Solar irradiation mapping with exogenous data from support vector regression machines estimations

Exactly how to estimate solar resources in areas without pyranometers is of great concern for solar energy planners and developers. This study addresses the mapping of daily global irradiation by combining geostatistical interpolation techniques with support vector regression machines. The support vector regression machines training process incorporated commonly measured meteorological variables (temperatures, rainfall, humidity and wind speed) to estimate solar irradiation and was performed with data of 35 pyranometers over continental Spain. Genetic algorithms were used to simultaneously perform feature selection and model parameter optimization in the calibration process. The model was then used to estimate solar irradiation in a massive set of exogenous stations, 365 sites without irradiation sensors, so as to overcome the lack of pyranometers. Then, different spatial techniques for interpolation, fed with both measured and estimated irradiation values, were evaluated and compared, which led to the conclusion that ordinary kriging demonstrated the best performance. Training and interpolation mean absolute errors were as low as 1.81 MJ/m(2) day and 1.74 MJ/m(2) day, respectively. Errors improved significantly as compared to interpolation without exogenous stations and others referred in the bibliography for the same region. This study presents an innovative methodology for estimating solar irradiation, which is especially promising since it may be implemented broadly across other regions and countries under similar circumstances. (C) 2015 Elsevier Ltd. All rights reserved.