Neurogenetic modeling of energy demand in the United Arab Emirates, Saudi Arabia, and Qatar

Rahman, Syed Masiur; Khondaker, A.N.; Hossain, Mohammad Imtiaz; Shafiullah, Md; Hasan, Arif

doi:10.1002/ep.12558

Cited by 26 publications

(7 citation statements)

References 48 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In the years 2010-2020, Qatar had the highest growth rate of energy demand among the countries of the Persian Gulf Cooperation Council [36]. It is the largest exporter of liquefied natural gas in the world and supplies its domestic energy using natural gas (76%) and crude oil (23%).…”

Section: Yesmentioning

confidence: 99%

Investigating the Cost-Effectiveness of Solar Electricity Compared to Grid Electricity in the Capitals of Middle Eastern Countries: A Residential Scale Case Study

Shahgholian

Taheri

Jahangiri

2023

International Journal of Photoenergy

View full text Add to dashboard Cite

Despite of being rich in fossil fuels, the Middle East is currently the main energy consumer and is projected to have the highest growth in energy demand in the world. Due to its great potential in the Middle East, solar energy can play an important role in the plans of energy decision-makers in the region. According to the studies done so far, no study has been done to show the potential benefit of using home-scale solar systems in the Middle East. Therefore, in this work for the first time, the potential of solar electricity production in the capitals of Middle Eastern countries has been studied using HOMER V2.81 software. The investigations are technical, economic, energy, and environmental, and the studied solar system is connected to the national electricity grid. The results showed that in Nicosia, due to the sale of electricity to the grid, the levelized cost of electricity (LCOE) is equal to -0.759 $, which is the lowest price for produced electricity and leads to a return on investment time of 5.69 years for this system. The solar fraction for the Nicosia station is 92%, which prevents the emission of more than 8 tons of CO2 pollutants during the year. The highest value of LCOE with the amount of $0.25 is related to Sana’a, whose investment return time, solar fraction, and annual CO2 emission prevention amount are 14.1 years, 53%, and 1162 kg, respectively. Ranking analysis was done on the results of 5 outputs of the HOMER software as well as 3 other influential parameters using 4 multicriteria decision-making (MCDM) methods. TOPSIS, GRA, WSM, and AHP methods were used, and the final ranking of each station was considered the average of the 4 methods. According to the results, Cyprus and Kuwait stations were the best and worst, respectively.

show abstract

Section: Yesmentioning

confidence: 99%

Investigating the Cost-Effectiveness of Solar Electricity Compared to Grid Electricity in the Capitals of Middle Eastern Countries: A Residential Scale Case Study

Shahgholian

Taheri

Jahangiri

2023

International Journal of Photoenergy

View full text Add to dashboard Cite

show abstract

“…For trend analysis of each indicator, this study adopts the double exponential smoothing technique because it considers both the mean and the trend component, unlike the single exponential smoothing technique. Moreover, this is a widely used technique for temporal analysis, and many researchers have used it [45,46].…”

Section: Temporal Analysismentioning

confidence: 99%

A Critical, Temporal Analysis of Saudi Arabia’s Initiatives for Greenhouse Gas Emissions Reduction in the Energy Sector

et al. 2022

View full text Add to dashboard Cite

The per capita greenhouse gas (GHG) emissions of Saudi Arabia were more than three times the global average emissions in 2019. The energy sector is the most dominant GHG-emitting sector in the country; its energy consumption has increased over five times in the last four decades, from over 2000 quadrillion joules in 1981 to around 11,000 quadrillion joules in 2019, while the share of renewable energy in 2019 was only 0.1%. To reduce GHG emissions, the Saudi Arabian government has undertaken initiatives for improving energy efficiency and increasing the production of renewable energies in the country. However, there are few investigative studies into the effectiveness of these initiatives in improving energy efficiency and reducing greenhouse gas emissions. This study provides an overview of the various energy efficiency and renewable energy initiatives undertaken in Saudi Arabia. Then, it evaluates the effectiveness of energy-related policies and initiatives using an indicator-based approach. In addition, this study performs temporal and econometrics analyses to understand the trends and the causal relationships among various drivers of energy sector emissions. Energy intensity and efficiency have improved moderately in recent years. This study will support policymakers in identifying significant policy gaps in reducing the emissions from the energy sector; furthermore, this study will provide a reference for tracking the progress of their policy initiatives. In addition, the methodology used in this study could be applied in other studies to evaluate various climate change policies and their progress.

show abstract

“…Hence, these tools have already been implemented to model real-life problems related to classification, clustering, and regression [35][36][37]. For instance, the techniques were employed for power quality disturbances classification [38], power system faults detection and classification [39], water quality parameter modeling [40], and many more classification, clustering, and regression problems [41][42][43] with promising results. As this study aims to predict the mode choice behavior of the school-goers, it employs three different MLT: SVM, MLP-NN, and ELM.…”

Section: Data Processingmentioning

confidence: 99%

Travel-To-School Mode Choice Modelling Employing Artificial Intelligence Techniques: A Comparative Study

et al. 2019

View full text Add to dashboard Cite

Many techniques including logistic regression and artificial intelligence have been employed to explain school-goers mode choice behavior. This paper aims to compare the effectiveness, robustness, and convergence of three different machine learning tools (MLT), namely the extreme learning machine (ELM), support vector machine (SVM), and multi-layer perceptron neural network (MLP-NN) to predict school-goers mode choice behavior in Al-Khobar and Dhahran cities of the Kingdom of Saudi Arabia (KSA). It uses the students’ information, including the school grade, the distance between home and school, travel time, family income and size, number of students in the family and education level of parents as input variables to the MLT. However, their outputs were binary, that is, either to choose the passenger car or walking to the school. The study examined a promising performance of the ELM and MLP-NN suggesting their significance as alternatives for school-goers mode choice modeling. The performances of the SVM was satisfactory but not to the same level of significance in comparison with the other two. Moreover, the SVM technique is computationally more expensive over the ELM and MLP-NN. Further, this research develops a majority voting ensemble method based on the outputs of the employed MLT to enhance the overall prediction performance. The presented results confirm the efficacy and superiority of the ensemble method over the others. The study results are likely to guide the transport engineers, planners, and decision-makers by providing them with a reliable way to model and predict the traffic demand for transport infrastructures on the basis of the prevailing mode choice behavior.

show abstract

Neurogenetic modeling of energy demand in the United Arab Emirates, Saudi Arabia, and Qatar

Cited by 26 publications

References 48 publications

Investigating the Cost-Effectiveness of Solar Electricity Compared to Grid Electricity in the Capitals of Middle Eastern Countries: A Residential Scale Case Study

Investigating the Cost-Effectiveness of Solar Electricity Compared to Grid Electricity in the Capitals of Middle Eastern Countries: A Residential Scale Case Study

A Critical, Temporal Analysis of Saudi Arabia’s Initiatives for Greenhouse Gas Emissions Reduction in the Energy Sector

Travel-To-School Mode Choice Modelling Employing Artificial Intelligence Techniques: A Comparative Study

Contact Info

Product

Resources

About