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.

Energy models for demand forecasting—A review

The purpose of this paper is to provide a source of information on thermal energy use in buildings, its drivers, and their past, present and future trends on a global and regional basis. Energy use in buildings forms a large part of global and regional energy demand. The importance of heating and cooling in total building energy use is very diverse with this share varying between 18% and 73%. Biomass is still far the dominant fuel when a global picture is considered; the role of electricity is substantially growing, and the direct use of coal is disappearing from this sector, largely replaced by electricity and natural gas in the most developed regions. This paper identifies the different drivers of heating and cooling energy demand, and decomposes this energy demand into key drivers based on a Kaya identity approach: number of households, persons per household, floor space per capita and specific energy consumption for residential heating and cooling; and GDP, floor space per GDP, and specific energy consumption for commercial buildings. This paper also reviews the trends in the development of these drivers for the present, future – and for which data were available, for the past – in 11 world regions as well as globally. Results show that in a business-as-usual scenario, total residential heating and cooling energy use is expected to more or less stagnate, or slightly decrease, in the developed parts of the world. In contrast, commercial heating and cooling energy use will grow in each world region. Finally, the results show that per capita total final residential building energy use has been stagnating in the vast majority of world regions for the past three decades, despite the very significant increases in energy service levels in each of these regions.

Heating and cooling energy trends and drivers in buildings

In order to find the efficient ways to reduce carbon emission intensity in China, we utilize the LMDI method to decompose the changes of China׳s carbon emissions and carbon emission intensity from 1996 to 2010, from the perspectives of energy sources and industrial structure respectively. Then we introduce the decoupling index to analyze the decoupling relationship between carbon emissions and economic growth in China. The results indicate that, on the one hand, economic growth appeared as the main driver of carbon emissions increase in the past decades, while the decrease of energy intensity and the cleaning of final energy consumption structure played significant roles in curbing carbon emissions; meanwhile, the secondary industry proved the principal source of carbon emissions reduction among the three industries and had relatively higher potential. On the other hand, when the decoupling relationship is considered, most years during the study period saw the relative decoupling effect between carbon emissions and economic growth, which indicated that the reduction effect of inhibiting factors of carbon emissions was less than the driving effect of economic growth, and the economy grew with increased carbon emissions; there appeared the absolute decoupling effect in 1997, 2000 and 2001, which implied that the economy grew while carbon emissions decreased; whereas no decoupling effect was identified in 2003 and 2004.

The decomposition of energy-related carbon emission and its decoupling with economic growth in China

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A multivariate causality test of carbon dioxide emissions, energy consumption and economic growth in China

Factors that influence carbon emissions due to energy consumption in China: Decomposition analysis using LMDI

Decomposition of energy-related CO2 emission over 1991–2006 in China

Forecasting the transport energy demand based on PLSR method in China

China is now experiencing rapid development and has become the second largest energy consumer. China has presented the objectives of building a well off society in an all round way at the 16th National Congress of the Communist Party of China in 2002. Without question, the future society, with higher level of modernization and standard of living, will consume more energy. In this paper, the LMDI (Log-Mean Divisia Index) method is used to analyze the nature of the factors that influence energy consumption during the period 1980-2006. Another contribution of this paper is to develop a forecasting model (LMDIED) based on the LMDI method, in which the effects from each factor can be displayed.

Ming Zhang

Papers

Decomposition of energy-related CO2 emission over 1991–2006 in China

Forecasting the transport energy demand based on PLSR method in China

Analysis and Prediction of China's Energy Consumption Based on LMDI Method