Evaluation of electrical efficiency of photovoltaic thermal solar collector

EasyChair Preprint 2651

Abstract

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.

Keyphrases: Adaptive Neuro-Fuzzy Inference System (ANFIS), Hybrid Machine learning model, Least Square Support Vector Machine (LSSVM), Photovoltaic-Thermal (PV/T), neural networks (NNs), renewable energy

@booklet{EasyChair:2651,
  author    = {Mohammad Hossein Ahmadi and Alireza Baghban and Milad Sadeghzadeh and Mohammad Zamen and Amir Mosavi and Shahab Shamshirband and Ravinder Kumar and Mohammad Mohammadi-Khanaposhtani},
  title     = {Evaluation of electrical efficiency of photovoltaic thermal solar collector},
  howpublished = {EasyChair Preprint 2651},
  year      = {EasyChair, 2020}}