Document Details

Document Type : Thesis 
Document Title :
LONG TERM ELECTRICITY CONSUMPTION FORECASTING IN SAUDI ARABIA
التنبؤ طويل المدى لاستهلاك الكهرباء في المملكة العربية السعودية
 
Subject : Faculty of Engineering 
Document Language : Arabic 
Abstract : This master thesis is about electricity demand forecasting in Saudi Arabia till the year 2025. The data on demand were collected from King Abdullah Petroleum Studies and Research Center, for the four regions in Saudi Arabia namely; western, central, eastern and southern regions. The data pattern for the regions were studied and all showed monthly seasonality with slight variations. After that three forecasting methods were applied starting with the time series decomposition method with a multiplicative model, The Box-Jenkins methodology (ARIMA) and Winter’s triple smoothing method to find the forecast of electricity demand for each region. Then, those forecasts were combined to obtain the total demand of electricity for Saudi Arabia. The forecast for the 96 months from Jan. 2018 until Dec. 2025 were obtained which showed that the demand will continue to grow for all regions with a peak at the summer season. An analysis of electricity demand factors was also studied using fishbone and regression analysis to identify the most important factors affecting electricity demand in Saudi Arabia, which were found to be: populations, number of subscribers, number of factories, CO2 emissions, and air temperature. Comparison of the forecasting errors measurements indicates that, in general , the time series decomposition method is the best model, however, a combined model was also generated to optimize accuracy, which shows that peak demand in August 2025 will be 63.30115 GW. 
Supervisor : Prof. Mustafa Alidrisi 
Thesis Type : Master Thesis 
Publishing Year : 1442 AH
2021 AD
 
Co-Supervisor : Dr. Abdulaziz Alkabaa 
Added Date : Wednesday, May 26, 2021 

Researchers

Researcher Name (Arabic)Researcher Name (English)Researcher TypeDr GradeEmail
فارس محمود رمزيRamzi, Fares ResearcherMaster 

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