Impacts of Tariffs on Energy Conscious Behavior with Respect to Household Attributes in Saudi Arabia

Kh Md Nahiduzzaman (), Abdullatif Said Abdallah, Arash Moradzadeh, Amin Mohammadpour Shotorbani, Kasun Hewage and Rehan Sadiq
Additional contact information
Kh Md Nahiduzzaman: Life Cycle Management Laboratory, School of Engineering, University of British Columbia, 1137 Alumni Avenue, Kelowna, BC V1V 1V7, Canada
Abdullatif Said Abdallah: Faculty of Architecture, Building and Planning, University of Melbourne, Melbourne, VIC 3053, Australia
Arash Moradzadeh: Faculty of Electrical and Computer Engineering, University of Tabriz, Tabriz 5166616471, Iran
Amin Mohammadpour Shotorbani: Life Cycle Management Laboratory, School of Engineering, University of British Columbia, 1137 Alumni Avenue, Kelowna, BC V1V 1V7, Canada
Kasun Hewage: Life Cycle Management Laboratory, School of Engineering, University of British Columbia, 1137 Alumni Avenue, Kelowna, BC V1V 1V7, Canada
Rehan Sadiq: Life Cycle Management Laboratory, School of Engineering, University of British Columbia, 1137 Alumni Avenue, Kelowna, BC V1V 1V7, Canada

Energies, 2023, vol. 16, issue 3, 1-24

Abstract: Historically, the combination of generous subsidies along with extreme climate has led to unsustainable domestic electricity consumption in Saudi Arabia. The residential sector constitutes a significant portion of this consumption. Amid the economic challenges, the country enforced a new electricity tariff for residential consumers in 2018. This study thus leverages change in 2018–2020 by collecting and analyzing the electricity consumption data of 73 households in the Eastern Province of Saudi Arabia. The energy consumption is modeled based on the households’ attributes (e.g., dwelling type, ownership, number of residents, rooms, ventilation type, etc.) and applied tariffs using a machine learning technique. The extreme learning machine (ELM) is employed in solving the overfitting problem due to low-volume data. The correlation matrix is also constructed to determine the relationship between the household attributes. The ELM model developed in this study extracts the correlation between the input variables in determining energy consumption and also predicts the energy consumption related to low consumption data. The findings indicated that the electricity consumption between the pre-revised tariff year and the revised tariff enforcement year saw a reduction which was consistent in the subsequent years. This was also validated by the paired sample t -test, which showed a significant decrease in electricity consumption for the study period. The analysis also revealed that several household attributes had a relatively high impact on the reduction in the electricity consumption level following the revised tariffs, whereas the majority of the attributes had a moderate impact. In addition to these key findings, the demonstrated pathway adopted in this study is itself a methodological contribution that provides critical information about the sensitivity of the impacts of tariffs on energy consumption with respect to different household attributes. Economic factors being the critical stress need to be blended with existing energy consciousness for positive changes in favor of energy-saving behavior of the household members. The study does not attempt to represent the population of concern, but demonstrates a methodology that would help unleash inherent energy consciousness in favor of sustainable and energy-efficient behavior.

Keywords: energy consumption; energy conscious behavior; extreme learning machine; electricity tariff (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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