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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. [ABSTRACT FROM AUTHOR]

Rapid population growth has led to significant demand for residential buildings around the world. Consequently, there is a growing energy demand associated with increased greenhouse gas (GHG) emissions. The residential building energy demand in arid countries such as Saudi Arabia is supplied with fossil fuel. The existing consumption pattern of fossil fuels in Saudi Arabia is less sustainable due to the depletion of fossil fuel resources and resulting environmental impacts. Buildings built in hot and arid climatic conditions demand high energy for creating habitable indoor environments. Enormous energy is required to maintain a cool temperature in hot regions. Moreover, climate change may have different impacts on hot climatic regions and affect building energy use differently. This means that different building interventions may be required to improve the performance of building energy performance in these geographical regions, thereby reducing the emissions of GHGs. In this study, this framework has been applied to Saudi Arabia, a hot and arid country. This research proposes a community–government partnership framework for developing low-carbon energy in residential buildings. This study focuses on both the operational energy demand and a cost-benefit analysis of energy use in the selected geographical regions for the next 30 years (i.e., 2050). The proposed framework primarily consists of four stages: (1) data collection on energy use (2020 to 2050); (2) setting a GHG emissions reduction target; (3) a building intervention approach by the community by considering cost, energy, and GHG emissions using the Technique for Order of Performance by Similarity to the Ideal Solution (TOPSIS) to select the best combinations in each geographical region conducting 180 simulations; and (4) a clean energy approach by the government using grey relational analysis (GRA) to select the best clean energy system on the grid. The clean energy approach selected six different renewable power generation systems (i.e., PV array, wind turbine, hybrid system) with two storage systems (i.e., battery bank and a combination of electrolyte, fuel cell, and hydrogen tank storage). This approach is designed to identify the best clean energy systems in five geographical regions with thirty scenario analyses to define renewable energy-economy benefits. This framework informs through many engineering tools such as residential building energy analysis, renewable energy analysis, multi-criteria decision analysis (MCDA) techniques, and cost-benefit analysis. Integration between these engineering tools with the set of energy policies and public initiatives is designed to achieve further directives in the effort to reach greater efficiency while downsizing residential energy demands. The results of this paper propose that a certain level of cooperation is required between the community and the government in terms of financial investments and the best combinations of retrofits and clean energy measures. Thus, retrofits and clean energy measures can help save carbon emissions (enhancing the energy performance of buildings) and decrease associated GHG emissions, which can help policy makers to achieve low-carbon emission communities. [ABSTRACT FROM AUTHOR]