Daily solar irradiation estimation over a mountainous area using artificial neural networks

In order to design both active and passive solar energy systems, radiation data are needed for the studied location. The implementation of such renewable energy systems is especially important in places like natural parks, where acoustic and fossil fuel derived contamination has to be completely avoided. Measure of solar radiation is usually accomplished by means of radiometric station nets with a low spatial resolution. To estimate the radiation in sites located away from the stations, different interpolation/extrapolation techniques may be used. These methods are valid on places where the spatial variability of radiation is not significant, but becomes less accurate if complex terrain areas are present in between the radiometric stations. As an alternative, artificial intelligence techniques have been used in this work, along with a 20m resolution digital model of terrain. The inputs to the network have been selected using the automatic relevance determination methodology. The data set contains 3 years’ data of daily global radiation measured at 12 different stations located in the north face of the Sierra Nevada National Park in the surroundings of Huéneja (Granada), a town located in the South East of Spain. The stations altitude varies from 1000 to 1700m. The goal of this work has been to estimate daily global irradiation on stations located in a complex terrain, and the values estimated by the neural network model have been compared with the measured ones leading to a root mean square error (RMSE) of 6.0% and a mean bias error (MBE) of 0.2%, both expressed as a percentage of the mean value. Performance achieved individually for each of the stations lies in the range [5.0–7.5]% for the RMSE and [−1.2 to +2.1]% for the MBE. Results point out artificial neural networks as an efficient and easy methodology for calculating solar radiation levels over complex mountain terrains from only one radiometric station data. In addition, this methodology can be applied to other areas with a complex topography.