Optimization of district heating system aided by geothermal heat pump: A novel multistage with multilevel ANN modelling

Arat, Halit; Arslan, Oğuz

doi:10.1016/j.applthermaleng.2016.09.150

Cited by 59 publications

(18 citation statements)

References 51 publications

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“…Table 4 lists some important papers in this field. Arat and Arslan (2017) [42] presented an optimum design for a geothermal heat pump for a district heating system. Three different back propagation learning algorithms was used.…”

Section: Elm and Other Advanced Annsmentioning

confidence: 99%

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State of the Art of Machine Learning Models in Energy Systems, a Systematic Review

et al. 2019

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Machine learning (ML) models have been widely used in the modeling, design and prediction in energy systems. During the past two decades, there has been a dramatic increase in the advancement and application of various types of ML models for energy systems. This paper presents the state of the art of ML models used in energy systems along with a novel taxonomy of models and applications. Through a novel methodology, ML models are identified and further classified according to the ML modeling technique, energy type, and application area. Furthermore, a comprehensive review of the literature leads to an assessment and performance evaluation of the ML models and their applications, and a discussion of the major challenges and opportunities for prospective research. This paper further concludes that there is an outstanding rise in the accuracy, robustness, precision and generalization ability of the ML models in energy systems using hybrid ML models. Hybridization is reported to be effective in the advancement of prediction models, particularly for renewable energy systems, e.g., solar energy, wind energy, and biofuels. Moreover, the energy demand prediction using hybrid models of ML have highly contributed to the energy efficiency and therefore energy governance and sustainability.

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Section: Elm and Other Advanced Annsmentioning

confidence: 99%

“…RMSE values of the study of Arat and Arslan (2017). Reproduced from [42], Elsevier: 2017. Bagnasco et al (2015) presented a study of power consumption forecasting (load forecasting model) in hospitals.…”

Section: Elm and Other Advanced Annsmentioning

confidence: 99%

State of the Art of Machine Learning Models in Energy Systems, a Systematic Review

et al. 2019

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“…Further, the choice of HP technology e.g. refrigerant may also impose a significant change in the cost of supplied heat [10]. For utilisation in Danish DH systems, the temperature requirements for direct utilisation limits the possibilities and economic applicability [11].…”

Section: Introductionmentioning

confidence: 99%

Design of serially connected district heating heat pumps utilising a geothermal heat source

Jensen

Ommen

Markussen

et al. 2017

Energy

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The design of two heat pumps (HP), connected in series, was investigated for operation in the district heating (DH) network of the Greater Copenhagen area, Denmark. The installation was dimensioned to supply 7.2 MW of heat at a temperature of 85 • C. The heat pumps utilise a geothermal heat source at 73 • C. Both heat source and sink experience a large temperature change, which may lead to decreased performance for single vapour compression HP. The performance may be increased by using HPs connected in series and by applying HPs with zeotropic mixtures. First a generic study with a simple representation of the HP was applied to investigate optimal system configurations. It was shown that using two heat pumps in series with direct heat exchange in parallel with the first heat pump could increase the performance compared to the HP performance. Detailed thermodynamic models of a zeotropic mixture HP predicted that an exergetic efficiency of the units between 50 % and 65 % is possible. The technical feasibility as well as the economic viability of this installation was investigated for a range of optimal configurations. The analysis recommends a heat pump configuration with a system exergetic efficiency of 63 %.

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“…Numerous scholars have analyzed and evaluated the heating capacities of GSHP systems [32][33][34][35][36]. For example, analyzed the economic benefits of a GSHP using the life cycle cost (LCC) and the net present value (NPV); the authors calculated the number of dwellings that could be heated and optimized the district heating system aided by a ground-source heat pump using a novel artificial neural network (ANN) [37,38]. Keçebaş (2016) investigated a geothermal district heating system by using an exergo-environmental analysis and found that the environmental impacts of the system were reduced when the ambient temperature decreased and the wellhead temperature increased [39].…”

Section: Introductionmentioning

confidence: 99%

Environmental and Economic Benefit Analysis of an Integrated Heating System with Geothermal Energy—A Case Study in Xi’an China

Yan

Qin

2017

Energies

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Due to the increase in environmental problems and air pollution during the heating period, it is important to promote clean heating in cold regions, thereby meeting the heating demand in a green manner. In order to allocate resources more effectively and facilitate the consumption of renewable energy, this paper designs an integrated heating system that incorporates geothermal energy into the framework of an integrated energy system of electricity, heating, and gas. An analysis of the environmental and economic benefits indicates that the system reduces pollutant emissions and decreases the cost of urban heating. Using an example of central heating of residential buildings in Xi'an, the paper conducts a scenario analysis based on the gas peak-shaving ratio and the ratio of geothermal heating loads to basic heating loads. The results demonstrate that the environmental and economic benefits of the integrated heating system are higher compared to central heating using coal-fired boilers. In addition, this paper conducts a sensitivity analysis of the heat source to the price factors and the load ratios. The results show that the operating costs of the integrated heating system are most sensitive to the natural gas price and the gas peak-shaving ratio. Therefore, an optimum natural gas peak-shaving ratio can be determined.

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Optimization of district heating system aided by geothermal heat pump: A novel multistage with multilevel ANN modelling

Cited by 59 publications

References 51 publications

State of the Art of Machine Learning Models in Energy Systems, a Systematic Review

State of the Art of Machine Learning Models in Energy Systems, a Systematic Review

Design of serially connected district heating heat pumps utilising a geothermal heat source

Environmental and Economic Benefit Analysis of an Integrated Heating System with Geothermal Energy—A Case Study in Xi’an China

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