build 55:660\u2013667","journal-title":"Energy Build"}],"container-title":["Neural Computing and Applications"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-016-2310-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/article\/10.1007\/s00521-016-2310-z\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-016-2310-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-016-2310-z","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2019,5,28]],"date-time":"2019-05-28T22:21:32Z","timestamp":1559082092000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/s00521-016-2310-z"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2016,4,11]]},"references-count":36,"journal-issue":{"issue":"12","published-print":{"date-parts":[[2017,12]]}},"alternative-id":["2310"],"URL":"https:\/\/doi.org\/10.1007\/s00521-016-2310-z","relation":{},"ISSN":["0941-0643","1433-3058"],"issn-type":[{"value":"0941-0643","type":"print"},{"value":"1433-3058","type":"electronic"}],"subject":[],"published":{"date-parts":[[2016,4,11]]}}}