  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.
