April 18, 2024

Hossein Moayedi

Academic rank:
Address:
Education: Ph.D
Phone:
Faculty: Faculty of Engineering

Research

Title
Machine learning predictive models for optimal design of building‐integrated photovoltaic‐thermal collectors
Type Article
Keywords
BIPVT, exergetic performance, machine learning, performance evaluation criteria, predictive model
Researchers Amin Shahsavar Goldanloo، Hossein Moayedi، Ali H. A. Al-Waeli، Kamaruzzaman Sopian، Puvaneswaran Chelvanathan

Abstract

This research article aims to examine the feasibility of several machine learning techniques to forecast the exergetic performance of a building‐integrated photovoltaic‐thermal (BIPVT) collector. In this regard, it uses multiple linear regression, multilayer perceptron, radial basis function regressor, sequential minimal optimization improved support vector machine, lazy.IBK, random forest (RF), and random tree approaches. Moreover, it implements the performance evaluation criteria (PEC) to evaluate the system's performance from the perspective of exergy. The use of these approaches serves the identification process to realize the relationship between the input–output parameters of the BIPVT system. The novelty of this work is that it utilizes and compares multiple learning algorithms to predict the PEC of BIPVT through design parameters. Hence, the research considers the parameter (PEC) as the essential output of the BIPVT collector, while the input parameters are the length, width, and depth of the duct, located under the PV modules, as well as the air mass flow rate. The results of the research for the statistical indexes of mean absolute error, root mean square error, relative absolute error (%), and root relative squared error (%) show values of (0.2967, 0.3885, 1.8754, and 1.5237) and (0.4957, 0.8153, 2.9586, and 2.8289), respectively, for the training and testing datasets. While R2 ranges (0.9997‐0.9999) for those datasets. Therefore, to estimate the exergy performance of the BIPVT collector, the RF model is superior to other proposed models.