Case study of performance evaluation of ground source heat pump system based on ANN and ANFIS models

Sun, Wei; Hu, Pingfang; Lei, Fei; Zhu, Na; Jiang, Zhangning

doi:10.1016/j.applthermaleng.2015.04.082

Cited by 64 publications

(34 citation statements)

References 25 publications

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“…Safa et al [23] analyzed the comparative performance of GCHP and air source heat pump (ASHP) in the two semidetached sustainable houses, evaluated the coefficient of performance (COP) at various load/source temperatures and modeled the archetype sustainable twin house and the heat pumps by using TRNSYS simulation, which is also used to predict annual performances of each system for major Canadian cities. In addition to the professional software, various data mining techniques, such as adaptive neuro-fuzzy inference systems (ANFIS) [24], Lin-kernel support vector machine (SVM) [25], artificial neural network (ANN) [26], iteratively reweighed least squared (IRLS) [27], random forest (RF) [27], multivariate adaptive regression splines (MARS) [28], have been widely used in the assessment of building energy performance. Esen et al [24] predicted GCHP performance related to ground and air temperature by using adaptive neuro-fuzzy inference systems (ANFIS) and Linkernel support vector machine (SVM) [25].…”

Section: Related Workmentioning

confidence: 99%

Accurately predicting heat transfer performance of ground heat exchanger for ground-coupled heat pump systems using data mining methods

Zhuang

Ben

Yan

et al. 2016

Neural Comput & Applic

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Nowadays, the ground-coupled heat pump (GCHP) systems have been recognized as one of the most energy-efficient systems for heating, cooling and hot water supply in both residential and commercial buildings. However, the heat transfer of ground heat exchanger (GHE) involves in large spatial scales, long time span and complex influential factors. We develop a data mining framework constructed by using 1998 experimental data to study the effects of 12 input variables composed of seven borehole parameters, two U-tube parameters, two ground parameters and one circulating liquid parameter to accurately predict the heat transfer performance of GHE for GCHP systems in 10 years. Hence, selecting a suitable input configuration to improve the energy efficiency has important sustainability benefits. The role of each of independent variable explaining the output variables is analyzed by partial least squares regression. Furthermore, support vector regression and M5 Model Tree are, respectively, used to predict the heat transfer performance. Extensive simulations show that we can predict the average quantity of heat exchanger, temperature of ground around GHE, inlet temperature of heat pump unit with very low level of error.

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Section: Related Workmentioning

confidence: 99%

Accurately predicting heat transfer performance of ground heat exchanger for ground-coupled heat pump systems using data mining methods

Zhuang

Ben

Yan

et al. 2016

Neural Comput & Applic

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“…The advantages of FLC overcame the drawbacks of the existing programmable logic controller. Another study compared ANN and ANFIS in terms of the chiller input variables in calculating the coefficient of performance (COP) during the summer of 2013 (in Turkey) [7]. ANN has four input variables; evaporator inlet/outlet water temperatures, and condenser inlet/outlet water temperatures.…”

Section: Review Of Artificial Intelligence-based Techniquesmentioning

confidence: 99%

“…The main shortcoming of ANN and ANFIS study by Sun et al is that the flow rate is ignored as a variable input for a system. This results in a low COP [7]. ANN has been used to evaluate COP and given a poor efficiency based on a model regression of 0.93 [8].…”

Section: Summary and Shortcomings Of The Reviewmentioning

confidence: 99%

An Application of a Novel Technique for Assessing the Operating Performance of Existing Cooling Systems on a University Campus

et al. 2018

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Abstract:Optimal operation is an important aspect of energy efficiency that can be employed to reduce power consumption. In cooling systems, the chillers consume a large amount of electricity, especially if they are not optimally operated, therefore, they cannot produce the required or rated cooling load capacity. The objective of this paper is to improve coefficient of performance (COP) for the operation of chillers and to reduce power consumption. Two contributions in this work are: (1) the prediction of a model by using Adaptive Neuro-Fuzzy Inference System (ANFIS)-based Fuzzy Clustering Subtractive (FCS), and (2) the classification and optimization of the predicted models by using an Accelerated Particle Swarm Optimization (APSO) algorithm. Particularly, in contribution (1), two models are developed to predict/assess power consumption and cooling load capacity. While in contribution (2), the predictive model's data obtained are used to classify the operating performance of the chiller and to optimize the model in order to reduce power consumption and cooling capacity. Therefore, data classification by APSO is used to enhance the coefficient of performance (COP). The proposed technique reduces the total power consumption by 33.2% and meets the cooling demand requirements. Also, it improves the cooling performance based on COP, thus resulting in a 15.95% increase in efficiency compared to the existing cooling system. The studied ANFIS-based FCS outperforms the ANFIS-based fuzzy C-means clustering in terms of the regression. Then, the algorithm-based classifier APSO has better results compared to the conventional particle swarm optimization (PSO). The data was acquired from the District Cooling System (DCS) at the Universiti Teknologi Petronas (UTP) campus in Malaysia.

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“…Such networks, whose primary objective is to emulate the learning capabilities of a biological brain, can be trained to perform different tasks. In the field of geothermal energy, ANNs have been used to predict heat pump's COP (Esen, et al 2008;Sun, et al 2015), efficiency of a district geothermal system (Arat and Arslan 2017), model a direct expansion system (Fannou, et al 2014), control district (Yabanova and Keeba 2013) and hybrid (Gang, et al 2014) systems or construct the short-term g-function of a borehole (Pasquier, et al 2018).…”

Section: Introductionmentioning

confidence: 99%

Near-instant g-function construction with artificial neural networks

Dusseault¹,

Pasquier²

2018

Proceedings of the IGSHPA Research Track 2018

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Case study of performance evaluation of ground source heat pump system based on ANN and ANFIS models

Cited by 64 publications

References 25 publications

Accurately predicting heat transfer performance of ground heat exchanger for ground-coupled heat pump systems using data mining methods

Accurately predicting heat transfer performance of ground heat exchanger for ground-coupled heat pump systems using data mining methods

An Application of a Novel Technique for Assessing the Operating Performance of Existing Cooling Systems on a University Campus

Near-instant g-function construction with artificial neural networks

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