A Prediction Model for Energy Production in a Solar Concentrator Using Artificial Neural Networks

Abstract

Solar energy is widely adopted today and produced by photovoltaic or concentrator solar power (CSP). Photovoltaic technology is the most prevalent, thanks to its well-established technology and low costs. CSP technology, on the other hand, has received less attention and interest, as it requires larger investments and a considerable surface. A relevant difficulty connected to the CSP is decoupling solar randomness and energy production. This paper proposes an artificial neural network (ANN) which foresees energy production using a solar parabolic dish installed at Politecnico di Torino (Energy Center Lab). The investigation was performed using a backpropagation ANN. Different learning algorithms were used: Levenberg-Marquardt, Bayesian regularization, resilient backpropagation, and scaled conjugate gradient. Seven atmospheric condition parameters were adopted (humidity, temperature, pressure, wind velocity and direction, solar radiation, and rain), to calculate the receiver temperature as an output. Bayesian regularization was found to be the optimal model for CSP energy production. The results of this investigation suggest that the ANNs are a strong, reliable, and useful tool for predicting temperature in a CSP receiver that can be of great value in the forecasting of energy production. The outcome of this investigation can simplify energy production forecasting using readily available meteorological data.