{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,26]],"date-time":"2025-03-26T16:21:43Z","timestamp":1743006103106,"version":"3.40.3"},"publisher-location":"Singapore","reference-count":28,"publisher":"Springer Singapore","isbn-type":[{"type":"print","value":"9789811598289"},{"type":"electronic","value":"9789811598296"}],"license":[{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"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":[],"published-print":{"date-parts":[[2021]]},"DOI":"10.1007\/978-981-15-9829-6_1","type":"book-chapter","created":{"date-parts":[[2021,3,17]],"date-time":"2021-03-17T22:02:50Z","timestamp":1616018570000},"page":"1-13","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Modelling and Forecasting of Solar Radiation Data: A Case Study"],"prefix":"10.1007","author":[{"given":"Somila","family":"Hashunao","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hano","family":"Sunku","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"R. K.","family":"Mehta","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2021,3,18]]},"reference":[{"key":"1_CR1","doi-asserted-by":"publisher","first-page":"1102","DOI":"10.1016\/j.solener.2019.07.005","volume":"188","author":"Y Chaibi","year":"2019","unstructured":"Chaibi, Y., Allouhi, A., Malvoni, M., Salhi, M., Saadani, R.: Solar irradiance and temperature influence on the photovoltaic cell equivalent-circuit models. Sol. Energy 188, 1102\u20131110 (2019)","journal-title":"Sol. Energy"},{"key":"1_CR2","first-page":"1158575","volume":"186","author":"DHW Li","year":"2019","unstructured":"Li, D.H.W., Chen, W., Li, S., Lou, S.: Estimation of hourly global solar radiation using multivariate adaptive Regression spline (MARS)\u2014a case study of Hong Kong. Energy 186, 1158575 (2019)","journal-title":"Energy"},{"key":"1_CR3","doi-asserted-by":"publisher","first-page":"160","DOI":"10.1016\/j.compag.2019.03.022","volume":"160","author":"VZ Antonopoulos","year":"2019","unstructured":"Antonopoulos, V.Z., Papamichail, D.M., Aschonitis, V.G., Antonopoulos, A.V.: Solar radiation estimation methods using ANN and empirical models. Comput. Electron. Agric. 160, 160\u2013167 (2019)","journal-title":"Comput. Electron. Agric."},{"key":"1_CR4","doi-asserted-by":"publisher","first-page":"333","DOI":"10.1016\/j.apenergy.2014.05.055","volume":"130","author":"B Amrouche","year":"2014","unstructured":"Amrouche, B., Le Pivert, X.: Artificial neural network based daily local forecasting for global solar radiation. Appl. Energy 130, 333\u2013341 (2014)","journal-title":"Appl. Energy"},{"key":"1_CR5","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.jclepro.2015.04.041","volume":"104","author":"A Qazi","year":"2015","unstructured":"Qazi, A., Fayaz, H., et al.: The artificial neural network for solar radiation prediction and designing solar systems: a systematic literature review. J. Clean. Prod. 104, 1\u201312 (2015)","journal-title":"J. Clean. Prod."},{"issue":"3","key":"1_CR6","doi-asserted-by":"publisher","first-page":"273","DOI":"10.1016\/S0306-2619(03)00137-5","volume":"77","author":"A Sozena","year":"2000","unstructured":"Sozena, A., et al.: Use of artificial neural networks for mapping of solar potential in Turkey. Appl. Energy 77(3), 273\u2013286 (2000)","journal-title":"Appl. Energy"},{"key":"1_CR7","doi-asserted-by":"publisher","first-page":"320","DOI":"10.1016\/j.enconman.2016.04.101","volume":"120","author":"E Federico","year":"2016","unstructured":"Federico, E., et al.: Artificial neural network optimisation for monthly average daily global solar radiation. Energy Convers. Manage. 120, 320\u2013329 (2016)","journal-title":"Energy Convers. Manage."},{"key":"1_CR8","unstructured":"Siva Krishna Rao K.D.V., Premalatha, M., Naveen, C.: Analysis of different combinations of meteorological parameters in predicting the horizontal global solar radiation with ANN approach: a case study. Renew. Sustain. Energy Rev. 91, 248\u2013258 (2018)"},{"issue":"8","key":"1_CR9","doi-asserted-by":"publisher","first-page":"1468","DOI":"10.1016\/j.solener.2010.05.009","volume":"84","author":"MA Behrang","year":"2010","unstructured":"Behrang, M.A., et al.: The potential of different artificial neural network (ANN) techniques in daily global solar radiation modeling based on meteorological data. Sol. Energy 84(8), 1468\u20131480 (2010)","journal-title":"Sol. Energy"},{"key":"1_CR10","first-page":"01","volume":"161","author":"M Marzouq","year":"2017","unstructured":"Marzouq, M., et al.: ANN-based modelling and prediction of daily global solar irradiation using commonly measured meteorological parameters. IOP Conf. Ser. Earth Environ. Sci. 161, 01 (2017)","journal-title":"IOP Conf. Ser. Earth Environ. Sci."},{"issue":"3","key":"1_CR11","doi-asserted-by":"publisher","first-page":"206","DOI":"10.1016\/j.jart.2016.05.001","volume":"14","author":"N Premalatha","year":"2016","unstructured":"Premalatha, N., Amirtham, V.A.: Prediction of solar radiation for solar systems by using ANN models with different back propagation algorithms. J. Appl. Res. Technol. 14(3), 206\u2013214 (2016)","journal-title":"J. Appl. Res. Technol."},{"issue":"2","key":"1_CR12","doi-asserted-by":"publisher","first-page":"181","DOI":"10.1016\/j.solener.2007.06.003","volume":"82","author":"J Mubiru","year":"2008","unstructured":"Mubiru, J., Banda, E.J.K.B.: Estimation of monthly average daily global solar irradiation using artificial neural networks. Sol. Energy 82(2), 181\u2013187 (2008)","journal-title":"Sol. Energy"},{"issue":"7","key":"1_CR13","doi-asserted-by":"publisher","first-page":"1622","DOI":"10.1016\/j.renene.2007.09.012","volume":"33","author":"JL Boscha","year":"2008","unstructured":"Boscha, J.L., et al.: Daily solar irradiation estimation over a mountainous area using artificial neural networks. Renew. Energy 33(7), 1622\u20131628 (2008)","journal-title":"Renew. Energy"},{"key":"1_CR14","doi-asserted-by":"publisher","first-page":"569","DOI":"10.1016\/j.renene.2016.12.095","volume":"105","author":"C Voyant","year":"2017","unstructured":"Voyant, C., et al.: Machine learning methods for solar radiation forecasting: a review. Renew. Energy 105, 569\u2013582 (2017)","journal-title":"Renew. Energy"},{"key":"1_CR15","doi-asserted-by":"publisher","first-page":"727","DOI":"10.1016\/j.egypro.2017.12.753","volume":"143","author":"B Sivaneasan","year":"2017","unstructured":"Sivaneasan, B., Yu, C.Y., Goh, K.P.: Solar forecasting using ANN with fuzzy logic pre-processing. Energy Procedia 143, 727\u2013732 (2017)","journal-title":"Energy Procedia"},{"key":"1_CR16","unstructured":"Solanki, C.S.: Solar Photovoltaics: Fundamentals, Technologies and Applications, 2nd edn. PHI Learning Private Limited, New Delhi (2011)"},{"key":"1_CR17","doi-asserted-by":"crossref","unstructured":"Quansah, E., et al.: Empirical models for estimating global solar radiation over the ashanti region of Ghana. J. Sol. Energy 2014, 6 (2014). Article ID 897970","DOI":"10.1155\/2014\/897970"},{"issue":"6","key":"1_CR18","doi-asserted-by":"publisher","first-page":"3178","DOI":"10.1016\/j.rser.2011.04.007","volume":"15","author":"TV Ramachandra","year":"2011","unstructured":"Ramachandra, T.V., Jaina, R., Krishnadas, G.: Hotspots of solar potential in India. Renew. Sustain. Energy Rev. 15(6), 3178\u20133186 (2011)","journal-title":"Renew. Sustain. Energy Rev."},{"key":"1_CR19","doi-asserted-by":"crossref","unstructured":"Fadare, D.A., Irimisose, I., Oni, A.O., Falana, A.: Modeling of solar energy potential in Africa using an artificial neural network (2010)","DOI":"10.5251\/ajsir.2010.1.2.144.157"},{"issue":"9","key":"1_CR20","doi-asserted-by":"publisher","first-page":"1410","DOI":"10.1016\/j.apenergy.2008.12.005","volume":"86","author":"DA Fadare","year":"2009","unstructured":"Fadare, D.A.: Modelling of solar energy potential in Nigeria using an artificial neural network model. Appl. Energy 86(9), 1410\u20131422 (2009)","journal-title":"Appl. Energy"},{"issue":"2","key":"1_CR21","doi-asserted-by":"publisher","first-page":"571","DOI":"10.1016\/j.enpol.2007.09.033","volume":"36","author":"S Rehmana","year":"2008","unstructured":"Rehmana, S., Mohandes, M.: Artificial neural network estimation of global solar radiation using air temperature and relative humidity. Energy Policy 36(2), 571\u2013576 (2008)","journal-title":"Energy Policy"},{"issue":"5","key":"1_CR22","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, A.M.: 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 (2010)","journal-title":"Sol. Energy"},{"issue":"2","key":"1_CR23","doi-asserted-by":"publisher","first-page":"201","DOI":"10.1016\/j.apenergy.2003.11.004","volume":"79","author":"J Soares","year":"2004","unstructured":"Soares, J., et al.: Modeling hourly diffuse solar-radiation in the city of Paulo using a neural-network technique. Appl. Energy 79(2), 201\u2013214 (2004)","journal-title":"Appl. Energy"},{"key":"1_CR24","doi-asserted-by":"crossref","unstructured":"Mellit, A., et al.: An ANFIS-based forecasting for solar radiation data from sunshine duration and Ambient temperature. IEEE Power Eng. Soc. Gen. Meet. 24\u201328 (2007)","DOI":"10.1109\/PES.2007.386131"},{"key":"1_CR25","first-page":"113","volume":"9","author":"R Iqdour","year":"2006","unstructured":"Iqdour, R., Zeroual, A.: A rule based fuzzy model for the prediction of solar radiation. IEEE Revue Des Energies Renouvelables 9, 113\u2013120 (2006)","journal-title":"IEEE Revue Des Energies Renouvelables"},{"key":"1_CR26","doi-asserted-by":"crossref","unstructured":"Almaraashi, M.: Short-term prediction of solar energy in Saudi Arabia using automated-design fuzzy logic systems. PLoS One 12(8), e0182429, (2017)","DOI":"10.1371\/journal.pone.0182429"},{"key":"1_CR27","doi-asserted-by":"publisher","first-page":"304","DOI":"10.1016\/j.procs.2017.09.045","volume":"114","author":"A Alzahrani","year":"2017","unstructured":"Alzahrani, A., et al.: Solar Irradiance forecasting using deep neural networks. Procedia Comput. Sci. 114, 304\u2013313 (2017)","journal-title":"Procedia Comput. Sci."},{"key":"1_CR28","unstructured":"Sivanandam, S.N., Sumathi, S., Deepa, S.N.: Introduction to neural networks using Matlab 6.0. McGraw Hill Education (India) Private Limited (2006)"}],"container-title":["Smart Innovation, Systems and Technologies","Modeling, Simulation and Optimization"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-15-9829-6_1","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,3,17]],"date-time":"2021-03-17T22:03:23Z","timestamp":1616018603000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/978-981-15-9829-6_1"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"ISBN":["9789811598289","9789811598296"],"references-count":28,"URL":"https:\/\/doi.org\/10.1007\/978-981-15-9829-6_1","relation":{},"ISSN":["2190-3018","2190-3026"],"issn-type":[{"type":"print","value":"2190-3018"},{"type":"electronic","value":"2190-3026"}],"subject":[],"published":{"date-parts":[[2021]]},"assertion":[{"value":"18 March 2021","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}}]}}