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All Journal Indonesian Journal of Electrical Engineering and Computer Science
Md Pauzi Abdullah
Universiti Teknologi Malaysia (UTM)

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Smart home appliances scheduling considering user comfort level Hui Ming Hoe; Md Pauzi Abdullah
Indonesian Journal of Electrical Engineering and Computer Science Vol 20, No 2: November 2020
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v20.i2.pp593-601

Abstract

Smart home appliances scheduling, employing optimization optimization algorithms to reduce utility costs, is gaining traction under the introduction of time-of-use tariffs and the development of internet of things (IoT). The prior electricity cost reduction scheduling algorithms, however, causes substantial discomfort to users for restricting users from using the appliances at their desired times. To address the problem, a novel versatile systematic method is proposed by pricing the mismatch of proposed schedule with users’ usage preference pattern to quantify discomfort, coupled with comfort-cost weight factor. The method employing customizable user preference patterns, user-perceived pricing of mismatch and user-specified comfort-savings weightage, not only captures the complex dependence of comfort to individual preference, but the evolution with time by continuous user survey. The proposed method, formulated to be simple enough to be applied on an Excel spreadsheet, demonstrates substantial reduction of electricity cost and users’ discomfort simultaneously. Studies on the algorithm found it to be robust against of fluctuations of parameters, with optimization performance comparable to prior work. The work demonstrates that despite the complex nature of comfort to users’ behaviors and perception, simple pricing surveys can be used to accurately quantify, compare and optimize users’ comfort together with economic savings. 
Parameter selection in data-driven fault detection and diagnosis of the air conditioning system Noor Asyikin Sulaiman; Md Pauzi Abdullah; Hayati Abdullah; Muhammad Noorazlan Shah Zainudin; Azdiana Md Yusop; Siti Fatimah Sulaiman
Indonesian Journal of Electrical Engineering and Computer Science Vol 25, No 1: January 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v25.i1.pp59-67

Abstract

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.
Co-Authors Azdiana Md Yusop Hayati Abdullah Hui Ming Hoe Muhammad Noorazlan Shah Zainudin Noor Asyikin Sulaiman Siti Fatimah Sulaiman