Bi-fluid photovoltaic/thermal (PV/T) solar collector: Experimental validation of a 2-D theoretical model

Jarimi, Hasila; Bakar, Mohd Nazari Abu; Othman, Mahmod; Din, Mahadzir Hj

doi:10.1016/j.renene.2015.07.014

Cited by 117 publications

(38 citation statements)

References 35 publications

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“…The change in thermal efficiency is clearer than that of PVT electrical efficiency, nonetheless, nanofluid is found to lead to better energetic performance than water for PVT collector. Jarimi et al performed a two‐dimensional steady‐state analysis of a bi‐fluid PVT using MATLAB software, and validated the model by performing indoor experiments. The authors also implemented various methods to investigate the error between predictions and experiments.…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2020

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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 R 2 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. K E Y W O R D SBIPVT, exergetic performance, machine learning, performance evaluation criteria, predictive model

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Section: Introductionmentioning

confidence: 99%

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

et al. 2020

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“…Ruobing Liang et al 3 conducted a study on ordinary PV modules and solar PV/T collectors with graphite fill layer, based on the second law of thermodynamics. Hasila Jarimi et al 8 proposed a 2-D theoretical model of bifluid PV/T solar collector. Carlo Renno 5 proposed a domestic concentrating PV/T system, which used a parabolic concentrator, triple junction solar cells and an active cooling module.…”

Section: Introductionmentioning

confidence: 99%

“…F. Hussain et al 7 performed a study on a novel air-cooling PV/T system with a honeycomb structure heat exchanger. Hasila Jarimi et al 8 proposed a 2-D theoretical model of bifluid PV/T solar collector. Furthermore, the model was validated in water cooling, air cooling, and mixed cooling mode.…”

Section: Introductionmentioning

confidence: 99%

Design and performance study of a low concentration photovoltaic-thermal module

et al. 2018

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Summary Compared with traditional photovoltaic‐thermal system, low concentration photovoltaic‐thermal systems install the low‐power concentrator (compound parabolic concentrator, Fresnel concentrator, miniature trough concentrator, etc) to improve system performance. In this paper, a baffle heat exchange channel was designed, which can decrease the temperature gradient of low concentration photovoltaic‐thermal module in the flow direction, based on the traditional flat‐box photovoltaic‐thermal collector. The system performance was monitored with different baffle spacing, flow rate, inlet temperature, ambient temperature, and radiation intensity by adopting simulation methodology. Furthermore, an experiment was performed to validate and evaluate the simulation results. The results indicated that, on a typical day, the maximum of thermal efficiency and electrical efficiency of the module were 55.11% and 12.50%, respectively.

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“…In addition, thermal and electrical efficiencies of about 50% and 4-5% have been achieved by the hybrid system. Jarimi et al [30] analyzed a bi-fluid PVT solar collector from a theoretical and experimental point of view be means of a 2D model and steady state analysis. In particular, the operation of the collector was investigated in case of only air or water fluid operation and also with both fluids.…”

Section: Introductionmentioning

confidence: 99%

Experimental and Numerical Analyses of a Flat Plate Photovoltaic/Thermal Solar Collector

2017

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This paper presents a one-dimensional finite-volume model of an unglazed photovoltaic/thermal (PVT) solar collector. The unit consists of a conventional solar photovoltaic (PV) collector coupled with a suitable heat exchanger. In particular, the collector includes a roll bond heat exchanger and it is not equipped with back and frame insulation. The system is discretized along the flow direction (longitudinal) of the cogenerative collector. For each finite-volume element of the discretized computational domain, mass and energy balances are implemented. The collector geometry and materials parameters are taken from a commercially available device. An on-field experimental investigation is performed in order to validate the proposed model. The model is used to evaluate both electrical and thermodynamic parameters for each element of the domain and for fixed operating conditions. Finally, a sensitivity analysis is also performed in order to investigate the energetic performance of the cogenerative collector as a function of the main design/environmental parameters.

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Bi-fluid photovoltaic/thermal (PV/T) solar collector: Experimental validation of a 2-D theoretical model

Cited by 117 publications

References 35 publications

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

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

Design and performance study of a low concentration photovoltaic-thermal module

Experimental and Numerical Analyses of a Flat Plate Photovoltaic/Thermal Solar Collector

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