title: Hourly photosynthetically active radiation estimation in Midwestern United States from artificial neural networks and conventional regressions models.
abstract: The relationship between hourly photosynthetically active radiation (PAR) and the global solar radiation (R s ) was analyzed from data gathered over 3 years at Bondville, IL, and Sioux Falls, SD, Midwestern USA. These data were used to determine temporal variability of the PAR fraction and its dependence on different sky conditions, which were defined by the clearness index. Meanwhile, models based on artificial neural networks (ANNs) were established for predicting hourly PAR. The performance of the proposed models was compared with four existing conventional regression models in terms of the normalized root mean square error (NRMSE), the coefficient of determination (r (2)), the mean percentage error (MPE), and the relative standard error (RSE). From the overall analysis, it shows that the ANN model can predict PAR accurately, especially for overcast sky and clear sky conditions. Meanwhile, the parameters related to water vapor do not improve the prediction result significantly.
authors.affiliation: Department of Geography and Planning, University of Saskatchewan, Kirk Hall 117 Science Place, Saskatoon, SK, S7N 5C8, Canada. yux051@mail.usask.ca.
authors.forename: Xiaolei
authors.initials: X
authors.lastname: Yu
authors.affiliation: Department of Geography and Planning, University of Saskatchewan, Kirk Hall 117 Science Place, Saskatoon, SK, S7N 5C8, Canada.
authors.forename: Xulin
authors.initials: X
authors.lastname: Guo
mesh_terms: D007087:Illinois; D008962:Models, Theoretical; D016571:Neural Networks, Computer; D010788:Photosynthesis; D012044:Regression Analysis; D012621:Seasons; D013023:South Dakota; D013472:Sunlight
keywords: Artificial neural networks; Clearness index; Photosynthetically active radiation; Solar irradiance