Saudi Arabia's Development Potential: Application of an Islamic Growth Model and Saudi Arabia: Energy, Development Planning, and Industrialization

1982 ◽  
Vol 59 (1) ◽  
pp. 124-125
Author(s):  
A. K. Selby
Keyword(s):  
Saudi Arabia ◽  
Growth Model ◽  
Potential Application ◽  
Development Planning ◽  
Energy Development ◽  
Development Potential

scholarly journals Sustainable Energy Development in Saudi Arabia

2015 ◽  
Vol 7 (5) ◽  
pp. 5153-5170 ◽  
Author(s):  
Mounir Belloumi ◽  
Atef Alshehry
Keyword(s):  
Saudi Arabia ◽  
Sustainable Energy ◽  
Energy Development

Energy development potential: An analysis of Brazil

Energy Policy ◽  
2013 ◽  
Vol 59 ◽  
pp. 683-701 ◽  
Author(s):  
Fernando Salgueiro Perobelli ◽  
Caio Cézar Calheiros de Oliveira
Keyword(s):  
Energy Development ◽  
Development Potential

scholarly journals Energy Consumption Prediction in Vietnam with an Artificial Neural Network-Based Urban Growth Model

Energies ◽  
2020 ◽  
Vol 13 (17) ◽  
pp. 4282
Author(s):  
Hye-Yeong Lee ◽  
Kee Moon Jang ◽  
Youngchul Kim
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Growth Factors ◽  
Energy Consumption ◽  
Growth Model ◽  
Urban Growth ◽  
Energy Demand ◽  
Development Planning ◽  
Urban Growth Model ◽  
Artificial Neural

In developing countries, energy planning is important in the development planning due to high rates of economic growth and energy demand. However, existing approaches of energy prediction, using gross domestic product, hardly demonstrate how much energy specific regions or cities may need in the future. Thus, this study seeks to predict the amount of energy demand by considering urban growth as a crucial factor for investigating where and how much energy is needed. An artificial neural network is used to forecast energy patterns in Vietnam, which is a quickly developing country and seeks to have an adequate energy supply. Urban growth factors, population, and night-time light intensity are collected as an indicator of energy use. The proposed urban-growth model is trained with data of the years 1995, 2000, 2005, and 2010, and predicts the light distribution in 2015. We validated the model by comparing the predicted result with actual light data to display the spatial characteristics of energy-consumption patterns in Vietnam. In particular, the model with urban growth factors estimated energy consumption more closely to the actual consumption. This spatial prediction in Vietnam is expected to help plan geo-locational energy demands.

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