Electricity consumption and economic growth, the case of Lebanon

Abosedra, Salah; Dah, Abdallah; Ghosh, Sajal

doi:10.1016/j.apenergy.2008.06.011

Cited by 156 publications

(51 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Specifically for residential demand, variables representing micro-behaviour such as household size, or number of appliances have also been used in the literature (Wilson, 1971;Westley, 1984) along with variance in the weather conditions (Zachariadis and Pashourtidou, 2007;Diabi, 1998;Hondroyiannis, 2004;Donatos and Megos, 1991;Abosedra et al, 2009). For the South African case, except for Ziramba (2008) that looked at the residential demand but in a macro-approach, Louw et al (2008) examined the determinants of electricity demand for newly electrified low-income African households.…”

Section: South African Department Of Energy (Doe)mentioning

confidence: 99%

On the causality and determinants of energy and electricity demand in South Africa: A review

Inglesi‐Lotz

Pouris

2016

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

View full text Add to dashboard Cite

The purpose of this paper is to review, summarize, and critically assess the academic studies that have dealt with either the causal relationship between energy consumption and growth or the determinants of energy demand in South Africa from 2007 and outline recent forecasts for electricity demand. The results of this review aim to identify gaps in the existing research. From a policy point of view, the findings of this effort have the potential to inform the relevant stakeholders to make appropriate interventions to improve the status quo of the energy sector. The results have indicated that studies examining the causality direction between energy (electricity) consumption and economic growth have failed to reach a consensus. The main differences identified were the time periods examined, the econometric approaches, and the variables included in the estimations. Another potential reason for the results is the availabilityor lack thereof-of data specific for the country. On the other side, the studies looking at the factor affecting energy (electricity) demand have agreed that economic growth or income or output are considered significant factors. The role of prices was debatable among different studies. This has become more apparent when reviewing the few forecasting efforts in the country that resulted in conflicting results.

show abstract

Section: South African Department Of Energy (Doe)mentioning

confidence: 99%

On the causality and determinants of energy and electricity demand in South Africa: A review

Inglesi‐Lotz

Pouris

2016

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

View full text Add to dashboard Cite

show abstract

“…Electricity consumption is a key component of economic growthand is directly or indirectly a complement to labor and capital as a factor of production (Costantini and Martini, 2010). Various studies have revealed the diverse impact of electricity consumption on economic growth (Yuan et al 2007, Chen et al 2007, Yuan et al 2008, Narayan and Prasad 2008, Abosedra et al 2009, Ahmed and Azam 2016, Streimikiene and Kasperowicz 2016. For example, some studies suggest apositive impact of electricity consumption on economic growth (Shiu and Lam 2004, Yuan et al 2007, Shahbaz and Lean 2012, Iyke 2015, Tang et al 2016, Streimikiene and Kasperowicz 2016.…”

Section: Introductionmentioning

confidence: 99%

Dynamics of electricity consumption, oil price and economic growth: Global perspective

et al. 2017

View full text Add to dashboard Cite

Abstract:This study uses the data from 157 countries from 1960 to 2014 to analyze the relationship between economic growth, electricity consumption, oil prices, capital, and labor. The economic growth of developing countries with industrial infrastructure has a more significant association with electricity consumption than oil prices. We use oil prices and electricity consumption jointly to study highly predictive observations for economic growth. The data are categorized by income, OECD and regional levels. The panel cointegration, long-run parameter estimation, and Pool Mean Group testsare used to analyze the cointegration and short-run and long-run relationships between the variables. The empirical results indicate the presence of cointegration between the variables. The presence of feedback effects between electricity consumption and economic growth, oil prices and economic growth is valid. These findings confirm that inspite of the oil prices, developing countries rely heavily on electricity consumption for economic growth. In the short run, growth and feedback effects suggest that more vigorous electricity policies should be implemented to attain sustainable economic growth for the long-term.

show abstract

“…Connect all maxima by a cubic spline line to generate the upper envelop, and connect all minima by another cubic spline line to generate the lower envelop. The mean value of the upper envelop and the lower envelop is defined as m, and the difference of p(t) and m is defined as h, which is illustrated in Equation (1).…”

Section: Empirical Mode Decompositionmentioning

confidence: 99%

“…Compared with traditional energy such as natural gas, coal and oil, electric power is more suitable and efficient for the requirement of environment-friendly society. In the field of power system planning, the accuracy of electric load forecasting is of great importance for energy generating capacity scheduling and power system management [1,2]. An overestimation may waste many energy resources and significantly improve the operational costs, and an underestimation will decrease the reliability of the power system and cannot meet the requirement of electricity utilization [3].…”

Section: Introductionmentioning

confidence: 99%

An Ensemble Model Based on Machine Learning Methods and Data Preprocessing for Short-Term Electric Load Forecasting

et al. 2017

View full text Add to dashboard Cite

Abstract:The experience with deregulated electricity market has shown the increasingly important role of short-term electric load forecasting in the energy producing and scheduling. However, because of nonlinear, stochastic and nonstable characteristics associated with the electric load series, it is extremely difficult to precisely forecast the electric load. This paper aims to establish a novel ensemble model based on variational mode decomposition (VMD) and extreme learning machine (ELM) optimized by differential evolution (DE) algorithm for multi-step ahead electric load forecasting. The proposed model is novel in the sense that VMD is firstly applied to decompose the original electric load series into a set of components with different frequencies in order to effectively eliminate the stochastic fluctuation characteristic so as to improve the overall prediction accuracy. The proposed ensemble model is tested using two electric load series collected from New South Wales (NSW) and Queensland (QLD) in the Australian electricity market. The experimental results show that: (1) the data preprocessing by VMD can effectively decrease the stochastic fluctuation characteristics that existed in the electric load series, consequently improving the whole forecasting accuracy, and; (2) the proposed forecasting model performs better than all other benchmark models for both one-step and multi-step ahead electric load forecasting.

show abstract

Electricity consumption and economic growth, the case of Lebanon

Cited by 156 publications

References 15 publications

On the causality and determinants of energy and electricity demand in South Africa: A review

On the causality and determinants of energy and electricity demand in South Africa: A review

Dynamics of electricity consumption, oil price and economic growth: Global perspective

An Ensemble Model Based on Machine Learning Methods and Data Preprocessing for Short-Term Electric Load Forecasting

Contact Info

Product

Resources

About