{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,28]],"date-time":"2026-08-28T13:35:43Z","timestamp":1787924143216,"version":"build-2784847793"},"reference-count":16,"publisher":"Wiley","issue":"1","license":[{"start":{"date-parts":[[2011,11,20]],"date-time":"2011-11-20T00:00:00Z","timestamp":1321747200000},"content-version":"vor","delay-in-days":323,"URL":"http:\/\/creativecommons.org\/licenses\/by\/3.0\/"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Advances in Artificial Neural Systems"],"published-print":{"date-parts":[[2011,1]]},"abstract":"<jats:p>We used five years of global solar radiation data to estimate the monthly average of daily global solar irradiation on a horizontal surface based on a single parameter, sunshine hours, using the artificial neural network method. The station under the study is located in Kampala, Uganda at a latitude of 0<jats:italic>.<\/jats:italic>19\u00b0N, a longitude of 32<jats:italic>.<\/jats:italic>34\u00b0E, and an altitude of 1200\u2009m above sea level. The five\u2010year data was split into two parts in 2003\u20132006 and 2007\u20102008; the first part was used for training, and the latter was used for testing the neural network. Amongst the models tested, the feed\u2010forward back\u2010propagation network with one hidden layer (65 neurons) and with the tangent sigmoid as the transfer function emerged as the more appropriate model. Results obtained using the proposed model showed good agreement between the estimated and actual values of global solar irradiation. A correlation coefficient of 0.963 was obtained with a mean bias error of 0.055\u2009MJ\/m<jats:sup>2<\/jats:sup> and a root mean square error of 0.521\u2009MJ\/m<jats:sup>2<\/jats:sup>. The single\u2010parameter ANN model shows promise for estimating global solar irradiation at places where monitoring stations are not established and stations where we have one common parameter (sunshine hours).<\/jats:p>","DOI":"10.1155\/2011\/751908","type":"journal-article","created":{"date-parts":[[2011,11,20]],"date-time":"2011-11-20T21:01:36Z","timestamp":1321822896000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":24,"title":["Predicting Global Solar Radiation Using an Artificial Neural Network Single\u2010Parameter Model"],"prefix":"10.1155","volume":"2011","author":[{"given":"Karoro","family":"Angela","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ssenyonga","family":"Taddeo","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mubiru","family":"James","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2011,11,20]]},"reference":[{"key":"e_1_2_9_1_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.renene.2003.11.016"},{"key":"e_1_2_9_2_2","doi-asserted-by":"publisher","DOI":"10.1016\/0960-1481(94)00093-L"},{"key":"e_1_2_9_3_2","unstructured":"DagestadK. Estimating global radiation at ground level from satellite images Ph.D. thesis 2005 Department of Meteorology University of Bergen Bergen Norway."},{"key":"e_1_2_9_4_2","doi-asserted-by":"publisher","DOI":"10.1002\/qj.49705021008"},{"key":"e_1_2_9_5_2","doi-asserted-by":"publisher","DOI":"10.1016\/S0960\u20101481(98)00065\u20102"},{"key":"e_1_2_9_6_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.solener.2007.06.003"},{"key":"e_1_2_9_7_2","doi-asserted-by":"publisher","DOI":"10.5194\/npg-11-683-2004"},{"key":"e_1_2_9_8_2","unstructured":"Artificial neural networks http:\/\/www.sciencedirect.com\/science\/journal\/03062."},{"key":"e_1_2_9_9_2","first-page":"229","article-title":"Contribution to the relationship between solar radiation and sunshine duration in the tropics: a case study of experimental data at Ilorin, Nigeria","volume":"26","author":"Udo S. 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