Electrical energy consumption in the eastern province of Saudi Arabia is modeled as a function of weather data, global solar radiation, population, and gross domestic product per capita. Five years of data have been used to develop the energy consumption model. Variation selection in the regression model is carried out by using the general stepping-regression technique. Model adequacy is determined from a residual analysis technique. Model validation aims to determine if the model will function successfully in its intended operating field. In this regard, new energy consumption data for a sixth year are collected, and the results predicted by the regression model are compared with the new data set. Finally, the sensitivity of the model is e...
Deriving some models to estimate the electrical demand for future for the Kingdom of Bahrain is carr...
Energy data are often reported on an annual basis. To address the climate and health impacts of gree...
This study aims to develop statistical and machine learning methodologies for forecasting yearly ele...
An econometric model is developed to forecast electricity consumption and study the impact of ambien...
This thesis addresses the long-range generation planning problem in Saudi Arabia up to the year 2000...
Culture influences the way that people act and behave in all societies. In Saudi Arabia, culture and...
Residential buildings are vital in the energy scenario of Saudi Arabia as they account for 52% of th...
Energy efficiency and conservation are important areas of consideration in many developed and develo...
The problem of load forecasting represents an integral part in the power system planning process- Ma...
In Saudi Arabia, housing projects account for almost half of the total electricity consumed by the c...
The paper attempts to establish the determinants of energy use in Saudi Arabia over the period (197...
The paper evaluates domestic energy consumption patterns in a hot and arid climate, using a multiple...
This study utilized monthly mean daily values of global solar-radiation and sunshine duration at 41 ...
Historically, the combination of generous subsidies along with extreme climate has led to unsustaina...
This submission contains the data used to calibrate the residential energy use model to Saudi househ...
Deriving some models to estimate the electrical demand for future for the Kingdom of Bahrain is carr...
Energy data are often reported on an annual basis. To address the climate and health impacts of gree...
This study aims to develop statistical and machine learning methodologies for forecasting yearly ele...
An econometric model is developed to forecast electricity consumption and study the impact of ambien...
This thesis addresses the long-range generation planning problem in Saudi Arabia up to the year 2000...
Culture influences the way that people act and behave in all societies. In Saudi Arabia, culture and...
Residential buildings are vital in the energy scenario of Saudi Arabia as they account for 52% of th...
Energy efficiency and conservation are important areas of consideration in many developed and develo...
The problem of load forecasting represents an integral part in the power system planning process- Ma...
In Saudi Arabia, housing projects account for almost half of the total electricity consumed by the c...
The paper attempts to establish the determinants of energy use in Saudi Arabia over the period (197...
The paper evaluates domestic energy consumption patterns in a hot and arid climate, using a multiple...
This study utilized monthly mean daily values of global solar-radiation and sunshine duration at 41 ...
Historically, the combination of generous subsidies along with extreme climate has led to unsustaina...
This submission contains the data used to calibrate the residential energy use model to Saudi househ...
Deriving some models to estimate the electrical demand for future for the Kingdom of Bahrain is carr...
Energy data are often reported on an annual basis. To address the climate and health impacts of gree...
This study aims to develop statistical and machine learning methodologies for forecasting yearly ele...