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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Bulletin of Electrical Engineering and Informatics
Arfah Ahmad
Universiti Teknikal Malaysia Melaka
Computer Science & IT Electrical & Electronics Engineering Engineering

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A review of electricity consumer behavioural change under sustainable energy management scheme Mohamad Fani Sulaima; Nurul Fasihah Jumidey; Arfah Ahmad; Aida Fazliana Abdul Kadir; Mohamad Firdaus Sukri; Musthafah Mohd Tahir
Bulletin of Electrical Engineering and Informatics Vol 11, No 3: June 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v11i3.3800

Abstract

Many authorities launched their energy sustainability plan that involve the sustainable energy management scheme to improve energy efficiency. The sustainable energy management scheme consists of several measures to encourage energy efficiency in three primary energy consumers by pursuing implementation measures in the industrial, commercial, and residential sectors. Meanwhile, energy performance is quantifiable in energy efficiency and energy consumption become one of scheme measure aspects. In this review, the ASEAN Energy Management Scheme (AEMAS) was discussed as a regionally structured training and certification system for ASEAN Energy Managers. Besides that, Energy Management Gold Standard (EMGS) is AEMAS's first regional achievement certification for global excellence in energy management systems. Previous literatures exposed the key to energy efficiency goals is behavioural change, which means individual attitudes affect energy consumption.
Coronavirus disease 2019; pandemic; Data analysis; Energy demand; Neural network; Self-organizing mapping; Mohamad Fani Sulaima; Sharizad Saharani; Arfah Ahmad; Elia Erwani Hassan; Zul Hasrizal Bohari
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 11, No 4: December 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v11.i4.pp%p

Abstract

The world faces a significant impact from the coronavirus disease 2019 (Covid-19) pandemic, which also influences energy consumption. This study investigates the substantial connection of the classified data between power consumption, cooling degree days, average temperature, and covid-19 cases information using mathematical and neural network approaches regression analysis, and self-organizing maps. It is well established that various data mining methods have revamped the classification process of data analytics. Specifically, this study investigates the correlation between the collected variables using regression analysis and selecting the best-matching unit under the normalization method using self-organizing maps. The selforganizing maps become better when the datasets have variations; the result denotes that this method produced high mapping quality based on the map size and normalization method. Furthermore, the data crossing connection is indicated using the regression analysis method. Finally, the classified data results during the movement control order are validated in self-organizing maps to achieve the study objective. By performing these methods, this study established that the correlation between the energy demand towards cooling degree days, average temperature, and covid-19 cases is very weak. The verification has been made where the ‘logistic’ normalization method has produced the best classification result.
Co-Authors Aida Fazliana Abdul Kadir Elia Erwani Hassan Mohamad Fani Sulaima Mohamad Firdaus Sukri Musthafah Mohd Tahir Nurul Fasihah Jumidey Sharizad Saharani Zul Hasrizal Bohari