Search

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]

Simultaneous optimization of energy and water quality in real-time large-sized water distribution systems is a daunting task for water suppliers. The complexity of energy optimization increases with a large number of pipes, scheduling of several pumps, and adjustments of tanks' water levels. Most of the simultaneous energy and water quality optimization approaches evaluate small (or hypothetical) networks or compromise water quality. In the proposed staged approach, Stage 1 uses a risk-based approach to optimally locate the chlorine boosters in a large distribution system based on residual chlorine failures and the associated consequences in different land uses of the service area. Integrating EPANET and CPLEX software, Stage 2 uses mixed integer goal programming for optimizing the day-ahead pump scheduling. The objective function minimizes the pumping energy cost as well as the undesirable deviations from goal constraints, such as expected water demand. Stage 3 evaluates the combined hydraulics and water quality performances at the network level. The implementation of the proposed approach on a real-time large-sized network of Al-Khobar City in Saudi Arabia, with 44 groundwater wells, 12 reservoirs, 2 storage tanks, 191 mains, 141 junctions, and 17 pumps, illustrated the practicality of the framework. Simulating the network with an optimal pumping schedule and chlorine boosters' locations shows a 40% improvement in water quality performance, desired hydraulics performance with optimal pump scheduling, and an average 20% energy cost reduction compared to the normal (unoptimized) base case scenario. [ABSTRACT FROM AUTHOR]

The objective of this study is to examine the reuse of wastewater for beneficial purposes. To accomplish this objective, the efficiency of slow sand filters in removing total coliforms (TC) was studied using a probabilistic method. Three pilot scale slow sand filters were constructed at Alkhobar wastewater treatment plant, Dhahran, Saudi Arabia. The removal efficiency of filters was estimated under different operating control parameters, which included filtration rate (q), sand bed depth (d) and sand grain size (c). The Type III extreme value distribution best fitted the removal efficiency data. A multiple linear regression analysis was performed to develop a relationship for mean removal efficiency as a function of control parameters. The predicted mean response and experimental results of previous studies were compared to validate the empirical regression model. The control parameters and influent concentrations of total coliform were used in Monte Carlo (MC) simulations for calculating the reliability index (β). The reliability index and corresponding risk were calculated for lognormally distributed safety margins (SM). An effluent standard of 100 total coliform/100 mL was defined as capacity of the filter to ascertain the risks of exceedence, which was approximately less than 50 for 95% of the time. Pre and/or post disinfection would be necessary to meet the stipulated effluent standards for unrestricted agriculture use. [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]

Many medium-sized middle-eastern cities like Khobar in Saudi Arabia have become a popular destination for predominantly low-to semi-skilled expatriate workers from several South and South East Asian countries. The social spatialization of these working class people has been manifested in compartmentalized public spaces as specific ethnic oriented "third places", such as Filipino markets, Bangladeshi bazaar, and Pakistani neighborhood. Yet, there is no research scholarship available on the nature and type of such third places and their importance and influences on plan making process in the middle-eastern context. Certain (re-)defining themes, notably expressing and celebrating everyday culture, feeling of home being away from home, sense of places, and center places for socialization and integration emerge to brand these places as we explored empirical evidence with grounded theory centered socio-cultural lenses. Additionally, exploring spatio-temporal permeability of these third places using Space Syntax accessibility model helped us to draw critical insights about how such lessons and practices can challenge the contemporary city planning and design practices. Critical planning interventions like collaborative governance of third places as proposed here can be adopted in making cities like Khobar cohesive. • Spatialized third places are a contemporary landscape in the middle-eastern cities, including Khobar, Saudi Arabia. • Yet, there is no scholarship available on the spatial and socio-cultural features of such ethnic third places. • Formal plans don't tend to acknowledge such ethnic enclaves, neither take them into account for plan making exercise. • Third places are evaluated with grounded theory and their spatio-temporal permeability is assessed using space-syntax. • Collaborative governance, inclusive and adaptive planning approaches are needed to account third places in the formal plan. [ABSTRACT FROM AUTHOR]