Projecting Saudi sectoral electricity demand in 2030 using a computable general equilibrium model

Soummane, Salaheddine; Ghersi, Frédéric

doi:10.1016/j.esr.2021.100787

Cited by 27 publications

(8 citation statements)

References 21 publications

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“…A computable general equilibrium model (CGE) was developed by Soummane et al in [43] to estimate sectoral electricity demand in Saudi Arabia until 2030. The study expected total Saudi power demand to expand much slower over the next decade than previous trends.…”

Section: Discussionmentioning

confidence: 99%

Bayesian-Optimization-Based Long Short-Term Memory (LSTM) Super Learner Approach for Modeling Long-Term Electricity Consumption

Almuhaini,

Sultana

2023

Sustainability

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This study utilized different methods, namely classical multiple linear regression (MLR), statistical approach exponential smoothing (EXPS), and deep learning algorithm long short-term memory (LSTM) to forecast long-term electricity consumption in the Kingdom of Saudi Arabia. The originality of this research lies in (1) specifying exogenous variables that significantly affect electrical consumption; (2) utilizing the Bayesian optimization algorithm (BOA) to develop individual super learner BOA-LSTM models for forecasting the residential and total long-term electric energy consumption; (3) measuring forecasting performances of the proposed super learner models with classical and statistical models, viz. MLR and EXPS, by employing the broadly used evaluation measures regarding the computational efficiency, model accuracy, and generalizability; and finally (4) estimating forthcoming yearly electric energy consumption and validation. Population, gross domestic products, imports, and refined oil products significantly impact residential and total annual electricity consumption. The coefficient of determination (R2) for all the proposed models is greater than 0.93, representing an outstanding fitting of the models with historical data. Moreover, the developed BOA-LSTM models have the best performance with R2>0.99, enhancing the predicting accuracy (Mean Absolute Percentage Error (MAPE)) by 59.6% and 54.8% compared to the MLR and EXPS models, respectively, of total annual electricity consumption. This forecasting accuracy in residential electricity consumption for the BOA-LSTM model is improved by 62.7% and 68.9% compared to the MLR and EXPS models. This study achieved a higher accuracy and consistency of the proposed super learner model in long-term electricity forecasting, which can be utilized in energy strategy management to secure the sustainability of electric energy.

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Section: Discussionmentioning

confidence: 99%

Bayesian-Optimization-Based Long Short-Term Memory (LSTM) Super Learner Approach for Modeling Long-Term Electricity Consumption

Almuhaini,

Sultana

2023

Sustainability

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“…For example, the 2019 consumption level of electricity in the residential sector declined by 11 percent compared to 2015, which is equivalent to saving more than two million barrels of oil equivalent per year. According to (Soummane and Ghersi, 2022), for the period between 2019 and 2030 and under the current tariff, residential demand is expected to recover at an average annual growth rate of 1.1 percent. Environmentally, the level of CO 2 emissions started to reduce.…”

Section: Discussionmentioning

confidence: 99%

Electrical energy future of Saudi Arabia: Challenges and opportunities

Aldhubaib

2022

Front. Energy Res.

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The economy of Saudi Arabia is primarily oil-based, and the energy system is heavily dependent on fossil fuel sources. Electrical demand in Saudi Arabia has been overgrowing due to the high rates of both population and economy. The real problem is that most of this demand has been met by polluting sources, which in turn causes severe economic and environmental challenges. Despite the abundant amount of clean and freely available renewable sources, the practice of benefiting from these sources is minimal and modest. Therefore, the country has launched a promising vision, one pillar of which is diversifying energy sources and reducing dependence on oil. Since Saudi Arabia is located within the equatorial Sun Belt area, the country is a candidate for leading the World’s renewable energy industry, particularly solar energy. This paper discusses the current energy policy in Saudi Arabia, the associated main energy-related challenges and promising opportunities, and the efforts and actions taken by the country’s policymakers to address these challenges and to take advantage of available opportunities–especially those related to electrical energy–to assure continued future prosperity for subsequent generations.

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“…The energy demand in Saudi Arabia in 2018 was 299.2 terawatthours (TWh). This energy demand is comparable to countries with larger populations (e.g., Mexico had a population of 127.5 million in 2019, compared to Saudi Arabia's population of 34.2 million) [2]. The energy demands expanded quickly for decades under the backing of government incentives through fixed and low energy prices.…”

Section: Introductionmentioning

confidence: 96%

“…To achieve this objective, technologies and innovations are needed to address the energy crises, especially in developing nations. Since the early 1970s, the demand for energy in Saudi Arabia has increased rapidly due to human expansion, economic growth, and low energy prices [2]. The energy demand in Saudi Arabia in 2018 was 299.2 terawatthours (TWh).…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Building a Sustainable Energy Community: Design and Integrate Variable Renewable Energy Systems for Rural Communities

et al. 2022

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This study proposes a decentralized hybrid energy system consisting of solar photovoltaics (PV) and wind turbines (WT) connected with the local power grid for a small Najran, Saudi Arabia community. The goal is to provide the selected community with sustainable energy to cover a partial load of the residential buildings and the power requirements for irrigation. For this, a dynamic model was constructed to estimate the hourly energy demand for residential buildings consisting of 20 apartments with a total floor area of 4640 and the energy requirements for irrigation to supply a farm of 10,000 with water. Subsequently, HOMER software was used to optimize the proposed hybrid energy system. Even considering the hourly fluctuations of renewable energies, the artificial neural network (ANN) successfully estimated PV and wind energy. Based on the mathematical calculations, the final R-square values were 0.928 and 0.993 for PV and wind energy, respectively. According to the findings, the cost of energy (COE) for the optimized hybrid energy system is $0.1053/kWh with a renewable energy penetration of 65%. In addition, the proposed system will save 233 tons of greenhouse gases annually.

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Projecting Saudi sectoral electricity demand in 2030 using a computable general equilibrium model

Cited by 27 publications

References 21 publications

Bayesian-Optimization-Based Long Short-Term Memory (LSTM) Super Learner Approach for Modeling Long-Term Electricity Consumption

Bayesian-Optimization-Based Long Short-Term Memory (LSTM) Super Learner Approach for Modeling Long-Term Electricity Consumption

Electrical energy future of Saudi Arabia: Challenges and opportunities

Building a Sustainable Energy Community: Design and Integrate Variable Renewable Energy Systems for Rural Communities

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