Determinant Factors of Electricity Consumption for a Malaysian Household Based on a Field Survey

Abstract

Electricity-saving strategies are an essential solution to overcoming increasing global CO2 emission and electricity consumption problems; therefore, the determinant factors of electricity consumption in households need to be assessed. Most previous studies were conducted in developed countries of subtropical regions that had different household characteristic factors from those in developing countries of tropical regions. A field survey was conducted on electricity consumption for Malaysian households to investigate the factors affecting electricity consumption that focused on technology perspective (building and appliance characteristics) and socio-economic perspective (socio-demographics and occupant behaviour). To analyse the determinant factors of electricity consumption, direct and indirect questionnaire surveys were conducted from November 2017 to January 2018 among 214 university students. Direct questionnaire surveys were performed in order to obtain general information that is easily answered by respondents. On the other hand, some questions such as electricity consumption and detailed information of appliances must be confirmed by the respondents’ parents or other household members through an indirect questionnaire survey. The results from multiple linear regression analyses of the survey responses showed that appliance characteristic factors were the main variables influencing electricity consumption and house characteristics were the least significant. Specifically, air conditioners, fluorescent lamps, and flat-screen TVs emerged as appliances with the most significant effect on electricity consumption. Occupant behaviour factors had a more significant influence than socio-demographic factors. The findings in this study can be used by policymakers to develop electricity-saving strategies in Malaysia.

Development of an Electrical Energy Consumption Model for Malaysian Households, Based on Techno-Socioeconomic Determinant Factors

“…The impact of multicollinearity and non-linear relationships among the determinant factors could reduce the model performance as reported by Kim et al [37]. Furthermore, the current model removed factors that had low significance and multicollinearity, as explained by Sena et al [27]. However, the current model had fewer data training and testing sets than that of Kim et al [37].…”

“…Data on techno-socioeconomic factors, which consists of socio-demographics, house characteristics, occupant behavior and appliance characteristics as acquired from the questionnaire, would be used as an input of the ANN model. A detailed explanation of those factors has been provided by Sena et al [27]. Weight function (w) and bias function (θ) would be randomly determined while developing the ANN model.…”

“…The verified and completed questionnaires from direct and indirect questionnaire surveys will be analyzed further. Details of the process and the results of the questionnaire survey were provided by Sena et al [27]. Summary of questionnaire survey was provided in Appendix A.…”

“…It uses genuine real-time and categorical data with a normalization process, which was better than that of the current study, as shown in Table 5. Nevertheless, the current study used correlation analysis to separate the datasets that had low and insignificant correlations from the analysis, as explained by Sena et al [27], while Lee et al [17] did not use the same method. The current study showed better performance than that of Lee et al [17] by explaining 49% and 40% of the observed electrical energy consumption variance, respectively.…”

“…The current study showed better performance than Kim et al [37] by explaining 11% and 70% of the variance, as shown in Table 8. The current model utilized correlation analysis to assess the linear relationship among the determinant factors of monthly electrical energy consumption and multiple linear regression, to determine the best combinations of those factors, as explained by Sena et al [27]. In contrast, Kim et al [37] utilized multiple linear regression to assess the determinant factors without correlation analysis.…”