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[en] Highlights: ► Fuzzy linear regression method is used for developing benchmarking systems. ► The systems can be used to benchmark energy efficiency of commercial buildings. ► The resulting benchmarking model can be used by public users. ► The resulting benchmarking model can capture the fuzzy nature of input–output data. -- Abstract: Benchmarking systems from a sample of reference buildings need to be developed to conduct benchmarking processes for the energy efficiency of commercial buildings. However, not all benchmarking systems can be adopted by public users (i.e., other non-reference building owners) because of the different methods in developing such systems. An approach for benchmarking the energy efficiency of commercial buildings using statistical regression analysis to normalize other factors, such as management performance, was developed in a previous work. However, the field data given by experts can be regarded as a distribution of possibility. Thus, the previous work may not be adequate to handle such fuzzy input–output data. Consequently, a number of fuzzy structures cannot be fully captured by statistical regression analysis. This present paper proposes the use of fuzzy linear regression analysis to develop a benchmarking process, the resulting model of which can be used by public users. An illustrative example is given as well.
[en] Highlights: • The activity effect accounts for 98.05% increase in energy use. • Only Eastern’s structural effect contributes energy savings. • Intensity effect contributes energy saving in −4.24% of total energy changes. • Energy-mix effect is insignificant. - Abstract: As one of the three high-energy consumption sectors (industry, building, and transportation) in China, the transport sector faced a devastating resource and environment challenge. The transport sector was reportedly responsible for about 15.9% of the country’s total final energy consumption in 2008. This paper investigates the energy consumption and efficiency of China’s transport sector from 2003 to 2009. The transport energy data of 30 China administrative regions were divided into “three-belts” (Eastern, Western, and Central areas), and the corresponding turnovers were reported and analyzed using an index decomposition analysis (Logarithmic Mean Divisia Index). The energy data and turnover of the transport sector indicated that the high growth rate of turnover results is attributed to the high growth rate of diesel, assuming that diesel is the major fuel for freight transport. The growth of diesel is the main contributor to the overall growth of energy consumption. The growth rate of gasoline has become minimal since 2006. Since 2005, all three-belt areas, with regard to the effectiveness of energy conservation policies, have continuously improved their energy efficiencies based on the results of decomposition analysis. The energy intensity effect shows that the energy conservation and efficiency policies were more effective in the Central and Western areas than that in the Eastern area. On the other hand, the regional shift effect indicates that the policies favor to the Eastern area since only its regional shift effect contributes energy savings since 2008. The energy-mix effect is insignificant, which indicates that it is not necessary to conduct CO2 emission decomposition analysis due to the redundant observations. At last, the activity effect dominates the energy consumption increase (98.05% of the accumulated total energy increase) from 2003 to 2009, which is consistent with the rapid development of transportation in China. That is, the policies in the transport sector mainly focused on the economic development but the energy efficiencies in the study period
[en] Benchmarking energy-efficiency is an important tool to promote the efficient use of energy in commercial buildings. Benchmarking models are mostly constructed in a simple benchmark table (percentile table) of energy use, which is normalized with floor area and temperature. This paper describes a benchmarking process for energy efficiency by means of multiple regression analysis, where the relationship between energy-use intensities (EUIs) and the explanatory factors (e.g., operating hours) is developed. Using the resulting regression model, these EUIs are then normalized by removing the effect of deviance in the significant explanatory factors. The empirical cumulative distribution of the normalized EUI gives a benchmark table (or percentile table of EUI) for benchmarking an observed EUI. The advantage of this approach is that the benchmark table represents a normalized distribution of EUI, taking into account all the significant explanatory factors that affect energy consumption. An application to supermarkets is presented to illustrate the development and the use of the benchmarking method