Development of an electrical energy consumption model for Malaysian households, based on techno-socioeconomic determinant factors

• Main Authors: Sena, Boni, Zaki, Sheikh Ahmad, Rijal, Hom Bahadur, Ardila-Rey, Jorge Alfredo, Md. Yusoff, Nelidya, Yakub, Fitri, Liana, Farah, Hassan, Mohamad Zaki
• Format: Article
• Language: English
• Published: MDPI 2021
• Subjects: TK Electrical engineering. Electronics Nuclear engineering
• Online Access: http://eprints.utm.my/id/eprint/95745/1/BoniSena2021_DevelopmentofanElectricalEnergyConsumption.pdf; http://eprints.utm.my/id/eprint/95745/; http://dx.doi.org/10.3390/su132313258

Energy-saving strategies are required to address the increasing global CO2 and electrical energy consumption problems. Therefore, the determinant factors of electrical energy consumption consist of socio-demographic changes, occupant behavior, house and appliance characteristics, or so-called techno-socioeconomic factors, which all need to be assessed. Statistics models, such as the artificial neural network (ANN), can investigate the relationship among those factors. However, the previous ANN model only used limited factors and was conducted in the developed countries of subtropical regions with different determinant factors than those in the developing countries of tropical regions. Furthermore, the previous studies did not investigate the various impacts of techno-socioeconomic factors concerning the performance of the ANN model in estimating monthly electrical energy consumption. The current study develops a model with a more-in depth architecture by examining the effect of additional factors such as socio-demographics, house characteristics, occupant behavior, and appliance characteristics that have not been investigated concerning the model performance. Thus, a questionnaire survey was conducted from November 2017 to January 2018 with 214 university students. The best combination factors in explaining the monthly electrical energy consumption were developed from occupant behavior, with 81% of the variance and a mean absolute percentage error (MAPE) of 20.6%, which can be classified as a reasonably accurate model. The current study’s findings could be used as additional information for occupants or for companies who want to install photovoltaic or wind energy systems.