Machine learning predictive models for optimal design of building‐integrated photovoltaic‐thermal collectors

Summary 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...

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Bibliographic Details
Published in:International journal of energy research Vol. 44; no. 7; pp. 5675 - 5695
Main Authors: Shahsavar, Amin, Moayedi, Hossein, Al‐Waeli, Ali H. A., Sopian, Kamaruzzaman, Chelvanathan, Puvaneswaran
Format: Journal Article
Language:English
Published: Chichester, UK John Wiley & Sons, Inc 10.06.2020
Subjects:
Air flow
Air masses
Algorithms
BIPVT
Datasets
Decision trees
Design parameters
exergetic performance
Exergy
Feasibility studies
Flow rates
Learning algorithms
Machine learning
Mass flow rate
Mathematical models
Multilayer perceptrons
Optimization
Parameter identification
Parameters
Performance evaluation
performance evaluation criteria
Performance indices
Photovoltaic cells
Photovoltaics
Prediction models
predictive model
Radial basis function
Statistical analysis
Support vector machines
Thermodynamics
Training
ISSN:0363-907X, 1099-114X
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Summary:Summary 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. Reliable predictive models of the exergetic performance of a BIPVT collector in Iran were developed in this study. The Performance Evaluation Criteria (PEC) was used to assess the performance of the BIPVT collector. The most reliable predictions of PEC were obtained from the Random Forest (RF) method.
Bibliography:Funding information
Universiti Kebangsaan Malaysia, Grant/Award Number: MI‐2019‐011
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ISSN:0363-907X
1099-114X
DOI:10.1002/er.5323