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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Indonesian Journal of Electrical Engineering and Computer Science
Hayati Abdullah
Universiti Teknologi Malaysia
Computer Science & IT Engineering

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Fault detection for air conditioning system using machine learning Noor Asyikin Sulaiman; Md Pauzi Abdullah; Hayati Abdullah; Muhammad Noorazlan Shah Zainudin; Azdiana Md Yusop
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 9, No 1: March 2020
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (568.712 KB) | DOI: 10.11591/ijai.v9.i1.pp109-116

Abstract

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.
Energy efficiency index by considering number of occupants: a study on the lecture rooms in a university building Noor Ameera Zakaria; Mohammad Yusri Hassan; Hayati Abdullah; Md Pauzi Abdullah; Faridah Hussin; Siti Maherah Hussin; Nur Najihah Abu Bakar
Indonesian Journal of Electrical Engineering and Computer Science Vol 15, No 3: September 2019
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v15.i3.pp1154-1160

Abstract

The building sector is attributed to approximately 40% of the nation’s energy consumption and this accounts for a significant percentage of the nation’s energy consumption. For this reason, energy efficiency in buildings has now become an important subject in the national energy scenario. Energy Efficiency Index (EEI) is one of the energy consumption indicators that is widely used in the building sector for measuring energy performance. This index is generally measured based on the energy used per unit of building floor area. However, this index is not able to directly identify other factors affecting energy usage. This paper suggests an Energy Efficiency Index (EEI) for determining the performance of lecturer rooms in a university building. Unlike the conventional EEI, the proposed EEI determines the room’s energy usage performance by considering the number of occupants. The study was conducted at the Faculty of Electrical Engineering, Universiti Teknologi Malaysia (UTM) and the results show that the number of occupants significantly influences the energy usage performance of rooms in a university building.
Detection of occupancy status from internet connectivity for non-intrusive load monitoring Manjula Wickramathilaka; Md Pauzi Abdullah; Mohammad Yusri Hassan; Hayati Abdullah
Indonesian Journal of Electrical Engineering and Computer Science Vol 30, No 3: June 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v30.i3.pp1678-1688

Abstract

Non-intrusive load monitoring (NILM) methods are widely used for appliance level energy disaggregation in residential buildings. These methods mostly depend on electrical features, and they have not been much successful in applying for commercial buildings. However, recent research has indicated that the accuracy of existing NILM methods can be improved by associating with occupancy data. Therefore, in this paper a novel occupancy detection algorithm is proposed which can detect occupancy status of individuals using the connectivity of their information technology (IT) devices to the local area network of the building. The model is validated using data collected at a university building, with mean errors of 01:23 and 04:02 minutes for the detection of arrival and departure. The occupancy profiles developed by the proposed model can be used to disaggregate energy consumption in a commercial building to appliance and occupant level.
Co-Authors Azdiana Md Yusop Faridah Hussin Manjula Wickramathilaka Md Pauzi Abdullah Mohammad Yusri Hassan Muhammad Noorazlan Shah Zainudin Noor Ameera Zakaria Noor Asyikin Sulaiman Nur Najihah Abu Bakar Siti Maherah Hussin