Artificial Neural Network Based Prediction of Energy Generation from Thermoelectric Generator with Environmental Parameters

Ang, Zi Yang Adrian; Woo, Wai Lok; Ehsan, Mehdi

doi:10.18178/jocet.2017.5.6.416

Cited by 17 publications

(6 citation statements)

References 13 publications

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“…The resulting model demonstrated a prediction accuracy of 98% and achieved precise geometric design and optimization of thermoelectric conversion devices in just 40 s. This is over 1000 times faster than the traditional finite element method [18]. Ang et al employed an artificial neural network to predict the output voltage of a thermoelectric module under varying structural parameters and working conditions, achieving an error rate of only 0.3% between the ANN-generated output voltage and measured values [19]. Kim has developed Python code that utilizes neural networks to accurately predict the performance of thermoelectric generators in diesel engine thermoelectric generation systems, with a margin of error of only 3% between predicted and actual values.…”

Section: Introductionmentioning

confidence: 99%

GA−BP Prediction Model for Automobile Exhaust Waste Heat Recovery Using Thermoelectric Generator

Sun

Wu³

et al. 2023

Processes

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Thermoelectric generator (TEG) has important applications in automotive exhaust waste heat recovery. The Back propagation neural network (BP) can predict the electrical generating performance of TEG efficiently and accurately due to its advantage of being good at handing nonlinear data. However, BP algorithm is easy to fall into local optimum, and its training data usually have deviation since the data are obtained through the simulation software. Both of the problems will reduce the prediction accuracy. In order to further improve the prediction accuracy of BP algorithm, we use the genetic algorithm (GA) to optimize BP neural network by selection, crossover, and mutation operation. Meanwhile, we create a TEG for the heat waste recovery of automotive exhaust and test 84 groups of experimental data set to train the GA−BP prediction model to avoid the deviation caused by the simulation software. The results show that the prediction accuracy of the GA−BP model is better than that of the BP model. For the predicted values of output power and output voltage, the mean absolute percentage error (MAPE) increased to 2.83% and 2.28%, respectively, and the mean square error (MSE) is much smaller than the value before optimization, and the correlation coefficient (R2) of the network model is greater than 0.99.

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Section: Introductionmentioning

confidence: 99%

GA−BP Prediction Model for Automobile Exhaust Waste Heat Recovery Using Thermoelectric Generator

Sun

Wu³

et al. 2023

Processes

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“…They reported that the computational time required to obtain the electric potential and power generation in the TEG was reduced from 6 h when using high fidelity CFD simulations to 3 min using the hybrid method. Ang et al [12] suggested a coupled neural network to predict not only the averaged output values but also the reliability of the output values of a TEG. Despite the novelty of the coupled network, their study was only focused on a single TEM operating using thermal energy dissipated by a light-emitting diode.…”

Section: Introductionmentioning

confidence: 99%

Prediction of System-Level Energy Harvesting Characteristics of a Thermoelectric Generator Operating in a Diesel Engine Using Artificial Neural Networks

Kim

2021

Energies

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This study evaluated the potential of artificial neural networks (ANNs) to predict the system-level performance of a thermoelectric generator (TEG), whose performance depends on various variables including engine load, engine rotation speed, and external load resistance. Therefore, a Python code was developed to determine an optimal ANN structure by tracking the training/prediction errors of the ANN as a function of the number of hidden layers and nodes of hidden layers. The optimal ANN was trained using 484 output current (I)–load resistance (R) datasets obtained under three different engine rotation speeds and five different engine loads. The prediction accuracy of the ANN was validated by comparing 88 I–R datasets reproduced by the ANN using experimental data that were not used for training. In the validation procedure, differences of only 3.49% and 2.59% were observed in the experimental and ANN-predicted output power obtained for the 1000 rpm–0.8 MPa brake mean effective pressure (BMEP) and 1500 rpm–0.4 MPa BMEP scenarios, respectively. The exhaust gas flow characteristics were used for training and validation to predict the pumping loss caused by the installation of the TEG in the middle of the exhaust tailpipe with high accuracy. The results demonstrated that the ANN effectively reproduced datasets to fill the gaps between the discretized experimental results for all the experimental scenarios without any noticeable overfitting and underfitting. The net power gain obtained by the ANN exhibited a clear peak point for the engine rotation speed of 2000 rpm, which is difficult to obtain using experimental data.

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“…In addition, in case of input/s taken from single or multiple sensors, the amount of data available for processing is much less compared to images data. Many ANN-based curve-fitting models have already been published in literature (Adrian Ang et al, 2017;Martinez-Nieto et al, 2019) but most of them are not concerned with their implementation issues on embedded system like microcontrollers. There are a few examples where ANN model has been implemented in microcontrollers but the focus of such research work is also on only system functionality and accuracy (Cotton, 2011;Venzke et al, 2020).…”

mentioning

confidence: 99%

“…The larger the dataset, the higher the memory space reserved for it. This would constitute 12.5% occupancy for a 4096 dataset in 32 kB memory (Adrian Ang et al, 2017). The situation becomes more critical for microcontrollers like Attiny having 1 kB of flash memory with 8 MHz operating frequency.…”

mentioning

confidence: 99%

Artificial neural network and dataset optimization for implementation of linear system models in resource‐constrained embedded systems

et al. 2022

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On-board training of artificial neural network (ANN) is important in instances where real time data are required for model training. Provision of on-board intelligence enables the developed systems to self-recalibrate and enhances their efficiencies. In this work, investigations have been performed to determine optimized parameters of ANN model for linear systems. The performance parameters that is, model parameters, memory requirements, accuracy and processing time are chosen by considering the model to be installed on commercially available microcontrollers that have very limited on-board memory. Minimum data requirements for training ANN models of linear systems are also explored for better performance. All dataset ranges are normalized in order to exclude the effects of range differences. It is shown that for linear systems, 1-3-1 architecture produces best results against ≤100 data points when Bayesian Regularization (BR) training function is used along with Log Sigmoid Activation function. Simulations for 1-3-1 architecture are then performed for datasets having 10, 25, 50 and 100 data points. The results show that training with 25 data points produces over-all better performance than other datasets. A large dataset utilizes more training time and memory whereas a smaller dataset produces relatively lesser accuracy. The effects of clustered data and uniformly distributed data are also explored. It is found that total epochs in case of clustered data are significantly higher than uniformly distributed data. The combination of these optimized parameters that is, 1-3-1 architecture, with BR and Log function, for ≤100 data points can be used for the development and implementation of linear components or systems in resource-constrained embedded systems.

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Artificial Neural Network Based Prediction of Energy Generation from Thermoelectric Generator with Environmental Parameters

Cited by 17 publications

References 13 publications

GA−BP Prediction Model for Automobile Exhaust Waste Heat Recovery Using Thermoelectric Generator

GA−BP Prediction Model for Automobile Exhaust Waste Heat Recovery Using Thermoelectric Generator

Prediction of System-Level Energy Harvesting Characteristics of a Thermoelectric Generator Operating in a Diesel Engine Using Artificial Neural Networks

Artificial neural network and dataset optimization for implementation of linear system models in resource‐constrained embedded systems

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