The demand for energy for irrigation in the USA

There seems to be some question concerning the relationship between irrigation and the price of energy. Did changes in the price of energy actually impact irrigation in the USA? Additionally, the substitution between alternative types of energy seems to be a possibility for irrigation. Whether in fact it occurred is another matter. In this study, an appropriate model is developed and estimated to address the issues. The specific energy types considered include motor gasoline, diesel fuel, liquefied petroleum gas, natural gas and electrical energy. The results for 1978 and for 1980 clearly indicate that energy use for irrigation is responsive to the price of energy. Additionally, some substitution between energy types took place during this period. Finally, when the question of the stability of the demand for the various energy types is addressed, the suggestion is that the demand for the energy types considered was stable for the 1978 and 1980 periods.     

Dynamics of final sectoral energy demand and aggregate energy intensity

This paper proposes a regional and sectoral model of global final energy demand. For the main end-use sectors of consumption (industrial, commercial and public services, residential and road transportation), per-capita demand is expressed as an S-shaped function of per-capita income. Other variables intervene as well, like energy prices, temperatures and technological trends. This model is applied on a panel of 101 countries and 3 aggregates (covering the whole world) and it explains fairly well past variations in sectoral, final consumption since the beginning of the 2000s. Further, the model is used to analyze the dynamics of final energy demand, by sector and in total. The main conclusion concerns the pattern of change for aggregate energy intensity. The simulations performed show that there is no a priori reason for it to exhibit a bell-shape, as reported in the literature. Depending on initial conditions, the weight of basic needs in total consumption and the availability of modern commercial energy resources, various forms might emerge.     

An empirical analysis of energy demand in Namibia

Using a unique database of end-user local energy data and the recently developed Autoregressive Distributed Lag (ARDL) bounds testing approach to cointegration, we estimate the long-run elasticities of the Namibian energy demand function at both aggregated level and by type of energy (electricity, petrol and diesel) for the period 1980–2002. Our main results show that energy consumption responds positively to changes in GDP and negatively to changes in energy price and air temperature. The differences in price elasticities across fuels uncovered by this study have significant implications for energy taxation by Namibian policy makers. We do not find any significant cross-price elasticities between different fuel types.     

The impact of the first oil crisis on energy demand in Korea

This paper investigates how the first oil crisis affected energy demand in Korea. First, annual growth rates in energy consumptions for pre-and post-crisis periods are compared. Second, the price and income elasticities of energy sources estimated from the 1961–1972 and 1961–1976 observations are compared. Third, F-test is performed with an oil demand equation to see whether a structural change in oil demand occurred after the first oil crisis. The author concludes that the impact of the first oil crisis in Korea was not serious enough to cause a structural change in oil demand.     

Multivariate Box-Jenkins analysis: A case study in UK energy demand forecasting

The paper discusses a recent case study of Box-Jenkins multivariate techniques in the area of short-term energy demand forecasting by (i) explaining the reasons for adopting Box-Jenkins techniques; (ii) showing how the scale of the problem can be reduced by introducing a two-stage forecasting model; (iii) showing how the models performed against historical data in 1983.     

Functional forms in energy demand modeling

In the estimation of demand functions for energy resources, linear, log–linear and translog functional forms are commonly assumed. It has often been questioned whether such functional forms can indeed accurately represent the underlying relationships between the demand for various energy resources and explanatory variables such as energy prices, weather variables, income and other factors. This paper compares linear, log–linear and translog share equation functional forms against a non-parametric function. Bootstrapping methods are used to test the validity of using the three parametric functional forms in models of residential energy demand. Cross-sectional household-level data from the US BLS Consumption Expenditure survey and other government datasets are used. Each of the parametric functional forms tested performs poorly, suggesting that they may be insufficiently flexible to provide valid results in certain applications.     

The aging US population and residential energy demand

This piece explores the relationships between a rapidly aging U.S. population and the demand for residential energy. Data indicate that elderly persons use more residential energy than younger persons. In this time of steeply rising energy costs, energy is an especially important financial issue for the elderly with low and/or fixed incomes. As the absolute number of elderly as well as their proportion of the total US population both continue to increase, energy and the elderly population looms as another energy policy challenge.     

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.     

A regression analysis of the effect of energy use in agriculture

This study investigates the impacts of energy use on productivity of Turkey's agriculture. It reports the results of a regression analysis of the relationship between energy use and agricultural productivity. The study is based on the analysis of the yearbook data for the period 1971–2003.

Agricultural productivity was specified as a function of its energy consumption (TOE) and gross additions of fixed assets during the year. Least square (LS) was employed to estimate equation parameters. The data of this study comes from the State Institute of Statistics (SIS) and The Ministry of Energy of Turkey.     

Ant colony optimization approach to estimate energy demand of Turkey

This paper attempts to shed light on the determinants of energy demand in Turkey. Energy demand model is first proposed using the ant colony optimization (ACO) approach. It is multi-agent systems in which the behavior of each ant is inspired by the foraging behavior of real ants to solve optimization problem. ACO energy demand estimation (ACOEDE) model is developed using population, gross domestic product (GDP), import and export. All equations proposed here are linear and quadratic. Quadratic_ACOEDE provided better-fit solution due to fluctuations of the economic indicators. The ACOEDE model plans the energy demand of Turkey until 2025 according to three scenarios. The relative estimation errors of the ACOEDE model are the lowest when they are compared with the Ministry of Energy and Natural Resources (MENR) projection.     

Vietnam's energy sector: A review of current energy policies and strategies

Since the introduction of market-oriented economy in 1986, Vietnam has made noticeable socio-economic progress. In this progress, the energy sector has played a vital role. This role is likely to deepen in the years to come as Vietnam strives to achieve even higher levels in economic progress. Such deepening in the role of energy, this paper argues, will heighten concerns about the security of energy supply, and economic, environmental, social and political consequences. In order to address these issues, Vietnam has over the last decade, developed a suite of energy policies. A deeper review of these policies suggests that they are typified by economic-growth orientation, exclusive focus on a single-sector or single issue, and largely neglect the significance of cross-sectoral and cross-thematic issues arising from the interdependencies between energy, economy, and the polity at large. The existing energy policy settings are, therefore, unlikely to be able to provide a satisfactory redress to the challenges noted above. This paper provides an overview of the current energy policies with a view to identify areas where further policy effort is needed in order to facilitate a sustainable development of the Vietnamese energy sector.     

Structural changes in Japan''s economy and society and outlook for long-term energy supply and demand

The purpose of this report is to give an outlook for Japan's energy supply and demand to 2015, while taking into consideration structural changes in the economy and society at present and into the future, and to extract subjects that we should address today. When projecting energy supply and demand for the next two decades, we face a number of uncertainties both at home and abroad. In order to project energy supply and demand in the future, three scenarios are assumed: the standard case, the high growth case and the low growth case. Energy supply and demand are projected for each case, showing conditions under which the three scenarios are achieved.     

Improvement of energy demand forecasts using swarm intelligence: The case of Turkey with projections to 2025

The energy supply and demand should be closely monitored and revised the forecasts to take account of the progress of liberalization, energy efficiency improvements, structural changes in industry and other major factors. Medium and long-term forecasting of energy demand, which is based on realistic indicators, is a prerequisite to become an industrialized country and to have high living standards. Energy planning is not possible without a reasonable knowledge of past and present energy consumption and likely future demands. Energy demand management activities should bring the demand and supply closer to a perceived optimum. Turkey's energy demand has grown rapidly almost every year and is expected to continue growing. However, the energy demand forecasts prepared by the Turkey Ministry of Energy and Natural Resources overestimate the demand. Recently many studies are performed by researchers to forecast the energy demand of Turkey. Particle swarm optimization (PSO) technique has never been used for such a study. In this study a model is proposed, using PSO-based energy demand forecasting (PSOEDF), to forecast the energy demand of Turkey more efficiently. Although there are other indicators as well, gross domestic product (GDP), population, import and export are used as basic energy indicators of energy demand. In order to show the accuracy of the algorithm, a comparison is made with the ant colony optimization (ACO) energy demand estimation model which is developed for the same problem.     

Reduction potentials of energy demand and GHG emissions in China's road transport sector

Rapid growth of road vehicles, private vehicles in particular, has resulted in continuing growth in China's oil demand and imports, which has been widely accepted as a major factor effecting future oil availability and prices, and a major contributor to China's GHG emission increase. This paper is intended to analyze the future trends of energy demand and GHG emissions in China's road transport sector and to assess the effectiveness of possible reduction measures. A detailed model has been developed to derive a reliable historical trend of energy demand and GHG emissions in China's road transport sector between 2000 and 2005 and to project future trends. Two scenarios have been designed to describe the future strategies relating to the development of China's road transport sector. The ‘Business as Usual’ scenario is used as a baseline reference scenario, in which the government is assumed to do nothing to influence the long-term trends of road transport energy demand. The ‘Best Case’ scenario is considered to be the most optimized case where a series of available reduction measures such as private vehicle control, fuel economy regulation, promoting diesel and gas vehicles, fuel tax and biofuel promotion, are assumed to be implemented. Energy demand and GHG emissions in China's road transport sector up to 2030 are estimated in these two scenarios. The total reduction potentials in the ‘Best Case’ scenario and the relative reduction potentials of each measure have been estimated.     

Prospect of the upper limit of the energy demand in China from regional aspects

The aim of this article is to indicate the upper limit of the annual energy demand in China, taking into consideration regional demand trends and projecting these trends into the distant future. The upper limit of energy consumption is not strictly the maximum amount of consumption. It means that the actual consumption will possibly exceed this level but not by much.     

Potential energy demand for cooling in the 50 largest metropolitan areas of the world: Implications for developing countries

Air conditioning of dwellings in developing countries is currently rather rare, but increasing personal income is expected to change that. This study examined the potential energy demand for cooling in the 50 most populous metropolitan areas of the world, and assessed the incremental demand in developing countries that this would create on top of the current energy demand due to heating. The analysis used local cooling and heating degree-day data. The main results are as follows: (1) Most of the largest metropolitan areas are in developing countries (38 out of 50), and most of them, in turn, are in warm to hot climates. (2) All but two of the top 30 metropolitan areas in terms of cooling degree days are in developing countries. (3) The potential cooling demands are greater than heating demands in most of the metropolitan areas that are in developing countries (24 out of 38). The main implication of these findings is that increasing personal income is likely to lead to an unprecedented increase in energy demand in many developing countries. For example, the potential cooling demand in metropolitan Mumbai is about 24% of the demand for the entire United States.     

Weather, the estimation of residential energy demand relationships and the reporting of data

The need for adjusting energy related data, especially weather data, prior to estimating demand relationships is pointed out. A previous study by Rushdi in Energy Economics and other studies are used as examples. The lack of such adjustments could affect greatly the estimated results. The desirability of making data available along with an article is also pointed out.     

Energy models for demand forecasting—A review

Energy is vital for sustainable development of any nation - be it social, economic or environment. In the past decade energy consumption has increased exponentially globally. Energy management is crucial for the future economic prosperity and environmental security. Energy is linked to industrial production, agricultural output, health, access to water, population, education, quality of life, etc. Energy demand management is required for proper allocation of the available resources. During the last decade several new techniques are being used for energy demand management to accurately predict the future energy needs. In this paper an attempt is made to review the various energy demand forecasting models. Traditional methods such as time series, regression, econometric, ARIMA as well as soft computing techniques such as fuzzy logic, genetic algorithm, and neural networks are being extensively used for demand side management. Support vector regression, ant colony and particle swarm optimization are new techniques being adopted for energy demand forecasting. Bottom up models such as MARKAL and LEAP are also being used at the national and regional level for energy demand management.     

An energy demand model for a fleet of plug-in fuel cell vehicles and commercial building interfaced with a clean energy hub

This paper presents an energy demand model for a fleet of plug-in fuel cell vehicles and a medium-sized commercial/office building interfaced with a clean energy hub. The approach taken is to model the architecture and daily operation of every individual vehicle in the fleet. A simplified architecture model was developed, with daily operation divided into two periods: charging and travelling. During the charging period, the vehicle charges its batteries and refills its compressed hydrogen tanks. During the travelling period, the vehicle depletes the batteries and hydrogen tanks based on distance travelled. Daily travel distance is generated by a stochastic model. The modeling of the clean energy hub is also presented. The clean energy hub functions as an interface between electricity supply and the energy demand (i.e. hydrogen and electricity) of the vehicle fleet and the commercial building. Finally, a sample case is presented to demonstrate the use of the models.     

Testing for stability: Motor gasoline demand and distillate fuel oil demand

The structural stability of energy demand relationships is an important question in light of the events of the 1970s. This paper looks at this issue for two energy types, motor gasoline and distillate fuel oil (home heating oil). From the results of a statistical test developed by Brown, Durbin and Evans, it appears that the demand for motor gasoline destabilized following the 1979 Iranian revolution while the demand for distillate fuel oil destabilized following the very severe winter of 1976–1977. Once destabilized, neither of the demand relationships returned to their prior form.