Development of Neural Network Prediction Models for the Energy Producibility of a Parabolic Dish: A Comparison with the Analytical Approach

Valerio Lo Brano (), Stefania Guarino, Alessandro Buscemi and Marina Bonomolo
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Valerio Lo Brano: Department of Engineering, University of Palermo, 90133 Palermo, Italy
Stefania Guarino: Department of Engineering, University of Palermo, 90133 Palermo, Italy
Alessandro Buscemi: Department of Engineering, University of Palermo, 90133 Palermo, Italy
Marina Bonomolo: Department of Engineering, University of Palermo, 90133 Palermo, Italy

Energies, 2022, vol. 15, issue 24, 1-27

Abstract: Solar energy is one of the most widely exploited renewable/sustainable resources for electricity generation, with photovoltaic and concentrating solar power technologies at the forefront of research. This study focuses on the development of a neural network prediction model aimed at assessing the energy producibility of dish–Stirling systems, testing the methodology and offering a useful tool to support the design and sizing phases of the system at different installation sites. Employing the open-source platform TensorFlow, two different classes of feedforward neural networks were developed and validated (multilayer perceptron and radial basis function). The absolute novelty of this approach is the use of real data for the training phase and not predictions coming from another analytical/numerical model. Several neural networks were investigated by varying the level of depth, the number of neurons, and the computing resources involved for two different sets of input variables. The best of all the tested neural networks resulted in a coefficient of determination of 0.98 by comparing the predicted electrical output power values with those measured experimentally. The results confirmed the high reliability of the neural models, and the use of only open-source IT tools guarantees maximum transparency and replicability of the models.

Keywords: solar energy; concentrating solar power; dish–Stirling; neural network; energy performance forecasting (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
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