Population, Economic Growth, and Energy Demand, 1985-2020

Gilland, Bernard

doi:10.2307/1973571

Cited by 30 publications

(10 citation statements)

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“…Based on its approaches, Bhattacharyya [4] classified energy forecasting models into two types: simple approach and sophisticated models. An example of the study using the simple approach is a study by Gilland [5]. In the study, the world energy demand is projected for the year 2000 and 2020 on the basis of assumptions of population growth, economic growth and proposed relation between elasticity of energy demand and growth of GDP per capita.…”

Section: Review Of Literaturementioning

confidence: 99%

Modelling Energy Consumption of the Republic of Serbia using Linear Regression and Artificial Neural Network Technique

2019

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The objectives of the study are twofold. First, we aim to examine the most influential socio-economic indicators to explain energy consumption in Republic of Serbia. The second objective is to develop models that are able to predict the future energy consumption in the Republic of Serbia. This could be the first important step towards proper energy management in the country. Several potential socio-economic indicators are selected to be the independent variables. Regression analysis is conducted to select the most relevant independent variables as well as building the multiple linear regression (MLR) models. In addition, an artificial neural networks (ANN) model is developed as a comparison. Finally, the energy demand is projected to the year 2022. It is found that both models show the declining trend with respect to the current level of energy consumption.

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Section: Review Of Literaturementioning

confidence: 99%

Modelling Energy Consumption of the Republic of Serbia using Linear Regression and Artificial Neural Network Technique

2019

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“…Thus, more realistic models based on mathematical expressions are needed. Except many energy demand forecasting models, other studies have been developed by many researches using various forms of mathematical formulations to find a relation between energy consumption and income [24,25,11,22,21]. The literature on the energy forecasting models and their variants is extensive and the reader is referred to the surveys by Elkhafif [20] and Suganthi and Samuel [59].…”

Section: Literature Reviewmentioning

confidence: 99%

Swarm intelligence approaches to estimate electricity energy demand in Turkey

Kıran

Özceylan

Gündüz

et al. 2012

Knowledge-Based Systems

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“…On the other hand, many models have been developed from many studies using various forms of mathematical formulations, which are related to energy development models (Ebohan, 1996;Say and Yucel, 2006). Gilland (1988) developed an energy demand projection of the world primary energy demand for the years 2000 and 2020. Ediger and Tatlidil (2002) proposed an approach that uses the analysis of cyclic patterns in historical curves to forecast the primary energy demand in Turkey.…”

Section: Literature Reviewmentioning

confidence: 99%

Estimating Transport Energy Demand Using Ant Colony Optimization

Başkan

Haldenbilen

Ceylan

2012

Energy Sources, Part B: Economics, Planning, and Policy

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This study proposes a heuristic algorithm based on ant colony optimization for estimating the transport energy demand (TED) of Turkey using gross domestic product, population, and vehicle-km. Three forms of the improved ant colony optimization transport energy demand estimation (IACOTEDE) models are used for improving estimating capabilities of TED models. Performance of IACOTEDE is compared with the Ministry of Energy and Natural Resources (MENR) projections. Sensitivity analysis is also carried out for testing the effects of the parameters. The quadratic form provided a better-fit solution to the observed data, and it underestimates Turkey's TED by about 28% less than the MENR projection in year 2025. Thus, it may be used with a highest correlation coefficient and considerably lower relative error according as the MENR projection in the testing period. It is also expected that this study will be helpful in developing highly applicable and productive planning for transport energy policies.

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Population, Economic Growth, and Energy Demand, 1985-2020

Cited by 30 publications

References 0 publications

Modelling Energy Consumption of the Republic of Serbia using Linear Regression and Artificial Neural Network Technique

Modelling Energy Consumption of the Republic of Serbia using Linear Regression and Artificial Neural Network Technique

Swarm intelligence approaches to estimate electricity energy demand in Turkey

Estimating Transport Energy Demand Using Ant Colony Optimization

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