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

Boni Sena, Sheikh Ahmad Zaki, Hom Bahadur Rijal, Jorge Alfredo Ardila-Rey, Nelidya Md Yusoff, Fitri Yakub, Farah Liana and Mohamad Zaki Hassan
Additional contact information
Boni Sena: Malaysia-Japan International Institute of Technology, Universiti Teknologi Malaysia, Kuala Lumpur 54100, Malaysia
Sheikh Ahmad Zaki: Malaysia-Japan International Institute of Technology, Universiti Teknologi Malaysia, Kuala Lumpur 54100, Malaysia
Hom Bahadur Rijal: Faculty of Environmental Studies, Tokyo City University, Yokohama 224-8551, Japan
Jorge Alfredo Ardila-Rey: Department of Electrical Engineering, Universidad Técnica Federico Santa María, Santiago de Chile 8940000, Chile
Nelidya Md Yusoff: Razak Faculty of Technology and Informatics, Universiti Teknologi Malaysia, Kuala Lumpur 54100, Malaysia
Fitri Yakub: Malaysia-Japan International Institute of Technology, Universiti Teknologi Malaysia, Kuala Lumpur 54100, Malaysia
Farah Liana: Malaysia-Japan International Institute of Technology, Universiti Teknologi Malaysia, Kuala Lumpur 54100, Malaysia
Mohamad Zaki Hassan: Razak Faculty of Technology and Informatics, Universiti Teknologi Malaysia, Kuala Lumpur 54100, Malaysia

Sustainability, 2021, vol. 13, issue 23, 1-22

Abstract: Energy-saving strategies are required to address the increasing global CO 2 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.

Keywords: electrical energy consumption model; artificial neural network; socio-demographic; house characteristics; occupant behavior; appliance characteristic (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
https://www.mdpi.com/2071-1050/13/23/13258/pdf (application/pdf)
https://www.mdpi.com/2071-1050/13/23/13258/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jsusta:v:13:y:2021:i:23:p:13258-:d:691699

Access Statistics for this article

Sustainability is currently edited by Ms. Alexandra Wu

More articles in Sustainability from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().