Simulative Analysis of Metropolitan Electric Distribution Network using Conjugate Gradient Neuro-algorithm with Powell/Beale Restarts

Adedayo, Ojo O.; Onibonoje, Moses Oluwafemi; Fabunmi, Temitayo Emmanuel

doi:10.1109/icecet52533.2021.9698441

Cited by 1 publication

(2 citation statements)

References 7 publications

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“…As soon as the number of mesh weights and deviations equals the number of iterations, the standard reset point is reached. However, there exists other approaches that can improve training efficiency, and one of these approaches is the Powell Beale restart method [12] .…”

Section: Results and Discussion 31 Ann Design For The Passive Cooling...mentioning

confidence: 99%

“…Several studies have demonstrated that the temperature at the inlet, the circulating fluid velocity, the vertical pipe's center-to-center distance and the spiral pipe's higher pitch, as well as the thermal conductivity of the material in the filler are the factors influencing the system's performance [12] . This complex process is one of the most challenging in terms of obtaining data on the temperature and variation of underground soil, a factor that is paramount to making an informed assessment.…”

Section: Introductionmentioning

confidence: 99%

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An Estimation of the Performance of a Passive Cooling System Incorporating a Ground Heat Exchanger Using a Multilayer Artificial Neural Network

2022

J Mod Green Energy

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Objective: This paper examines the cooling of an office building without a heat pump, using only ground heat exchangers (GHE) and implementing artificial neural network (ANN) to train it on experimental data. Methods:The office building is situated at 38° 40′ 57′′ N latitude and 39° 10′ 29′′ E longitude in the province of Elazig. Each minute, the installed system was monitoring the office's external meteorological data, the office's indoor meteorological data, the GHE inlet and outlet temperatures, and the amount of heat load developed during the cooling process. In this study, the passive cooling system's cooling load and coefficient of performance (COP) were experimentally examined. A second important contribution of this paper is the multilayer ANN model that was created using data selected from the experimental setup, which was measured and recorded.Results: During the summer months of 2018, the COP of the system was measured to be 1.67 on average. The accuracy rates of the multilayer ANN model proposed for cooling systems were calculated to be over 99% and 95% in the training and test datasets, respectively. It was observed that the performance value estimated by ANN converges to the true value by 99%. Conclusion:Having performed this study, it has been demonstrated that passive cooling can be achieved with GHE, and by conducting this study without utilizing a heat pump system, we intend to contribute significantly to the relevant scientific literature.

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Section: Results and Discussion 31 Ann Design For The Passive Cooling...mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

An Estimation of the Performance of a Passive Cooling System Incorporating a Ground Heat Exchanger Using a Multilayer Artificial Neural Network

2022

J Mod Green Energy

View full text Add to dashboard Cite

show abstract

Simulative Analysis of Metropolitan Electric Distribution Network using Conjugate Gradient Neuro-algorithm with Powell/Beale Restarts

Cited by 1 publication

References 7 publications

An Estimation of the Performance of a Passive Cooling System Incorporating a Ground Heat Exchanger Using a Multilayer Artificial Neural Network

An Estimation of the Performance of a Passive Cooling System Incorporating a Ground Heat Exchanger Using a Multilayer Artificial Neural Network

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