Energetic and economic evaluations of geothermal district heating systems by using ANN

Keçebaş, Ali; Alkan, Mehmet Ali; Yabanova, İsmail; Yumurtacı, Mehmet

doi:10.1016/j.enpol.2013.01.039

Cited by 24 publications

(6 citation statements)

References 45 publications

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“…Keçebaș et al performed a study to simulate and assess economically and energetically the operation of a GDHS by using an ANN model, with the objective of minimising the LCC.…”

Section: Overview Of the Optimisation Models In The Literaturementioning

confidence: 99%

A review of modelling and optimisation techniques for district heating systems

2019

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Summary This article provides an extensive survey on the modelling and optimisation of district heating systems (DHSs) focused on the heat distribution network. For this purpose, the major components of a DHS have been described and discussed, and their modelling has been briefly reviewed, including numerical simulation models for heat sources, end‐users, and especially the distribution network. The main deterministic and heuristic optimisation techniques are briefly described. Additionally, a single‐objective and multiobjective optimisation problem is generally formulated, and the main optimisation criteria for the design and operation of the distribution network are synthesised and analysed. The state of the art in DHS optimisation has been also reviewed and categorised. Finally, some recommendations for future developments were included.

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“…Keçebaș et al performed a study to simulate and assess economically and energetically the operation of a GDHS by using an ANN model, with the objective of minimising the LCC.…”

Section: Overview Of the Optimisation Models In The Literaturementioning

confidence: 99%

A review of modelling and optimisation techniques for district heating systems

2019

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“…By using full implicit finite difference methods to solve three-dimensional meshed soil and GHE models, this approach can be implemented in the common practice of GCHPs with vertical or horizontal GHEs and can be modified to simulate complex GCHP applications such as GCHPs with dual sources or solar-assisted GCHPs (Trillat-Berdal et al, 2007;Chargui et al, 2012;Chargui and Sammouda, 2014;Weeratunge et al, 2018). Apart from TRNSYS, researchers have also developed other numerical simulation models and approaches, including Finite Geothermal well Exergy prediction FFNN Keçebaş et al, 2013;(Keçebaş et al, 2013) 6 plate heat exchangers Energy analysis FFNN Element Modelling and Computational Fluid Dynamics (Tarnawski and Leong, 1993;Colangelo et al, 2005;Tarnawski et al, 2009;Wu et al, 2010;Norway et al, 2012;Chong et al, 2013;Go et al, 2016;Esen et al, 2017). Numerical simulation results can be in good agreement with the respective experiments and hence contribute to the optimum design of horizontal GHEs.…”

Section: Introductionmentioning

confidence: 99%

Artificial neural networks for predicting the performance of heat pumps with horizontal ground heat exchangers

Zhou,

Narsilio,

Makasis

et al. 2024

Front. Energy Res.

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A Ground Coupled Heat Pump (GCHP) is a highly energy efficient heating, ventilation, and air conditioning (HVAC) system that utilises the ground as the heat source when heating and as the heat sink when cooling. This paper investigates GCHP systems with horizontal Ground Heat Exchangers (GHEs) in the rural industry, exemplifying the technology for poultry (chicken) sheds in Australia. This investigation aims to provide an Artificial Neural Network (ANN) model that can be used for GCHP design at various locations with different climates. To this extent, a Transient System Simulation Tool (TRNSYS) model for a typical horizontal GHE applied in a rural farm was first verified. Using this model, over 700,000 hourly performance data items were obtained, covering over 80 different yearly loading patterns under three different climate conditions. The simulated performance data was then used to train the ANN. As a result, the trained ANN can predict the performance of GCHP systems with identical (multiple) GHEs even under climatic conditions (and locations) that have not been specifically trained for. Unlike other works, the newly introduced ANN model is accurate even with limited types of input data, with high accuracy (less than 5% error in most cases tested). This ANN model is 100 times computationally faster than TRNSYS simulations and 10,000 times faster than finite element models.

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“…Fannou et al [19] established a model for direct expansion geothermal heat pump using ANN, and showed that the best number of neurons in the hidden layer in the model obtained using the LevenbergeMarquardt algorithm is 28. Kecebas [20] used ANN for the evaluation of the energy input, losses, output, and efficiency of an Afyon geothermal district heating system with the actual thermal data for the heating seasons in the period of 2006e2010. Some control strategies for the geothermal heating system based on the ANN model were also developed [21,22].…”

Section: Introductionmentioning

confidence: 99%