Estimation of the energy production of a parabolic trough solar thermal power plant using analytical and artificial neural networks models

Abstract

The accurate estimation of a concentrated solar power plant production is an important issue because of the fluctuations in meteorological parameters like solar radiation, ambient temperature, wind speed, and humidity. In this work, three models were conducted in order to estimate the hourly electric production of a parabolic trough solar thermal power plant (PTSTPP) located at Ain Beni-Mathar in Eastern Morocco. First, two analytical models are considered. The first analytical model (AM I) is based on calculating the heat losses of parabolic trough collectors (PTCs), while the second analytical model (AM II) is based on the thermal efficiency of PTCs. The third model is an artificial neural networks (ANN) model derived from artificial intelligence techniques. All models are validated using one year of real operating data. The simulation results indicate that the ANN model performs much better than the analytical models. Accordingly, the ANN model results show that the estimated annual electrical energy is about 42.6 GW h/year, while the operating energy is approximately 44.7 GWh/year. The frequency of occurrence shows that 86.77% of hourly values were estimated with a deviation of less than 3 MW h. The developed ANN model is readily useable to estimate energy production for PTSTPP.