Evaluation of electrical efficiency of photovoltaic thermal solar collector
This work addresses the need for efficient prediction models in solar energy systems, particularly for costly or time-consuming experimental measurements, but it is incremental in applying existing methods to new data.
The study tackled the problem of predicting the thermal performance of a photovoltaic-thermal solar collector by applying machine learning methods, with the LSSVM model outperforming ANFIS and ANNs in accuracy.
Solar energy is a renewable resource of energy that is broadly utilized and has the least emissions among renewable energies. In this study, machine learning methods of artificial neural networks (ANNs), least squares support vector machines (LSSVM), and neuro-fuzzy are used for advancing prediction models for the thermal performance of a photovoltaic-thermal solar collector (PV/T). In the proposed models, the inlet temperature, flow rate, heat, solar radiation, and the sun heat have been considered as the inputs variables. Data set has been extracted through experimental measurements from a novel solar collector system. Different analyses are performed to examine the credibility of the introduced approaches and evaluate their performance. The proposed LSSVM model outperformed ANFIS and ANNs models. LSSVM model is reported suitable when the laboratory measurements are costly and time-consuming, or achieving such values requires sophisticated interpretations.