Artificial neural network models for predicting the solar radiation as input of a concentrating photovoltaic system. (December 2015)

• Record Type: Journal Article
• Authors: Renno, C.; Petito, F.; Gatto, A.
• Is Part Of: Energy conversion and management. Volume 106(2016)
• Page Start: 999
• Page End: 1012
• Publication Date: 2015-12
• Subjects: Artificial neural network -- Direct normal irradiance -- Global solar radiation -- Concentrating photovoltaic -- Thermal system; Direct energy conversion -- Periodicals; Energy storage -- Periodicals; Energy transfer -- Periodicals; Énergie -- Conversion directe -- Périodiques; Direct energy conversion; Periodicals; 621.3105
• DOI: 10.1016/j.enconman.2015.10.033
• Languages: English
• ISSNs: 0196-8904

Abstract

Highlights: ANN models determination to predict the daily GR and the hourly DNI. Evaluation of the solar energy potential of a specific location. Performances evaluation of a point-focus concentrating photovoltaic and thermal system (CPV/T). Abstract: The energy production analysis of a system based on renewable technology depends on the inputs estimation accuracy. The solar energy is a free resource characterized by high variability; hence, its correct evaluation is a strategic factor for the feasibility of a solar system. In this paper a new methodological approach is presented in order to evaluate more accurately the electric and thermal energy production of a point-focus concentrating photovoltaic and thermal system (CPV/T). Two Artificial Neural Network (ANN) models for predicting solar global radiation and direct normal solar irradiance (DNI) are developed adopting different parameters such as climatic, astronomic and radiometric variables. In particular, a new combination of parameters is proposed in this paper and adopted first of all for the global radiation evaluation whose ANN model can be easily compared with the literature; the data are trained and tested by a multi layer perceptron (MLP). Hence, the results validation for the global solar radiation evaluation has encouraged to design an ANN model for the DNI by means of a similar variables set. The MLP network is trained, tested and validated for the hourly DNI estimation obtaining the MAPE, RMSE and R 2 statistical indexes values respectively equal to 5.72%, 3.15% and 0.992. Finally, the electric and thermal outputs of a point-focus CPV/T system are evaluated varying the concentration factor and cells number, and adopting as input the DNI evaluation results obtained by the ANN model presented in this paper. The CPV/T system outputs are estimated referring to the city of Salerno (Italy) under different meteorological conditions.