Analysis of a Thermal Energy Storage Tank in a Large District Cooling System: A Case Study

Majid, Mohd Amin Abd; Muhammad, Masdi; Hampo, Chima Cyril; Akmar, Ainul Bt

doi:10.3390/pr8091158

Cited by 13 publications

(6 citation statements)

References 15 publications

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“…Figure 3 shows that during charging, the temperature distribution moves from the bottom to the top direction, and the width of the thermocline thickness increases with the passage of time. This behavior has been verified in many previous studies, such as [ 6 , 38 , 39 , 40 ]. Evolution of temperature distribution and thermocline thickness during charging is shown in Figure 3 .…”

Section: Methodssupporting

confidence: 84%

“…The above-mentioned factors impact the temperature distribution in the TES tank during the charging period; the transition between hot and cold water in the tank is known as the thermocline thickness (

). The performance of TES tank is determined through thermocline thickness (

) [ 4 , 5 , 6 ]. Multiple methods have been proposed to calculate

, such as a small-scale experimental setup, finite element analysis, computational fluid dynamics, and curve-fitting from sensor data.…”

Section: Introductionmentioning

confidence: 99%

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Machine Learning Approach to Predict the Performance of a Stratified Thermal Energy Storage Tank at a District Cooling Plant Using Sensor Data

Soomro

Mokhtar

Salilew

et al. 2022

Sensors

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In the energy management of district cooling plants, the thermal energy storage tank is critical. As a result, it is essential to keep track of TES results. The performance of the TES has been measured using a variety of methodologies, both numerical and analytical. In this study, the performance of the TES tank in terms of thermocline thickness is predicted using an artificial neural network, support vector machine, and k-nearest neighbor, which has remained unexplored. One year of data was collected from a district cooling plant. Fourteen sensors were used to measure the temperature at different points. With engineering judgement, 263 rows of data were selected and used to develop the prediction models. A total of 70% of the data were used for training, whereas 30% were used for testing. K-fold cross-validation were used. Sensor temperature data was used as the model input, whereas thermocline thickness was used as the model output. The data were normalized, and in addition to this, moving average filter and median filter data smoothing techniques were applied while developing KNN and SVM prediction models to carry out a comparison. The hyperparameters for the three machine learning models were chosen at optimal condition, and the trial-and-error method was used to select the best hyperparameter value: based on this, the optimum architecture of ANN was 14-10-1, which gives the maximum R-Squared value, i.e., 0.9, and minimum mean square error. Finally, the prediction accuracy of three different techniques and results were compared, and the accuracy of ANN is 0.92%, SVM is 89%, and KNN is 96.3%, concluding that KNN has better performance than others.

show abstract

Section: Methodssupporting

confidence: 84%

). The performance of TES tank is determined through thermocline thickness (

) [ 4 , 5 , 6 ]. Multiple methods have been proposed to calculate

, such as a small-scale experimental setup, finite element analysis, computational fluid dynamics, and curve-fitting from sensor data.…”

Section: Introductionmentioning

confidence: 99%

Machine Learning Approach to Predict the Performance of a Stratified Thermal Energy Storage Tank at a District Cooling Plant Using Sensor Data

Soomro

Mokhtar

Salilew

et al. 2022

Sensors

View full text Add to dashboard Cite

show abstract

“…This works by discharging cold energy to meet the cooling loads, particularly at peak demand. Integrating a TES tank into a DCS setup helps improve operational efficiency, enhance the life span of the DCS, and reduce high tariff charges for peak and off-peak periods of the day [95,96]. Therefore, the workload of the electricity grid in the DCS is supported through the deployment of TES, leading to lower energy consumption, and reducing the cost of refrigeration using high electricity consumption due to transitioning to off-peak hours.…”

Section: Thermal Energy Storage (Tes)mentioning

confidence: 99%

A Review on Green Cooling: Exploring the Benefits of Sustainable Energy-Powered District Cooling with Thermal Energy Storage

Al-Nini

Almahbashi

et al. 2023

Sustainability

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This paper examines the economic and environmental impacts of district cooling systems (DCS) that are integrated with renewable energy sources and thermal energy storage (TES). Typically, a DCS offers a highly efficient and environmentally friendly alternative to traditional air conditioning systems, providing cool air to buildings and communities through a centralized system that uses chilled water. However, the integration of renewable energy and thermal energy storage into these systems can further increase their sustainability and efficiency, reducing their dependence on fossil fuels and improving their ability to handle fluctuations in demand. The goal of this paper is to provide a comprehensive review of the current state of the art of renewable energy-driven DCS with TES integrated and to highlight the benefits and challenges associated with these systems. Finally, the findings of this paper offer valuable insights into the potential for renewable energy-powered district cooling systems to contribute to a more sustainable and efficient built environment.

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“…The other two VCC systems serve as backup chillers, supporting the cooling load in a plant failure or scheduled maintenance and high chilled water demand scenarios. Figure 2 shows an example of data from the four VCC systems, over a week [17].…”

Section: System Descriptionmentioning

confidence: 99%

Life Cycle Assessment of a Vapor Compression Cooling System Integrated within a District Cooling Plant

Hampo

Majid

et al. 2021

Sustainability

Self Cite

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In standard district cooling (DC) plants, central chillers produce cold energy for space cooling throughout the district network. In recent times, the integration of the vapor compression system, which includes the functionalities of vapor compression chillers (VCC), and thermal energy storage (TES) tanks in the DC setup, has gained more implementation across the globe. This is due to the possibility of load shifting by using the VCC to produce chilled water for charging the TES tanks during off peak periods. Since the environmental implications of various energy intensive systems are largely determined by the amount of material and energy consumed throughout their life cycle, it is critical to conduct a sustainability assessment of these systems in terms of environmental contributions, and suggest design options to reduce these impacts. A cradle to grave life cycle assessment (LCA) model is created in response to these issues and in order to meet the project’s objectives. The life cycle impact assessment (LCIA) results of the analysis reveal that the carbon footprint per 1 RTh of the produced chilled water is estimated at 0.72 kg CO2 eq/RTh. The operation phase of the system’s life cycle accounted for the most impact, about 98%, with other life cycle phases having negligible contributions. In substantiating the study’s investigation, the environmental performance based on several design options were discussed and compared to the case study. Among the several scenarios considered, incorporating the Sweden mix technology provided the case study with the most significant environmental savings, of about 94%.

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Analysis of a Thermal Energy Storage Tank in a Large District Cooling System: A Case Study

Cited by 13 publications

References 15 publications

Machine Learning Approach to Predict the Performance of a Stratified Thermal Energy Storage Tank at a District Cooling Plant Using Sensor Data

Machine Learning Approach to Predict the Performance of a Stratified Thermal Energy Storage Tank at a District Cooling Plant Using Sensor Data

A Review on Green Cooling: Exploring the Benefits of Sustainable Energy-Powered District Cooling with Thermal Energy Storage

Life Cycle Assessment of a Vapor Compression Cooling System Integrated within a District Cooling Plant

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