Performance evaluation of an actual building air-conditioning system

Othman, Mohd Fauzi; Abdullah, Hayati; Sulaiman, Noor Asyikin; Hassan, M. Y.

doi:10.1088/1757-899x/50/1/012051

Cited by 3 publications

(9 citation statements)

References 3 publications

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“…The system coefficient of performance (COP) was also analyzed and presented in Sulaiman et al [13]. The results show that the COPs reduce when the system has faults, which is consistent with the results energy audit of the actual system presented in Othman et al [1]. All parameters in Table 1 was logged during various conditions simulations in the lab-scaled system.…”

Section: Lab Scaled Of Centralised Chilled Water Air Conditioning Systemsupporting

confidence: 73%

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Parameter selection in data-driven fault detection and diagnosis of the air conditioning system

Sulaiman

Abdullah

et al. 2022

IJEECS

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Data-driven fault detection and diagnosis system (FDD) has been proven as simple yet powerful to identify soft and abrupt faults in the air conditioning system, leading to energy saving. However, the challenge is to obtain reliable operation data from the actual building. Therefore, a lab-scaled centralized chilled water air conditioning system was successfully developed in this paper. All necessary sensors were installed to generate reliable operation data for the data-driven FDD. Nevertheless, if a practical system is considered, the number of sensors required would be extensive as it depends on the number of rooms in the building. Hence, parameters impact in the dataset were also investigated to identify critical parameters for fault classifications. The analysis results had identified four critical parameters for data-driven FDD: the rooms' temperature (TTCx), supplied chilled water temperature (TCHWS), supplied chilled water flow rate (VCHWS) and supplied cooled water temperature (TCWS). Results showed that the data-driven FDD successfully diagnosed all six conditions correctly with the proposed parameters for more than 92.3% accuracy; only 0.6-3.4% differed from the original dataset's accuracy. Therefore, the proposed parameters can reduce the number of sensors used for practical buildings, thus reducing installation costs without compromising the FDD accuracy.

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Section: Lab Scaled Of Centralised Chilled Water Air Conditioning Systemsupporting

confidence: 73%

“…Faults in air conditioning systems, especially soft faults, are hard to detect. Even a regularly maintained building may suffer from soft faults without realising it [1]. Therefore, fault detection and diagnosis (FDD) plays an important role in building energy savings.…”

Section: Introductionmentioning

confidence: 99%

Parameter selection in data-driven fault detection and diagnosis of the air conditioning system

Sulaiman

Abdullah

et al. 2022

IJEECS

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“…During experiments, the energy consumption of the prototype system was logged to measure the performance of the system. The performance of an air-conditioning system is measured by the Coefficient of Performance or COP [4]. The COP can be calculated as in (2).…”

Section: Energy Consumption and Copmentioning

confidence: 99%

“…Heating, ventilation, and airconditioning (HVAC) system is one of the leading energy consumers in the building and use up to 50% of the total building energy consumption [1][2]. In Malaysia, office buildings use the most energy for air conditioning system compared to other buildings such as hotels or shopping complexes [3][4]. In practice, the coefficient of performance (COP) is used as a measurement index to measure the performance of the air conditioning system [4].…”

Section: Introductionmentioning

confidence: 99%

“…In Malaysia, office buildings use the most energy for air conditioning system compared to other buildings such as hotels or shopping complexes [3][4]. In practice, the coefficient of performance (COP) is used as a measurement index to measure the performance of the air conditioning system [4]. The COP is a ratio of the rate of net heat removal of the rate of total energy input.…”

Section: Introductionmentioning

confidence: 99%

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Fault detection for air conditioning system using machine learning

Sulaiman

Abdullah

et al. 2020

IJ-AI

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Air conditioning system is a complex system and consumes the most energy in a building. Any fault in the system operation such as cooling tower fan faulty, compressor failure, damper stuck, etc. could lead to energy wastage and reduction in the system’s coefficient of performance (COP). Due to the complexity of the air conditioning system, detecting those faults is hard as it requires exhaustive inspections. This paper consists of two parts; i) to investigate the impact of different faults related to the air conditioning system on COP and ii) to analyse the performances of machine learning algorithms to classify those faults. Three supervised learning classifier models were developed, which were deep learning, support vector machine (SVM) and multi-layer perceptron (MLP). The performances of each classifier were investigated in terms of six different classes of faults. Results showed that different faults give different negative impacts on the COP. Also, the three supervised learning classifier models able to classify all faults for more than 94%, and MLP produced the highest accuracy and precision among all.

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Statistical analysis of air-conditioning and total load diversity in typical residential buildings

et al. 2021

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Electricity consumption by air-conditioners in residential buildings significantly affects the electrical grid. The stability of the grid can be improved by a precise prediction of air-conditioning (AC) variations. Therefore, more information about the electricity usage patterns specifically on AC load is important. This paper aims to determine the consumption patterns of AC and total load through a field measurement of 20 dwellings located in Kuala Lumpur and Selangor. Next, correlation and clustering are employed to identify the relationship between outdoor temperature and AC electricity consumption. The results revealed 75% of the occupants use the AC almost every day for about five to six hours per day at the rates of 0.93 kWh/day during day time and 3.43 kWh/day during night time. The correlation coefficient between the total daily AC consumption and the mean daily outdoor temperature ranged from -0.1 to 0.48. The variation of electricity consumption with time shows that AC electricity consumption increases with the increase of usage time. Lastly, the clustering process of AC usage pattern based on outdoor temperature displayed the uncommon behaviour from dwellings whereby high AC electricity consumption was recorded at low outdoor temperature.

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Performance evaluation of an actual building air-conditioning system

Cited by 3 publications

References 3 publications

Parameter selection in data-driven fault detection and diagnosis of the air conditioning system

Parameter selection in data-driven fault detection and diagnosis of the air conditioning system

Fault detection for air conditioning system using machine learning

Statistical analysis of air-conditioning and total load diversity in typical residential buildings

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