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To make good use of solar energy, an effective way to forecast solar radiation is essential to guarantee the reliability of grid\u2010connected photovoltaic installations. Although an artificial neural network (ANN) is of great importance, irrelevant variables are utilized which results in complex model and intractable computation cost. To remove these irrelevant variables, the combination of variable selection methods and ANN are applied. However, how to select the regularization parameters in these techniques is challenging. This paper successfully investigates a square root elastic net\u2010 (SREN\u2010) based approach to tackle this challenge and selects all the important variables. An Elman neural network (ENN) is constructed with the important variables selected by SREN as inputs. Based on meteorological data, SRENENN has been developed for 1\u2010year period in Xinjiang area of China. The present model delivers superior relationship between the estimated and measure values.<\/jats:p>","DOI":"10.1155\/2018\/8135193","type":"journal-article","created":{"date-parts":[[2018,8,26]],"date-time":"2018-08-26T23:32:32Z","timestamp":1535326352000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["A Novel Model Based on Square Root Elastic Net and Artificial Neural Network for Forecasting Global Solar 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