Energy Management System Industrialization for Off-Grids Power Systems Based on Data-Driven Machine Learning Models

Hernández, David Mendoza; Denis, Yvan

doi:10.2139/ssrn.4003926

Cited by 2 publications

(2 citation statements)

References 34 publications

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“…𝑂 𝑡 = 𝑔(𝑊 𝑜 • [ℎ 𝑡−1 . 𝑋 𝑡 ] + 𝑏 𝑜 ) (20) ℎ 𝑡 = 𝑂 𝑡 * tanh(𝐶 𝑡 ) (21) Also, o index introduces the cell-state output parameter. The AI predictions are done using these equations of the LSTM algorithm.…”

Section: Deep Neural Network Methodsmentioning

confidence: 99%

“…Also, Chu et al [19] studied the combined impacts of the thermal dependence in convection heat transfer by considering radiative heat flux boundary conditions. Among all of the tools used in the industry, machine learning with artificial neural network (ANN) and internet of things (IoT) is a powerful modeling and forecasting tool that offers an alternative way to solve complex problems such as predicting production capacity [20]. Also, several studies were conducted in the case of different machine learning methods approximations in a heat transfer field [21][22][23].…”

Section: Introductionmentioning

confidence: 99%

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Comparative Analysis of Machine Learning and Numerical Modeling for Combined Heat Transfer in Polymethylmethacrylate

et al. 2022

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This study has compared different methods to predict the simultaneous effects of conductive and radiative heat transfer in a polymethylmethacrylate (PMMA) sample. PMMA is a type of polymer utilized in various sensors and actuator devices. One-dimensional combined heat transfer is considered in numerical analysis. Computer implementation was obtained for the numerical solution of the governing equation with the implicit finite difference method in the case of discretization. Kirchhoff transformation was used to obtain data from a non-linear equation of conductive heat transfer by considering monochromatic radiation intensity and temperature conditions applied to the PMMA sample boundaries. For the deep neural network (DNN) method, the novel long short-term memory (LSTM) method was introduced to find accurate results in the least processing time compared to the numerical method. A recent study derived the combined heat transfer and temperature profiles for the PMMA sample. Furthermore, the transient temperature profile was validated by another study. A comparison proves the perfect agreement. It shows the temperature gradient in the primary positions, which provides a spectral amount of conductive heat transfer from the PMMA sample. It is more straightforward when they are compared with the novel DNN method. Results demonstrate that this artificial intelligence method is accurate and fast in predicting problems. By analyzing the results from the numerical solution, it can be understood that the conductive and radiative heat flux are similar in the case of gradient behavior, but the amount is also twice as high approximately. Hence, total heat flux has a constant value in an approximated steady-state condition. In addition to analyzing their composition, the receiver operating characteristic (ROC) curve and confusion matrix were implemented to evaluate the algorithm’s performance.

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Section: Deep Neural Network Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Comparative Analysis of Machine Learning and Numerical Modeling for Combined Heat Transfer in Polymethylmethacrylate

et al. 2022

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Deep Learning for Modeling an Offshore Hybrid Wind–Wave Energy System

et al. 2022

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The combination of an offshore wind turbine and a wave energy converter on an integrated platform is an economical solution for the electrical power demand in coastal countries. Due to the expensive installation cost, a prediction should be used to investigate whether the location is suitable for these sites. For this purpose, this research presents the feasibility of installing a combined hybrid site in the desired coastal location by predicting the net produced power due to the environmental parameters. For combining these two systems, an optimized array includes ten turbines and ten wave energy converters. The mathematical equations of the net force on the two introduced systems and the produced power of the wind turbines are proposed. The turbines’ maximum forces are 4 kN, and for the wave energy converters are 6 kN, respectively. Furthermore, the comparison is conducted in order to find the optimum system. The comparison shows that the most effective system of desired environmental condition is introduced. A number of machine learning and deep learning methods are used to predict key parameters after collecting the dataset. Moreover, a comparative analysis is conducted to find a suitable model. The models’ performance has been well studied through generating the confusion matrix and the receiver operating characteristic (ROC) curve of the hybrid site. The deep learning model outperformed other models, with an approximate accuracy of 0.96.

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Energy Management System Industrialization for Off-Grids Power Systems Based on Data-Driven Machine Learning Models

Cited by 2 publications

References 34 publications

Comparative Analysis of Machine Learning and Numerical Modeling for Combined Heat Transfer in Polymethylmethacrylate

Comparative Analysis of Machine Learning and Numerical Modeling for Combined Heat Transfer in Polymethylmethacrylate

Deep Learning for Modeling an Offshore Hybrid Wind–Wave Energy System

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