World oil demand’s shift toward faster growing and less price-responsive products and regions

Using data for 1971–2008, we estimate the effects of changes in price and income on world oil demand, disaggregated by product – transport oil, fuel oil (residual and heating oil), and other oil – for six groups of countries. Most of the demand reductions since 1973–74 were due to fuel-switching away from fuel oil, especially in the OECD; in addition, the collapse of the Former Soviet Union (FSU) reduced their oil consumption substantially. Demand for transport and other oil was much less price-responsive, and has grown almost as rapidly as income, especially outside the OECD and FSU. World oil demand has shifted toward products and regions that are faster growing and less price-responsive. In contrast to projections to 2030 of declining per-capita demand for the world as a whole – by the U.S. Department of Energy (DOE), International Energy Agency (IEA) and OPEC – we project modest growth. Our projections for total world demand in 2030 are at least 20% higher than projections by those three institutions, using similar assumptions about income growth and oil prices, because we project rest-of-world growth that is consistent with historical patterns, in contrast to the dramatic slowdowns which they project.     

Demand projections of petroleum products and natural gas in India

Indian economy has moved into a dynamic phase. It is necessary to see how energy demand will grow in this phase. In this paper, econometric models are developed for the various petroleum products separately with the aim of capturing variables that are specific to the individual fuel. This study projects the demand of fuels up to 2011–2012, end period for the 11th Five Year Plan, under two scenarios of annual gross domestic product (GDP) growth of 6% and 8%. The demand of petroleum products for the year 2011–2012 is estimated to be 147 and 162 million tons in the business as usual scenario of 6% and optimistic scenario of 8% GDP growth, respectively. Similarly, the demand of natural gas for the year 2011–2012 has been estimated to be 46 and 49 billion cubic meters for 6% and 8% growth, respectively. The projections suggest the level of preparedness that will be required from the oil and gas sector to enable India achieve the GDP growth target that it aims to.     

Genetic algorithm approach to estimate transport energy demand in Turkey

Transport energy modeling is a subject of current interest among transport engineers and scientists concerned with problems of sustainable transport. Transport energy planning is not possible without a reasonable knowledge of past and present energy consumption and likely future demands. In this study, three forms of the energy demand equations are developed in order to improve transport energy demand estimation efficiency for future projections based on genetic algorithm (GA) notion. The Genetic Algorithm Transport Energy Demand Estimation (GATEDE) model is developed using population, gross domestic product and vehicle-km. All equations proposed here are linear and non-linear, of which one is linear, second is exponential and third is quadratic. The quadratic form of the GATEDE model provided better-fit solution to the observed data and can be used with a high correlation coefficient for Turkey's future transport energy projections. It is expected that this study will be helpful in developing highly applicable and productive planning for transport energy policies. The GATEDE gives transport energy demand in comparison with the other transport energy demand projections. The GATEDE model plans the sectoral energy demand of Turkey until 2020.     

Electricity consumption forecasting in Italy using linear regression models

The influence of economic and demographic variables on the annual electricity consumption in Italy has been investigated with the intention to develop a long-term consumption forecasting model.The time period considered for the historical data is from 1970 to 2007. Different regression models were developed, using historical electricity consumption, gross domestic product (GDP), gross domestic product per capita (GDP per capita) and population.A first part of the paper considers the estimation of GDP, price and GDP per capita elasticities of domestic and non-domestic electricity consumption. The domestic and non-domestic short run price elasticities are found to be both approximately equal to −0.06, while long run elasticities are equal to −0.24 and −0.09, respectively. On the contrary, the elasticities of GDP and GDP per capita present higher values.In the second part of the paper, different regression models, based on co-integrated or stationary data, are presented. Different statistical tests are employed to check the validity of the proposed models.A comparison with national forecasts, based on complex econometric models, such as Markal-Time, was performed, showing that the developed regressions are congruent with the official projections, with deviations of ±1% for the best case and ±11% for the worst. These deviations are to be considered acceptable in relation to the time span taken into account.     

Turkey’s short-term gross annual electricity demand forecast by fuzzy logic approach

This paper aims to forecast Turkey's short-term gross annual electricity demand by applying fuzzy logic methodology while general information on economical, political and electricity market conditions of the country is also given. Unlike most of the other forecast models about Turkey's electricity demand, which usually uses more than one parameter, gross domestic product (GDP) based on purchasing power parity was the only parameter used in the model. Proposed model made good predictions and captured the system dynamic behavior covering the years of 1970–2014. The model yielded average absolute relative errors of 3.9%. Furthermore, the model estimates a 4.5% decrease in electricity demand of Turkey in 2009 and the electricity demand growth rates are projected to be about 4% between 2010 and 2014. It is concluded that forecasting the Turkey's short-term gross electricity demand with the country's economic performance will provide more reliable projections. Forecasting the annual electricity consumption of a country could be made by any designer with the help of the fuzzy logic procedure described in this paper. The advantage of this model lies on the ability to mimic the human thinking and reasoning.     

Forecasting the transport energy demand based on PLSR method in China

Transportation sector accounts for a major share of energy consumption in China, especially the petroleum products, which experienced rapid increases in energy demand. The purpose of this study is to forecast transport energy demand for 2010, 2015 and 2020 based on partial least square regression (PLSR) method under two scenarios. Transport energy demand is analyzed for the period of 1990–2006 based on gross domestic product (GDP), urbanization rate, passenger-turnover and freight-turnover. This method suggests that transport energy demand for 2020 will reach to a level of around 433.13 Mtce and 468.26 Mtce, respectively. Those figures are very close to the estimation obtained by Energy Research Institute of China. Thus this study provides an effective tool, which can be used as an alternative solution and estimation techniques for the transport energy demand.     

Forecasting total and industrial sector electricity demand based on genetic algorithm approach: Turkey case study

This study deals with estimation of the total and industrial sector electricity consumption based on genetic algorithm (GA) approach, and then proposes two scenarios to project future consumptions. Total electricity consumption is estimated based on gross national product (GNP), population, import and export figures of Turkey. Industrial sector electricity is calculated based on the GNP, import and export figures. Three forms of the genetic algorithm electricity demand (GAED) models for the total and two forms for the industrial electricity consumption are developed. The best‐fit GAED model in terms of total minimum relative average errors between observed and estimated values is selected for future demand estimation. ‘High‐ and low‐growth scenarios’ are proposed for predicting the future electricity consumption. Results showed that the GAED estimates the electricity demand in comparison with the other electricity demand projections. The GAED model plans electricity demand of Turkey until 2020. Copyright © 2005 John Wiley & Sons, Ltd.     

Transport energy modeling with meta-heuristic harmony search algorithm,an application to Turkey

This study proposes a new method for estimating transport energy demand using a harmony search (HS) approach. HArmony Search Transport Energy Demand Estimation (HASTEDE) models are developedtaking population, gross domestic product and vehicle kilometers as an input. The HASTEDE models are in forms of linear, exponential and quadratic mathematical expressions and they are applied to Turkish Transportation sector energy consumption. Optimum or near-optimum values of the HS parameters are obtained with sensitivity analysis (SA). Performance of all models is compared with the Ministry of Energy and Natural Resources (MENR) projections. Results showed that HS algorithm may be used for energy modeling, but SA is required to obtain best values of the HS parameters. The quadratic form of HASTEDE will overestimate transport sector energy consumption by about 26% and linear and exponential forms underestimate by about 21% when they are compared with the MENR projections. This may happen due to the modeling procedure and selected parameters for models, but determining the upper and lower values of transportation sector energy consumption will provide a framework and flexibility for setting up energy policies.     

Modeling and forecasting of Turkey’s energy consumption using socio-economic and demographic variables

This study deals with the modeling of the energy consumption in Turkey in order to forecast future projections based on socio-economic and demographic variables (gross domestic product-GDP, population, import and export amounts, and employment) using artificial neural network (ANN) and regression analyses. For this purpose, four diverse models including different indicators were used in the analyses. As the result of the analyses, this research proposes Model 2 as a suitable ANN model (having four independent variables being GDP, population, the amount of import and export) to efficiently estimate the energy consumption for Turkey. The proposed model predicted the energy consumption better than the regression models and the other three ANN models. Thus, the future energy consumption of Turkey is calculated by means of this model under different scenarios. The predicted forecast results by ANN were compared with the official forecasts. Finally, it was concluded that all the scenarios that were analyzed gave lower estimates of the energy consumption than the MENR projections and these scenarios also showed that the future energy consumption of Turkey would vary between 117.0 and 175.4 Mtoe in 2014.     

Prediction of net energy consumption based on economic indicators (GNP and GDP) in Turkey

The most important theme in this study is to obtain equations based on economic indicators (gross national product—GNP and gross domestic product—GDP) and population increase to predict the net energy consumption of Turkey using artificial neural networks (ANNs) in order to determine future level of the energy consumption and make correct investments in Turkey. In this study, three different models were used in order to train the ANN. In one of them (Model 1), energy indicators such as installed capacity, generation, energy import and energy export, in second (Model 2), GNP was used and in the third (Model 3), GDP was used as the input layer of the network. The net energy consumption (NEC) is in the output layer for all models. In order to train the neural network, economic and energy data for last 37 years (1968–2005) are used in network for all models. The aim of used different models is to demonstrate the effect of economic indicators on the estimation of NEC. The maximum mean absolute percentage error (MAPE) was found to be 2.322732, 1.110525 and 1.122048 for Models 1, 2 and 3, respectively. R² values were obtained as 0.999444, 0.999903 and 0.999903 for training data of Models 1, 2 and 3, respectively. The ANN approach shows greater accuracy for evaluating NEC based on economic indicators. Based on the outputs of the study, the ANN model can be used to estimate the NEC from the country's population and economic indicators with high confidence for planing future projections.     

Demographics,energy and our homes

The projected growth in households in the UK is a key factor in future domestic energy consumption, particularly electricity consumption. While every household needs a home and its heating, lighting and appliances, increasing incomes have historically led to significantly higher appliance ownership, higher expectations of levels of energy service and greater usage. In the past this trend was combined with increasing household numbers to drive growth in domestic electricity demand. Official projections for population growth and household composition indicate significant drivers for future growth in energy demand. Curbing this will require policies to reverse the tendency for energy–efficiency improvements to be overwhelmed by growing numbers of households, more widespread appliance ownership and increased service expectations.     

Estimating emissions on vehicular traffic based on projected energy and transport demand on rural roads: Policies for reducing air pollutant emissions and energy consumption

This study deals with the estimation of emissions caused by vehicular traffic based on transport demand and energy consumption. Projected transport demand is calculated with Genetic Algorithm (GA) using population, gross domestic product per capita (GDPPC) and the number of vehicles. The energy consumption is modelled with the GA using the veh-km. The model age of the vehicles and their corresponding share for each year using the reference years is obtained. The pollutant emissions are calculated with estimated transport and energy demand. All the calculations are made in line to meet the European standards. For this purpose, two cases are composed. Case 1: Emissions based on energy consumption, and Case 2: Emissions based on transport demand. The both cases are compared. Three policies are proposed to control demand and the emissions. The policies provided the best results in terms of minimum emissions and the reasonable share of highway and railway mode as 70% and 30% usage for policy I, respectively. The emission calculation procedure presented in this study would provide an alternative way to make policies when there is no adequate data on emission measurement in developing countries.     

Analysis of the Lithuanian final energy consumption using fuzzy sets

In the paper, the development of final energy consumption in Lithuania, on the basis of realistic economic scenarios, is investigated. The main parameters influencing the energy consumption are the gross national product (GNP) and the wholesale price of energy. Owing to the uncertainties in former socialist economies, these parameters are described as ‘fuzzy sets.’ The theory of fuzzy sets is used to study the influences that the prices of preceding periods have on the actual final energy consumption, with a quasidynamic model. In so far as this mechanism cannot be ascertained for Lithuania, experiences with other former centrally planned economies, which have already turned into a kind of market economy, are applied to give realistic projections for the transitory period. The underlying scenarios for the GNP and price developments are taken from official Lithuanian projections. The results of the fuzzy quasidynamic model are compared with the official final energy demand projections, to provide policy advice for a proper restructuring of the energy system.     

The causal relationship between electricity consumption and economic growth in a Gaming and Tourism Center: The case of Macao SAR, the People’s Republic of China

A number of Asian cities decided to establish gaming and resort facilities in order to capitalize on the growing number of gamblers and their family members in Asia. In doing so, they expect to sustain economic growth but, on the other hand, will consume a considerable amount of energy. Nevertheless, the causal relationship between economic growth and electricity consumption in this type of service-oriented territories has never been investigated. Using the historical data obtained from the Government of Macao SAR, we found that electricity consumption and economic growth in terms of gross domestic product are co-integrated for the period of 1999 Quarter 1-2008 Quarter 4. Moreover, vector error correction (VEC) models indicated a lack of short-run relationships but showed that there was a long-run equilibrium relationship between electricity consumption and gross domestic product. The accuracy of VEC models was assessed by using the mean squared error and the mean absolute error. The error analysis shows that VEC models reproduced time series of gross domestic product and electricity consumption in difference form accurately.     

China on the move: Oil price explosion?

Rapid expansion of highway and jet traffic in China has created a surge of demand for oil products, putting pressure on world energy markets and petroleum product prices. This paper examines trends in freight and passenger traffic to assess how growth in China's transport demand relates to growth in China's economy, as well as the energy intensity of transport. Based on assumptions about demand elasticity and energy intensity, a range of scenarios is developed for China's oil demand through 2020. Incremental oil demand from China's transport sector is then compared with world oil demand projections to assess the likely impact on world oil prices. The finding is that new demand from China's transport sector would likely raise world oil prices in 2020 by 1–3% in reference scenarios or by 3–10% if oil supply investment is constrained.     

Use of artificial neural networks for transport energy demand modeling

The paper illustrates an artificial neural network (ANN) approach based on supervised neural networks for the transport energy demand forecasting using socio-economic and transport related indicators. The ANN transport energy demand model is developed. The actual forecast is obtained using a feed forward neural network, trained with back propagation algorithm. In order to investigate the influence of socio-economic indicators on the transport energy demand, the ANN is analyzed based on gross national product (GNP), population and the total annual average veh-km along with historical energy data available from 1970 to 2001. Comparing model predictions with energy data in testing period performs the model validation. The projections are made with two scenarios. It is obtained that the ANN reflects the fluctuation in historical data for both dependent and independent variables. The results obtained bear out the suitability of the adopted methodology for the transport energy-forecasting problem.     

Energy demand estimation of South Korea using artificial neural network

Because South Korea's industries depend heavily on imported energy sources (fifth largest importer of oil and second largest importer of liquefied natural gas in the world), the accurate estimating of its energy demand is critical in energy policy-making. This research proposes an artificial neural network model (a structure with feed-forward multilayer perceptron, error back-propagation algorithm, momentum process, and scaled data) to efficiently estimate the energy demand for South Korea. The model has four independent variables, such as gross domestic product (GDP), population, import, and export amounts. The data are obtained from diverse local and international sources. The proposed model better estimated energy demand than a linear regression model (a structure with multiple linear variables and least square method) or an exponential model (a structure with mixed integer variables, branch and bound method, and Broyden–Fletcher–Goldfarb–Shanno (BFGS) method) in terms of root mean squared error (RMSE). The model also forecasted better than the other two models in terms of RMSE without any over-fitting problem. Further testing with four scenarios based upon reliable source data showed unanticipated results. Instead of growing permanently, the energy demands peaked at certain points, and then decreased gradually. This trend is quite different from the results by regression or exponential model.     

The challenge of reducing energy consumption of the Top-1000 largest industrial enterprises in China

In 2005, the Chinese government announced an ambitious goal of reducing energy consumption per unit of gross domestic product (GDP) by 20% between 2005 and 2010. One of the key initiatives for realizing this goal is the Top-1000 Energy-Consuming Enterprises program. The energy consumption of these 1000 enterprises accounted for 33% of national and 47% of industrial energy usage in 2004. Under the Top-1000 program, 2010 energy consumption targets were determined for each enterprise. The objective of this article is to evaluate the program design and initial results, given limited information and data, to understand the possible implications of its success in terms of energy and carbon dioxide emission reductions and to recommend future program modifications based on international experience with similar target-setting agreement programs. Even though the Top-1000 program was designed and implemented rapidly, it appears that – depending upon the GDP growth rate – it could contribute to somewhere between approximately 10% and 25% of the savings required to support China's efforts to meet a 20% reduction in energy use per unit of GDP by 2010.     

Forecasting electricity consumption in New Zealand using economic and demographic variables

The influence of selected economic and demographic variables on the annual electricity consumption in New Zealand has been investigated. The study uses gross domestic product, average price of electricity and population of New Zealand during the period 1965–1999. Models are developed using multiple linear regression analysis. It was found that the electricity consumption correlated effectively with all variables. Forecasts made using these models were compared with some available national forecasts. The forecasts are also compared with the forecasts of the previously developed Logistic model.     

Energy consumption and GDP in Tunisia: Cointegration and causality analysis

In this paper, the Johansen cointegration technique is used to examine the causal relationship between per capita energy consumption (PCEC) and per capita gross domestic product (PCGDP) for Tunisia during the 1971–2004 period. In order to test for Granger causality in the presence of cointegration among the variables, a vector error correction model (VECM) is used instead of a vector autoregressive (VAR) model. Our estimation results indicate that the PCGDP and PCEC for Tunisia are related by one cointegrating vector and that there is a long-run bi-directional causal relationship between the two series and a short-run unidirectional causality from energy to gross domestic product (GDP). The source of causation in the long-run is found to be the error-correction terms in both directions. Hence, an important policy implication resulting from this analysis is that energy can be considered as a limiting factor to GDP growth in Tunisia. Conclusions for Tunisia may also be relevant for a number of countries that have to go through a similar development path of increasing pressure on already scarce energy resources.