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 self-organizing 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. © 2022, Institute of Advanced Engineering and Science. All rights reserved.
ABSTRACT
During the movement control order (MCO), most of the citizen stay at home. Thus, it increased electricity consumption tremendously. The uncontrolled consumption of electricity happened when a lack of awareness among residents to practice home energy efficiency. Due to that reason, this study presents a real case of the MCO electricity energy waste during the early MCO stage. In contrast, sustainable home energy management with 5Core procedures was introduced to overcome the issue. The method was applied to manage people's activities inside the house, where the best arrangement of the energy efficiency approach has been used accordingly. Through the proposed process, energy consumption has significantly reduced while avoiding the spike of the electricity bill during the post-MCO of Pandemic COVID-19. The sustainability of the post MCO for the energy consumption among residential could contribute to the low CO2 emission while securing the factor of the cost-effectiveness for the electricity bill. It is hoped that the proposed method will be the guideline for the consumers to manage the consumption of the electricity, where the combination of the human factor will be the advantages. © International Research Publication House.