Abstract
Collector networks have various operational variables, including irradiance, ambient temperature, wind speed, flow rates, pressure drop, flow distribution, and pumping performance. Since a solar collector network is hydraulically interconnected, a non-uniform thermal behaviour can be observed. A neural network is a computational model inspired by the structure and function of the human brain, designed to process information and learn patterns from data. It comprises artificial neurons arranged in interconnected layers that transform and transmit signals using mathematical functions. A hybrid layer in neural networks is a layer that combines elements of physical or mathematical models with components of neural networks. This work presents a hybrid neural network model to improve and predict the thermal performance of a flat plate solar collector network. This work consists of two analytical sections. In the first stage, a hybrid neural layer was designed to predict the performance of a solar collector. Each solar collector has an area of 2.31 m², an inlet temperature of 25 ºC, and an inlet flow rate of 4 L/min. The input radiation data originated from a dataset concerning one day of operation at a Solar Plant in Mexico. Based on the behaviour of the hybrid neural layer, the second stage involved utilising these neurons to analyse the thermal performance of a line consisting of 10 collectors. The maximum predicted temperature was 79 ºC versus 72 ºC (solar plant value). The Artificial Neural Network model calculated an R² score of 0.9884 for the temperature profile. It is feasible to develop specialised neural modules for solar collector networks.
