Performance analysis of the photovoltaic‐thermoelectric system combined with serpentine type water collector

Gupta, Anmol; Agrawal, Sanjay; Pal, Yash

doi:10.1002/er.7015

Cited by 5 publications

(5 citation statements)

References 42 publications

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“…Figure 2 presents the finite element optimization results of the hybrid system utilizing cell 1 as the effects of the principal operating parameters on the system performance are investigated. Overall, isolated variations of these parameters are observed to significantly affect the hybrid system performance, as reported in similar previous studies [31,55]. Figures 2(a) and 2(b) depict the effect of concentrated solar irradiance on the system performance.…”

Section: Resultssupporting

confidence: 79%

“…It was found that at ambient operating conditions of 37.8 °C and 874 W/m 2 , the proposed system could produce hot water at a capacity of 80 L per day at a heat transfer rate of 535.5 W/day. Gupta et al, [31] conducted the performance analysis of a solar photovoltaic-thermoelectric integrated with a serpentine water collector for removing the waste heat from the back surface of the hybrid system. The performance of the proposed system was compared with 2…”

Section: Introductionmentioning

confidence: 99%

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Machine Learning Performance Prediction of a Solar Photovoltaic-Thermoelectric System with Various Crystalline Silicon Cell Types

Alghamdi

Maduabuchi

Albaker

et al. 2023

International Journal of Energy Research

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Since the solar photovoltaic-thermoelectric (PV-TE) is an upcoming technology, the current literature on PV-TEs have failed to thoroughly investigate the effects of different solar cell types on the PV-TE’s performance. Such parametric study becomes necessary since the properties of the solar cell, characterized by the cell temperature coefficient and reference efficiency, determine the overall performance of the PV-TE. Further, the design information obtained from numerical solvers is minimal due to the high time and computational energy required to optimize the photovoltaic-thermoelectric (PV-TE) performance. This problem severely affects the ease with which useful design information can be drawn from current methods to design high-performance PVs. Additionally, to reduce the complexity of the existing numerical model, the previous models have introduced some principal assumptions that have significantly affected the accuracy of the numerical results. Finally, the numerical model has neglected several real-life operating conditions since they increase the model complexity. For the first time, deep neural networks are proposed to predict the photovoltaic-thermoelectric performance designed with 3 different crystalline solar cells as a perfect replacement for the inefficient numerical methods used to analyze the hybrid system. After that, the data generated by the numerical solver is fed to an optimum 3-layer deep neural network with 20 neurons per layer to forecast the hybrid system’s performance efficiently. The training of the neural network is governed by Bayesian regularization and the performance of the model is evaluated using the mean squared error, coefficient of determination, and training time. Results show that the deep neural network accurately learned the results that took the conventional numerical solver 1,600 mins to generate in just 12 min and 27 s, making the proposed network 128.51 times faster than the traditional numerical method. Furthermore, the accuracy of the network is demonstrated by the low mean squared error of 3.34 × 10 − 8 and high training and testing regression of 100% and 99.98%, respectively.

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Section: Resultssupporting

confidence: 79%

Section: Introductionmentioning

confidence: 99%

Machine Learning Performance Prediction of a Solar Photovoltaic-Thermoelectric System with Various Crystalline Silicon Cell Types

Alghamdi

Maduabuchi

Albaker

et al. 2023

International Journal of Energy Research

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“…These findings show that serpentine collectors produce high thermal performance but minor electrical efficiency enhancement, in line with the results tabulated in Table 3. Another study on serpentine ST-PVT was carried out by Gupta et al [102]. They analyzed numerically a glazed PVT system combined with thermoelectric using weather data from Pune, India.…”

Section: Serpentinementioning

confidence: 99%

“…They analyzed numerically a glazed PVT system combined with thermoelectric using weather data from Pune, India. They found that the serpentine collectors could increase overall Another study on serpentine ST-PVT was carried out by Gupta et al [102]. They analyzed numerically a glazed PVT system combined with thermoelectric using weather data from Pune, India.…”

Section: Serpentinementioning

confidence: 99%

Global Advancement of Nanofluid-Based Sheet and Tube Collectors for a Photovoltaic Thermal System

2022

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The photovoltaic thermal (PVT) system was initially developed by attaching a simple sheet and tube thermal collector to the PV panel to improve cell performance while producing heat energy. The collector designs and heat transfer fluid are the main focus of PVT research, with the sheet and tube collector being the direct reference, and nanofluid being the promised working fluid. This study intends to review the development of the sheet and tube PVT (ST-PVT) system reported by researchers in the literature by searching and selecting quality literature from reputable academic databases guided by set criteria to maintain the consistency and validity of the literature selection. The findings indicate that the ST-PVT system with no glazing and a serpentine collector offers the most desirable thermal and electrical performance. It is also learned that CuO/water nanofluid enhances ST-PVT overall efficiency at a higher rate. However, it is observed that nanofluid required more pumping power, up to 67% for 0.4 wt% SiO2/water concentration compared to water. Also, many ST-PVT studies are only in the numerical modeling stage, while the negative impact of nanofluids is still rarely discussed in the literature. Thus, more research is required to prove the advantages of the ST-PVT system, especially in collector design and nanofluid application.

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“…However, researchers have proposed several means of cooling the solar panel when exposed to harsh environmental conditions like concentrated solar using active, passive, phase change material, and thermoelectric cooling techniques [11,12]. Among these proposed techniques, the thermoelectric cooling method stands out due to the relative effectiveness, commercial availability, and affordability of the thermoelectric module (TEM) in cooling the concentrated solar photovoltaic (CPV) system [13][14][15]. Some researchers have experimentally demonstrated how the CPV-TE hybrid system was capable of generating a higher power and efficiency than the stand-alone CPV system by utilizing the entire broad solar spectrum [16][17][18][19].…”

Section: Introductionmentioning

confidence: 99%

Multiobjective Optimization and Machine Learning Algorithms for Forecasting the 3E Performance of a Concentrated Photovoltaic-Thermoelectric System

Alghamdi

Maduabuchi

Yusuf³

et al. 2023

International Journal of Energy Research

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Previous theoretical research efforts which were validated by experimental findings demonstrated the thermo-economic benefits of the hybrid concentrated photovoltaic-thermoelectric (CPV-TE) system over the stand-alone CPV. However, the operating conditions and TE material properties for maximum CPV-TE performance may differ from those required in a standalone thermoelectric module (TEM). For instance, a high-performing TEM requires TE materials with high Seebeck coefficients and electrical conductivities, and at the same time, low thermal conductivities ( k ). Although it is difficult to attain these ideal conditions without complex material engineering, the low k implies a high thermal resistance and temperature difference across the TEM which raises the PV backplate’s temperature in a hybrid CPV-TE operation. The increased PV temperature may reduce the overall system’s thermodynamic performance. To understand this phenomenon, a study is needed to guide researchers in choosing the best TE material for an optimal operation of a CPV-TE system. However, no prior research effort has been made to this effect. One method of finding the optimum TE material property is to parametrically vary one or more transport parameters until an optimum point is determined. However, this method is time-consuming and inefficient since a global optimum may not be found, especially when large incremental step sizes are used. This study provides a better way to solve this problem by using a multiobjective optimization genetic algorithm (MOGA) which is fast and reliable and ensures that the global optimum is obtained. After the optimization has been conducted, the best performing conditions for maximum CPV-TE energy, exergy, and environmental (3E) performance are selected using the technique for order performance by similarity to ideal solution (TOPSIS) decision algorithm. Finally, the optimization workflow is deployed for 7000 test cases generated from 10 features using the optimal machine learning (ML) algorithm. The result of the optimization chosen by the TOPSIS decision-making method generated an output power, exergy efficiency, and CO2 saving of 44.6 W, 18.3%, and 0.17 g/day, respectively. Furthermore, among other ML algorithms, the Gaussian process regression was the most accurate in learning the CPV-TE performance dataset, although it required more computational effort than some algorithms like the linear regression model.

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Performance analysis of the photovoltaic‐thermoelectric system combined with serpentine type water collector

Cited by 5 publications

References 42 publications

Machine Learning Performance Prediction of a Solar Photovoltaic-Thermoelectric System with Various Crystalline Silicon Cell Types

Machine Learning Performance Prediction of a Solar Photovoltaic-Thermoelectric System with Various Crystalline Silicon Cell Types

Global Advancement of Nanofluid-Based Sheet and Tube Collectors for a Photovoltaic Thermal System

Multiobjective Optimization and Machine Learning Algorithms for Forecasting the 3E Performance of a Concentrated Photovoltaic-Thermoelectric System

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