Deep neural networks for quick and precise geometry optimization of segmented thermoelectric generators

Maduabuchi, Chika; Eneh, Chibuoke; Alrobaian, Abdulrahman A.

doi:10.1016/j.energy.2022.125889

Cited by 24 publications

(5 citation statements)

References 49 publications

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“…Some popular machine learning algorithms used for optimization include gradient descent, genetic algorithms, and reinforcement learning. By leveraging the power of machine learning for optimization, several novel researches have been conducted and findings published to aid the scientific community [7,[100][101][102][103][104][105]. There are three primary stages involved in an ML system (Figure 9).…”

Section: Inorganic Pcmsmentioning

confidence: 99%

“…As a result, the proposed network was found to be 128.51 times faster than the traditional numerical method. Furthermore, on deep neural networks for the optimization of PV systems, Maduabuchi et al [104] utilized DNN for the purpose of enhancing the thermomechanical performance of segmented TEGs through device geometry optimization. The conventional approach of using numerical methods to enhance the efficacy of segmented thermoelectric generators has proven to require a significant amount of computing time and energy.…”

Section: Optimization Of Pv-te Techniques Using Machinementioning

confidence: 99%

“…And lastly, another challenge is high-dimensional parameter space. The performance of PV-TE devices is influenced by a multitude of parameters, such as material properties [90], device geometry [104,141], and operating conditions. Optimizing these parameters using conventional numerical tools can be computationally expensive and time-consuming, especially when dealing with a high-dimensional parameter space.…”

Section: Challenges Of Conventional Optimization Toolsmentioning

confidence: 99%

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Latest Advancements in Solar Photovoltaic-Thermoelectric Conversion Technologies: Thermal Energy Storage Using Phase Change Materials, Machine Learning, and 4E Analyses

Alghamdi,

Maduabuchi,

Okoli

et al. 2024

International Journal of Energy Research

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In recent times, the significance of renewable energy generation has increased and photovoltaic-thermoelectric (PV-TE) technologies have emerged as a promising solution. However, the incorporation of these technologies still faces difficulties in energy storage and optimization. This review paper addresses these challenges by providing a comprehensive overview of the latest advancements in PV-TE technologies. The paper emphasizes the integration of phase change materials (PCMs) for thermal energy storage, also buttressing the use of encapsulated PCM for thermal storage and efficiency, and the use of hybrid PCM to enhance overall performance. Furthermore, reviews on the use of machine learning techniques for efficient optimization and the integration of thermoelectric modules into tandem perovskite silicon solar cells have been comprehensively analyzed. The advancements in photovoltaic-thermoelectric systems, as reviewed in this article, signify significant progress in attaining sustainable and effective energy production and storage. This review comprehensively addresses the 4Es, underlining their importance. It not only consolidates recent developments but also charts a path for future research in the field of PV-TE technologies, offering precise insights to guide upcoming studies and innovations.

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Section: Inorganic Pcmsmentioning

confidence: 99%

Section: Optimization Of Pv-te Techniques Using Machinementioning

confidence: 99%

Section: Challenges Of Conventional Optimization Toolsmentioning

confidence: 99%

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Latest Advancements in Solar Photovoltaic-Thermoelectric Conversion Technologies: Thermal Energy Storage Using Phase Change Materials, Machine Learning, and 4E Analyses

Alghamdi,

Maduabuchi,

Okoli

et al. 2024

International Journal of Energy Research

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“…Although the feasibility of the photovoltaic-thermoelectric system has been demonstrated using various theoretical and experimental methods [18,19], there is still room for further enhancements in system efficiency [20][21][22]. The system's performance can be further increased by optimizing the geometry of the thermoelectric generator.…”

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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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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“…One major gap is the need for more efficient optimization algorithms for large-scale deep learning (Shi et al, 2023) (Maduabuchi et al, 2023).…”

mentioning

confidence: 99%

Efficient optimization algorithms for various machine learning tasks, including classification, regression, and clustering

Sihotang

Albert²,

Riandari

et al. 2023

Idea

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The research on efficient optimization algorithms for machine learning is novel because it addresses several gaps in previous research and proposes new solutions to improve the efficiency and accuracy of machine learning models. Firstly, the proposed research focuses on developing more efficient algorithms for large-scale deep learning. While there have been many optimization algorithms proposed for deep learning, the proposed research aims to develop new algorithms that can handle the complexity and scale of these models and improve their efficiency. Secondly, the proposed research aims to explore the effectiveness of optimization algorithms for different types of machine learning tasks. While many studies have focused on deep learning, the proposed research aims to evaluate the effectiveness of optimization algorithms for other types of machine learning tasks, such as reinforcement learning, unsupervised learning, and semi-supervised learning. Thirdly, the proposed research aims to develop optimization algorithms that can handle noisy and incomplete data, which is a significant challenge for machine learning models. The proposed research aims to develop algorithms that can handle noisy and incomplete data and improve the accuracy of machine learning models. Fourthly, the proposed research aims to develop optimization algorithms that can handle non-convex objective functions. While some optimization techniques have been proposed for non-convex optimization, the proposed research aims to develop new algorithms that can handle these functions and improve the accuracy of machine learning models. The proposed research aims to investigate the trade-off between optimization efficiency and model performance. While previous research has explored this trade-off to some extent, the proposed research aims to develop algorithms that can balance these factors and optimize both efficiency and performance. The proposed research is novel because it addresses several gaps in previous research and proposes new solutions to improve the efficiency and accuracy of machine learning models for various tasks, including classification, regression, and clustering. By developing new algorithms and evaluating their effectiveness for different types of machine learning tasks, the proposed research can advance the field of machine learning and improve the accuracy and efficiency of machine learning models.

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Deep neural networks for quick and precise geometry optimization of segmented thermoelectric generators

Cited by 24 publications

References 49 publications

Latest Advancements in Solar Photovoltaic-Thermoelectric Conversion Technologies: Thermal Energy Storage Using Phase Change Materials, Machine Learning, and 4E Analyses

Latest Advancements in Solar Photovoltaic-Thermoelectric Conversion Technologies: Thermal Energy Storage Using Phase Change Materials, Machine Learning, and 4E Analyses

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

Efficient optimization algorithms for various machine learning tasks, including classification, regression, and clustering

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