J.R. Trapero, N. Kourentzes, A. Martin, 2015, 27th European Conference on operational Research, Glasgow.
Solar power generation is a crucial research area for countries that have high dependency on fossil energy sources and count on high solar resource potential. In order to integrate the electricity generated by solar power plants into the grid, solar irradiation must be reasonably well forecasted, where deviations of the forecasted value from the actual measured value involve significant costs. The present paper proposes a univariate Dynamic Harmonic Regression model set up in a State Space framework for short-term (1 to 24 hours) solar irradiation forecasting. The DHR is a type of Unobserved Components model that can be considered as an extension of the typical harmonic regression, where the coefficients are time-varying. This method provides a fast automatic identification and estimation procedure based on the frequency domain. Furthermore, the recursive algorithm as the Kalman Filter is employed to yield adaptive predictions. Time series hourly aggregated as the Global Horizontal Irradiation and the Direct Normal Irradiation will be used to illustrate the proposed approach. The good forecasting performance is illustrated with solar irradiance measurements collected from ground-based weather stations located in Spain. The results show that the Dynamic Harmonic Regression achieves a relative Root Mean Squared Error about 30% and 47% for the Global and Direct irradiation components, respectively, for a forecast horizon of 24 hours ahead.