Exergy Analysis of a Ground Source Heat Pump System for Cold Climatic Condition of Sivas, Turkey

Duman, Netice; Buyruk, Ertan; Acar, Halil İbrahim; Caner, Mustafa; Kılınç, Ferhat; Can, Ahmet

doi:10.21278/tof.si1006321

Cited by 3 publications

(2 citation statements)

References 10 publications

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“…In this study, a horizontal GSHP system was installed for the heating of a building called energy house located in the university campus of Sivas Cumhuriyet University [31][32]. The three-room energy house with 30 m 2 floor area is shown in Fig.…”

Section: Methodsmentioning

confidence: 99%

Performance Prediction of Ground Source Heat Pump System With Data-Driven Modelling: Application of Machine Learning Techniques

DUMAN,

YÜKSEK,

BUYRUK

et al. 2024

Preprint

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Ground source heat pumps (GSHP) offer a clean and sustainable energy solution by using ground heat as a heat source. Various machine learning techniques are used to estimate coefficient of performance (COP), one of the key metrics for evaluating ground source heat pump systems. In this study, three different machine learning methods, Artificial Neural Network (ANN), Support Vector Machines (SVM) and Gaussian Process Regression (GPR), were used to estimate the performance of the system, using measurements made on an experimental GSHP system established in Sivas province of Turkey. Five different combinations are recommended for each method. Training and testing data were used to evaluate the effectiveness of each method in estimating the COP in the GSHP system. In the first stage, ANN, SVM and GPR models were trained on training data, and in the second stage, validation was carried out with test data. The statistical results obtained show that all three machine learning methods are successful in estimating COP in the GSHP system. R2 values of the model MIP1 in which all variables are used are determined as 0.9726 for ANN, 0.9543 for SVM, and 0.9650 for GPR. This study has demonstrated that different machine learning models can be used as an effective method for predicting the performance of GSHP systems. Additionally, by comparing the applicability and performance of these models, it provides information on determining the most appropriate model that can be used to increase the energy efficiency of GSHP systems and optimize costs.

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Section: Methodsmentioning

confidence: 99%

Performance Prediction of Ground Source Heat Pump System With Data-Driven Modelling: Application of Machine Learning Techniques

DUMAN,

YÜKSEK,

BUYRUK

et al. 2024

Preprint

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“…Mostly, the performance evaluations of PV power systems are made with the energy analysis method. Moreover, the exergy analysis method is more detailed, it is used to determine from which part of the system the losses originate [6][7][8].…”

Section: Introductionmentioning

confidence: 99%

Exergoeconomic Analysis of a Photovoltaic Array Affected by Dynamic Shading

Keskin

2023

Journal of Scientific Reports-A

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Photovoltaic (PV) panels are affected by undesirable elements that exist around them, trees, structures, clouds, etc., as well as natural dirt, and dust accumulation on the PV surfaces. Unfortunately, partial shading falling on top of the PV panels may affect badly the output of photovoltaic arrays. In this study, an exergoeconomic analysis has been performed on the impact of dynamic partial shading created by a mislocated building on a photovoltaic array. Both experimental and theoretical results of this study have been compared on ambient temperature, solar radiation intensity, and shading ratio. The observations have been carried out on clear days starting in June 2018 to Mai 2019. According to the results, the shaded PV exergy efficiency (6.87%) and exergoeconomic parameter (Rex= 0.18508 W/$) are maxima in June and minimum in February (Ex =4.76%, Rex= 0.12228 W/$). As a result of this study, it can be said that the PV array exposed to long-term shading will seriously affect the service life of the PV array.

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Experimental comparison of First-law analysis of peak hour operation of GSHP system under optimized conditions

Sah,

Murugesan,

Rajasekar

2025

Applied Thermal Engineering

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Exergy Analysis of a Ground Source Heat Pump System for Cold Climatic Condition of Sivas, Turkey

Cited by 3 publications

References 10 publications

Performance Prediction of Ground Source Heat Pump System With Data-Driven Modelling: Application of Machine Learning Techniques

Performance Prediction of Ground Source Heat Pump System With Data-Driven Modelling: Application of Machine Learning Techniques

Exergoeconomic Analysis of a Photovoltaic Array Affected by Dynamic Shading

Experimental comparison of First-law analysis of peak hour operation of GSHP system under optimized conditions

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