Analysis of the Syrian long-term energy and electricity demand projection using the end-use methodology

A comprehensive analysis of the possible future long-term development of Syrian energy and electricity demand covering the period 1999–2030 is presented. The analysis was conducted using the IAEA's model MAED, which relies upon the end-use approach. This model has been validated during the last two decades through the successful application in many developing countries, even those having partial market economy and energy subsidy.     

World transport energy demand modelling : Methodology and elasticities

This paper presents the International Energy Agency's (IEA) approach of modelling transport energy demand. Fuel demand, which is not a demand per se, is derived, whenever possible, from the economic activity in the transport sector and not estimated directly, ie using one equation or (simultaneous) equation system. In general, the transport models employ a ‘two-step-approach’: in the first step, transport activity, the sector's relevant energy service, is estimated econometrically. In the second step, the transport activity projections are then combined with estimates of efficiency improvements, car turnover rates and diesel/gasoline penetration assumptions in order to arrive at projections of fuel demand. The principal advantages of this approach are that the relevant energy services are modelled and that, for model simulation, efficiency improvements can be dealt with explicitly. The effectiveness of economic instruments is a function of the reaction of consumers (and businesses) to income and price changes. An in-depth understanding of income and price elasticities of transport demand and transport energy demand is important in order to be able to assess the effectiveness of policies considered. The paper also shows the underlying long-term income and price elasticities for OECD and non-OECD regions.     

Prediction of greenhouse gas reduction potential in Japanese residential sector by residential energy end-use model

A model is developed that simulates nationwide energy consumption of the residential sector by considering the diversity of household and building types. Since this model can simulate the energy consumption for each household and building category by dynamic energy use based on the schedule of the occupants’ activities and a heating and cooling load calculation model, various kinds of energy-saving policies can be evaluated with considerable accuracy. In addition, the average energy efficiency of major electric appliances used in the residential sector and the percentages of housing insulation levels of existing houses is predicted by the “stock transition model.” In this paper, energy consumption and CO₂ emissions in the Japanese residential sector until 2025 are predicted. For example, as a business – as-usual (BAU) case, CO₂ emissions will be reduced by 7% from the 1990 level. Also evaluated are mitigation measures such as the energy efficiency standard for home electric appliances, thermal insulation code, reduction of standby power, high-efficiency water heaters, energy-efficient behavior of occupants, and dissemination of photovoltaic panels.     

Comparison of neural network,conditional demand analysis,and engineering approaches for modeling end-use energy consumption in the residential sector

This paper investigates the use of conditional demand analysis (CDA) method to model the residential end-use energy consumption at the national level. There are several studies where CDA was used to model energy consumption at the regional level; however the CDA method had not been used to model residential energy consumption at the national level. The prediction performance and the ability to characterize the residential end-use energy consumption of the CDA model are compared with those of a neural network (NN) and an engineering based model developed earlier. The comparison of the predictions of the models indicates that CDA is capable of accurately predicting the energy consumption in the residential sector as well as the other two models. The effects of socio-economic factors are estimated using the NN and the CDA models, where possible. Due to the limited number of variables the CDA model can accommodate, its capability to evaluate these effects is found to be lower than the NN model.     

Determinants of energy demand in the French service sector: A decomposition analysis

This paper analyzes the changes in the energy consumption of the service sector in France over the period 1995–2006, using the logarithmic mean Divisia index I (LMDI I) decomposition method. The analysis is carried out at various disaggregation levels to highlight the specifics of each sub-sector and end-use according to their respective determinants. The results show that in this period the economic growth of the service sector was the main factor that led to the increase in total energy consumption. Structure, productivity, substitution and intensity effects restricted this growth, but with limited effect. By analyzing each end-use, this paper enables a more precise understanding of the impact of these factors. The activity effect was the main determinant of the increase in energy consumption for all end-uses except for air conditioning, for which the equipment rate effect was the main factor. Structural changes in the service sector primarily impacted energy consumption for space heating and cooking. Improvements in productivity limited the growth of energy consumption for all end-uses except for cooking. Finally, energy efficiency improvements mainly affected space-heating energy use.     

Rural household energy demand modelling : A case study of Nepal

This paper describes the methodology adopted to arrive at a casual linear multiple regression to estimate and project end-use energy requirements of a rural household of Nepal. The general model developed is capable of estimating and projecting the total end-use energy requirements if eight variables are known. These are: standard population, household expenditure, agricultural commodity, number of livestock, number of cookstoves, area covered by housing, topography and forest accessibility condition, of a particular village. The method has been applied to estimate primary energy requirements in the household sector of a few surveyed villages typical of Nepal's different physiographical regions. Total useful energy is estimated considering the end-use efficiency of the devices employed for specific end-use activities. Relevance of the socio-economic variable for estimating end-use energy requirements for different villages varies according to the availability, price and choice of specific fuels for different end-use activities.     

Structural change and Thailand energy demand

This paper examines the impact of changes in the structure of the economy, radical changes in economic policy and oil price shocks on the relation between Thailand energy demand and its macroeconomic determinants. The impact of these structural changes on the relationship between energy consumption, income, energy prices and structural variation is examined through unit root and cointegration tests, the cointegration relationship and the error correction model. Methods which endogenize the location of an a priori unknown break point are employed to assess the impact of structural change. In general, the recognition of structural change has lead to some unique insights. In particular, the results of some of the conventional unit root and cointegration tests are reversed once structural changes are recognized. Estimates from the cointegrating regression imply long-run income, price, and structural variation elasticities of 0.568, −0.600 and 1.046, respectively. In comparison, estimates from the error correction model suggest a higher short-run income elasticity (0.788) but lower short-run price and structural variation elasticities (−0.522 and 0.491, respectively). One of the important implications of the estimates pertains to the low price elasticity for aggregate energy demand which implies that the over-pricing of energy as a policy instrument is not likely to be very influential for restraining future energy demand. Additionally, taxes on energy prices are unlikely to achieve government goals for energy conservation and environmental improvement, although they may well be efficient for raising revenue. Copyright © 2006 John Wiley & Sons, Ltd.     

ARIMA forecasting of primary energy demand by fuel in Turkey

Forecasting of energy demand in emerging markets is one of the most important policy tools used by the decision makers all over the world. In Turkey, most of the early studies used include various forms of econometric modeling. However, since the estimated economic and demographic parameters usually deviate from the realizations, time-series forecasting appears to give better results. In this study, we used the Autoregressive Integrated Moving Average (ARIMA) and seasonal ARIMA (SARIMA) methods to estimate the future primary energy demand of Turkey from 2005 to 2020. The ARIMA forecasting of the total primary energy demand appears to be more reliable than the summation of the individual forecasts. The results have shown that the average annual growth rates of individual energy sources and total primary energy will decrease in all cases except wood and animal–plant remains which will have negative growth rates. The decrease in the rate of energy demand may be interpreted that the energy intensity peak will be achieved in the coming decades. Another interpretation is that any decrease in energy demand will slow down the economic growth during the forecasted period. Rates of changes and reserves in the fossil fuels indicate that inter-fuel substitution should be made leading to a best mix of the country's energy system. Based on our findings we proposed some policy recommendations.     

Reevaluation of Turkey's hydropower potential and electric energy demand

This paper deals with Turkey's hydropower potential and its long-term electric energy demand predictions. In the paper, at first, Turkey's energy sources are briefly reviewed. Then, hydropower potential is analyzed and it has been concluded that Turkey's annual economically feasible hydropower potential is about 188 TWh, nearly 47% greater than the previous estimation figures of 128 TWh. A review on previous prediction models for Turkey's long-term electric energy demand is presented. In order to predict the future demand, new increment ratio scenarios, which depend on both observed data and future predictions of population, energy consumption per capita and total energy consumption, are developed. The results of 11 prediction models are compared and analyzed. It is concluded that Turkey's annual electric energy demand predictions in 2010, 2015 and 2020 vary between 222 and 242 (average 233) TWh; 302 and 356 (average 334) TWh; and 440 and 514 (average 476) TWh, respectively. A discussion on the role of hydropower in meeting long-term demand is also included in the paper and it has been predicted that hydropower can meet 25–35% of Turkey's electric energy demand in 2020.     

Energy conservation policy and environment for a clean and sustainable energy future

One of the most serious difficulties facing humanity is the need for a sustainable future. Maintaining nonrenewable energy resources for future generations is a main purpose of sustainability. However, for sustainable future, produced and consumed energy should support human development all social, economic and environmental dimensions. Problems related with environment and energy shortages have put all circles to the task of actively promoting education in energy conservation. Therefore, many countries have started to identify their responsibility for decreasing the unfavorable effect of high energy use on the environment. The primary contribution of this paper will be to ensure energy conservation policy advices to decrease climate change without effecting economic growth for a clean and sustainable energy future.     

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.     

Modelling energy consumption of the Jordanian transportation sector: the application of multivariate linear regression and adaptive neuro-fuzzy techniques

Studying the current level of energy consumed by the transportation sector in Jordan is a top priority and an important variable when it comes to modelling accurate projections of future consumption in order to monitor Jordan's sustainable development. This study compares two methods for modelling energy consumption within the Jordanian transportation sector: a multivariate linear regression model and a Neuro-fuzzy model. Within these two paradigms, energy consumption is modelled as a function of a number of factors such as: vehicles number, level of income and ownership, and fuel prices. A parallelism between the two models is highlighted providing a precise simulation for the energy consumption in the Jordanian transportation sector. The comparison proposes that when it comes to forecasting, the performance of the neuro-fuzzy model exceeds that of the multivariate linear regression model.     

Hybrid energy management with respect to a hydrogen energy system and demand response

A Hybrid Energy System (HES) is a mechanism that combines multiple sources of energy connected together to achieve synchronised energy output. However, increased energy consumption, operator energy expenses, and the potential environmental impact of increased emissions from the exhaustion of non-renewable energy resources (fossil fuel) pose major challenges to HES. This research is to conduct energy management strategy based on a demand response (DR) program and a hydrogen storage system by designing a Program Logic Controller (PLC) unit. The hybrid system is evaluated by comparing different scenarios such as a hydrogen energy system and demand response. The purpose of this research is to reducing peak demand, minimise the cost of the system and also to extract surplus power generation out of the rate of the battery. This can be achieved by improving the system performances and by eliminating any degradation at the early stages. Organisations or companies must be sure their systems are working properly and that their investments will pay off.     

Trends of energy demand in the Middle East: A sectoral level analysis

The Middle East region is a key player in the world energy market today. It holds approximately over 50% of the world's proven fossil fuel reserves. Yet, the region is significantly challenged by the large dependence on finite fossil fuel resources in its primary energy supply. The intricate relationship between climate change mitigation and the development of energy systems underlines great uncertainty over the future of energy development in the Middle East. Such uncertainty is greatly linked to growing energy demands and the region's capacity to transition to low‐carbon energy systems. Over the past 20 years, the total primary energy demand in the Middle has almost tripled due to rapid population growth and economic development. Notably, most of the growing energy demand was concentrated in 5 countries, Iran, Saudi Arabia, Iraq, Kuwait, and the UAE. These 5 countries represented around 82% of the total primary energy demand in 2015, with Saudi Arabia and Iran alone accounted for 60%. The core question of this paper is what are the possible implications of growing energy demands in these countries and which sectors will entail significant increases in the projected energy requirements? The significance of the work presented here stems from analyzing 4 major countries that constitute the largest share in Middle East's total energy consumption and associated emissions. Examining these 4 countries together is important to highlight how future increase in these countries could largely affect the overall energy demand from the Middle East region in the next 20 years. Thus, the scope of the paper is looking at energy demand implications in 4 countries, Iran, Saudi Arabia, Kuwait, and the United Arab Emirates (UAE). Iraq is excluded from the analysis due to the large political uncertainty associated with Iraq's energy development. Here, a regression model is used to forecast energy demand from 5 economic sectors across the 4 countries using projected increase in population and gross domestic product (GDP) by 2030. Results indicate that most of the projected energy demand will be from Iran and Saudi Arabia. In addition, industry and transportation sectors will witness the largest increase among the 5 sectors examined in the paper. For instance, industry and transportation sector will collectively account for 52% and 67% of the projected energy demand in Iran and Saudi Arabia, respectively. Such results are important to highlight when ascertaining sectoral level implications of future energy demands and to determine potential areas where energy savings can be made.     

Exploring drivers of energy demand in Cyprus – Scenarios and policy options

This paper describes a new set of energy demand forecasts for the Republic of Cyprus up to the year 2040, which have been developed in support of the renewable energy roadmap that was prepared for national authorities by the International Renewable Energy Agency. The analysis takes into account national end-use data from the residential and tertiary sector that had not been exploited up to now. Four final energy demand scenarios with diverging assumptions were defined in this study, offering a wide range of possible outcomes up to 2040; in addition, four alternative scenarios were applied for sensitivity analysis. Two of these scenarios can be regarded as those continuing the trends of the recent past in Cyprus (prior to the economic and financial downturn of years 2011–2014). However, a more rigorous implementation of energy efficiency measures in buildings and transport, as defined in the fourth scenario of this study, is also realistic; despite its potential costs, it might allow Cyprus both to decrease its carbon emissions in line with the long-term EU decarbonisation targets, and to reduce its dependence on fossil fuels, thereby promoting energy efficiency as an important climate change adaptation measure.     

A comprehensive energy supply model in an office building based on the reference energy system

In the last decade, technological innovations have resulted in considering distributed generation with heat recovery capability in addition to centralised generation. This work aims to develop a comprehensive model analysing techno-economic parameters of energy supply in an office building. The energy model is developed on the basis of the reference energy system with the linear programming technique. The objective function is the annual value of total costs of the energy supply system. The results of the model for the building of Sharif Energy Research Institute indicate that the most appropriate generation technology is an internal combustion engine of 461 kW and a water boiler of 152 kW. In addition to supplying the final service demand, hot water feeds an absorption chiller of 507 kW to produce cold water. Furthermore, storage systems of hot and cold water are required in order to achieve high load factors in the system.     

The status of transportation demand management in Greater Vancouver and energy implications

As transportation problems deteriorate and resources become limited, transportation professionals have realized that they cannot always rely on supplying more infrastructure to meet travel demands. Instead, they have looked to transportation demand management (TDM) as a means of influencing the demand for transportation. TDM is currently being planned for implementation in the Greater Vancouver region. Under an umbrella of extensive planning strategies, TDM is considered to play a significant role in the region's future state of transportation. Four main components are being planned under the Greater Vancouver Regional TDM Project: trip reduction service, parking management, conversion of fixed automobile costs to variable, and road pricing. Currently, 70% of the provincial transportation energy needs is due to road transportation, of which one-third is due to commuters. Compared to trend forecasts for the year 2021, it is estimated that these TDM measures could produce savings of approximately 56,000 l of automobile fuel in the morning peak hour, 343,000 l on a typical weekday, and 113,000,000 l annually. This paper will discuss the current and future states of transportation in the region, the planning process leading up to the TDM project, and the estimated implications of TDM on transportation energy needs.     

Planning sustainable hydrogen supply chain infrastructure with uncertain demand

This study introduces a multi-period optimization model taking into account the stochasticity and the effect of uncertainty in the hydrogen production, storage and usage in macro view (e.g. county level). The objective function includes minimization of total daily social cost of the hydrogen supply chain network with uncertain demand. There are several factors and key attributes, which influence consumer choice to buy a fuel cell vehicle. At the same time, consumer preference on the demand side is the most important factor in predicting changes in the auto market. A spatially aggregated demand model was developed to estimate the potential demand for fuel cell vehicles based on different household attributes such as income, education etc. These models were applied to evaluate the future hydrogen supply chain for State of New Jersey.     

大数据驱动的我国新能源汽车需求分析

文章基于网络搜索大数据,以新能源汽车为例,结合统计学和计量经济学理论与方法,利用斯皮尔曼相关系数、协整检验和格兰杰因果关系,检验分析了搜索指数与新能源汽车实际需求之间的关系。以新能源汽车历史销量作为单一变量建立自回归滑动平均模型(ARMA),并与加入了搜索指数的向量自回归模型(VAR)进行比较。结果表明,加入搜索指数的预测模型相较传统的预测模型,在样本期内和样本期外的预测精度分别提高了11.69%和14.95%。该模型只需利用前4个月的新能源汽车销售数据和网络搜索大数据,就能够准确地预测下一个月的需求,在提高预测时效性的同时,也为个人、企业和政府决策提供可靠的依据。