Electricity Demand Forecast in Saudi Arabia

Ouda, Mohamed; El-Nakla, Samir; Yahya, C.B.; Ouda, K. M. Omar

doi:10.1109/picece.2019.8747196

Cited by 9 publications

(7 citation statements)

References 10 publications

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“…Over the past 40 years, Saudi Arabia has undergone significant development. This has led to a substantial surge in the demand for electricity [24]. Notably, the demand for electricity in the KSA has risen from 53.8 GW in 2013 to 64.16 GW in 2021.…”

Section: Saudi Arabia's Current Electricity Statusmentioning

confidence: 99%

Transforming Saudi Arabia’s Energy Landscape towards a Sustainable Future: Progress of Solar Photovoltaic Energy Deployment

Ali

2023

Sustainability

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The Kingdom of Saudi Arabia’s electricity sector has undergone several distinct phases, and the country’s commitment to renewable energy development has resulted in a modern phase that includes the deployment of renewable energy power plants since 2010. Due to Saudi Arabia’s diverse topographical position, the exploration of renewable energy technologies is of interest, particularly solar energy, and its progress in renewable energy development could serve as a model for other countries seeking to transition to clean energy. This article explores the progress of solar photovoltaic (PV) energy deployment in Saudi Arabia, with a focus on the policies and regulations that have facilitated its growth. The article provides an overview of the energy landscape in Saudi Arabia and investigates the progress of solar PV deployment in Saudi Arabia, analyzing growth trends, capacity additions, and the role of policies and regulations in supporting the sector. The focus on renewable energy development in Saudi Arabia, particularly solar PV technology, could have far-reaching implications globally as the world seeks to transition to cleaner sources of energy. This research article highlights the importance of a comprehensive renewable energy policy for transforming the country’s energy landscape towards a sustainable future.

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Section: Saudi Arabia's Current Electricity Statusmentioning

confidence: 99%

Transforming Saudi Arabia’s Energy Landscape towards a Sustainable Future: Progress of Solar Photovoltaic Energy Deployment

Ali

2023

Sustainability

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“…Elnakla et al compared the electricity demand per capita in Saudi Arabia with the United Arab Emirates (UAE) and Australia [31]. The results showed that Saudi Arabia consumes less electric energy than the UAE and higher electricity than Australia.…”

Section: Literature Reviewmentioning

confidence: 99%

Bayesian Optimization Algorithm-Based Statistical and Machine Learning Approaches for Forecasting Short-Term Electricity Demand

et al. 2022

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This article focuses on developing both statistical and machine learning approaches for forecasting hourly electricity demand in Ontario. The novelties of this study include (i) identifying essential factors that have a significant effect on electricity consumption, (ii) the execution of a Bayesian optimization algorithm (BOA) to optimize the model hyperparameters, (iii) hybridizing the BOA with the seasonal autoregressive integrated moving average with exogenous inputs (SARIMAX) and nonlinear autoregressive networks with exogenous input (NARX) for modeling separately short-term electricity demand for the first time, (iv) comparing the model’s performance using several performance indicators and computing efficiency, and (v) validation of the model performance using unseen data. Six features (viz., snow depth, cloud cover, precipitation, temperature, irradiance toa, and irradiance surface) were found to be significant. The Mean Absolute Percentage Error (MAPE) of five consecutive weekdays for all seasons in the hybrid BOA-NARX is obtained at about 3%, while a remarkable variation is observed in the hybrid BOA-SARIMAX. BOA-NARX provides an overall steady Relative Error (RE) in all seasons (1~6.56%), while BOA-SARIMAX provides unstable results (Fall: 0.73~2.98%; Summer: 8.41~14.44%). The coefficient of determination (R2) values for both models are >0.96. Overall results indicate that both models perform well; however, the hybrid BOA-NARX reveals a stable ability to handle the day-ahead electricity load forecasts.

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“…Ouda et al in [18], introduced three electricity demand scenarios which were: Optimistic, Pessimistic, and Moderate, respectively. The estimations were calculated from the year 2014 up to the year 2040, based on past yearly total demands and population growth that started from 2006 and spanned 8 years.…”

Section: Saudi Arabia Electricity Consumption Forecastingmentioning

confidence: 99%

Machine Learning Empowered Electricity Consumption Prediction

Metrik¹,

Musleh²

2022

Computers, Materials &Amp; Continua

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Electricity, being the most efficient secondary energy, contributes for a larger proportion of overall energy usage. Due to a lack of storage for energy resources, over supply will result in energy dissipation and substantial investment waste. Accurate electricity consumption prediction is vital because it allows for the preparation of potential power generation systems to satisfy the growing demands for electrical energy as well as: smart distributed grids, assessing the degree of socioeconomic growth, distributed system design, tariff plans, demand-side management, power generation planning, and providing electricity supply stability by balancing the amount of electricity produced and consumed. This paper proposes a medium-term prediction model that can predict electricity consumption for a given location in Saudi Arabia. Hence, this study implemented a standalone Artificial Neural Network (ANN) model and bagging ensemble for predicting total monthly electricity consumption in 18 locations across Saudi Arabia. The dataset used in this research is gathered exclusively from the Saudi Electric Company. The pre-processing phase included normalizing the data using min-max method and mapping the cyclical attribute to its sine and cosine facets. The number of neurons and learning rate of the standalone model were optimized using hyperparameter tuning. Finally, the standalone model was tested against the bagging ensemble using the optimized ANN. The bagging ensemble with an optimized ANN as the chosen classifier outperformed the standalone ANN model. The results for the proposed model produced 0.9116 Correlation Coefficient (CC), 0.2836 Mean Absolute Percentage Error (MAPE), 0.4578, Root Mean Squared Percentage Error (RMSPE), 0.0298 MAE, and 0.069 Root Mean Squared Error (RMSE), respectively.

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Electricity Demand Forecast in Saudi Arabia

Cited by 9 publications

References 10 publications

Transforming Saudi Arabia’s Energy Landscape towards a Sustainable Future: Progress of Solar Photovoltaic Energy Deployment

Transforming Saudi Arabia’s Energy Landscape towards a Sustainable Future: Progress of Solar Photovoltaic Energy Deployment

Bayesian Optimization Algorithm-Based Statistical and Machine Learning Approaches for Forecasting Short-Term Electricity Demand

Machine Learning Empowered Electricity Consumption Prediction

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