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Facing the challage of energy conservation and emission reduction in the building sector, most energy saving strategies of central heating only focus on the performance improvement of building envelope, ignoring the impact of energy use behavior. This paper proposed a building energy conservation strategy based on energy use behavior analysis, which provides a perspective for in-depth exploration of building energy conservation. After investigating a typical university in the northern China, the energy use behaviors were characterized and two different DeST energy consumption models before or after considering energy use behavior were established. By comparing the results calculated by these two models, the impact of energy use behavior analysis on building energy consumption was quantitatively analyzed. The results show that the heating energy consumption and carbon emission can be reduced by 23.28% and 23% respectively through the proposed strategy based on energy use behavior. Therefore, the analysis of energy use behavior can provide a theoretical basis for heating operators to make decisions on energy supply regulation. With central cooling and heating further popularizing in the future, the research methodology of this paper has more extensive reference value for energy conservation and emission reduction strategies in the building sector. [ABSTRACT FROM AUTHOR]

• Obtaining the optimal interpolation method for adjacent sites. • Proposing the method for revising meteorological data at city scale. • Verifying the accuracy of the method by energy consumption simulation. In the development process of building energy conservation design, the correct provision of local Typical Meteorological Year (TMY) data is a prerequisite and basic work to evaluate building energy consumption, which directly affects the fine use of outdoor data for building energy conservation. Based on the recorded and observed data of meteorological stations in China, the acquisition and correction methods of TMY were studied by mathematical statistics and target searching, and the building energy consumption simulation of urban office buildings was conducted to verify the accuracy of the method, including:(1) Reviewed the generation method for TMY and the acquisition of data.(2) Based on the existing TMY data, the best spatial interpolation method of adjacent stations is studied by using the standard deviation method, and the selection method of the best number of stations on a large regional scale is given.(3) An improved method of urban environment TMY is proposed. The dry bulb temperature and relative humidity of the building site are corrected to generate TMY under Urban Heat Island (UHI) effect. The research in this paper improves the coverage and accuracy of urban TMY data and supplies basic research support for building energy-saving designs. [ABSTRACT FROM AUTHOR]

In order to resolve the contradiction between the lack of building energy data and the need of more detailed information about energy performance, this paper presents a simplified building energy performance diagnosis method which can assess the energy performance from building level to system level with only limited information. Based on multiple-parameter regression between the whole building energy bills and outdoor weather data, the proposed diagnosis method uses the regression coefficients to identify the energy use of main systems (i.e., lighting-plug system and cooling/heating system), as well as other detailed information about physical properties of the building and cooling/heating system (i.e., cooling or heating load, the operation of cooling/heating system and the efficiency of cooling/heating system). One case study was conducted in an office building in China to test the application of this diagnosis method. In addition, the regression result of simulated energy consumption served as benchmark data to further diagnose the performance gap between the operation stage and the design stage and help locate the poor performance and key points of energy-saving. Finally, all diagnosis results have been verified by the performance data from advanced energy consumption monitoring system together with field surveys and measurements. [ABSTRACT FROM AUTHOR]

The large public building energy consumption (BEC) is the focus of Building Energy Saving, therefore, it is necessary to study the characteristics of BEC to find out the important factors affecting BEC. For this purpose, 27 office buildings in the west of Inner Mongolia Autonomous Region were studied, based on statistical analysis of the researched basic information and energy consumption bill of these buildings. This paper focused on the determination of the significant factors affecting the total and subentry energy consumption intensity (ECI) of office building, as well as the establishment of standardized linear regression models between these selected factors and total and subentry ECI. Firstly, eleven continuous variables, three independent categorical variables, and the climate factor were selected and analyzed the impact on total and subentry ECI by statistical software SPSS20.0, in order to find out the significant influencing factors. Then based on the results of curve fitting, standardized models of total and subentry ECI and their respective significant impact factors were established using multiple linear regression analysis. The regression results showed that the electricity use percentage of the total equivalent electricity consumption was an important factor affecting the total ECI of office building in the west of Inner Mongolia Autonomous Region. Finally, univariate analysis of variance (ANOVA) was conducted between the three independent categorical variables and the total and subentry ECI, and the results showed that these factors had no significant effect on the ECI of office building. Process of the regressive model establishment and the results of analysis of variance could both guide us to propose more targeted energy saving measures. [ABSTRACT FROM AUTHOR]

China is currently experiencing rapid urbanization. The building sector plays an important role in China's energy conservation and emission reduction. An increasing amount of energy will be steadily consumed in the building sector as the urbanization process accelerates. China's government attaches great importance to building energy efficiency (BEE). How does the urbanization process affect building energy consumption (BEC)? How much energy can be saved in the building sector through implementation of energy polices? These questions are critical for government development strategies and planning. Thus, in this paper, an econometric model (co-integration technique) is developed to analyze how China's BEC is affected by macroeconomic variables in the context of urbanization, and also forecast the BEC and BEE potential until 2020 under different scenarios. The research result shows that occupants’ lifestyles, residents’ living standards, and energy price have significant influences on BEC. The building sector is likely to contribute approximately 20% to China's energy conservation through electricity pricing reform. [ABSTRACT FROM AUTHOR]

Abstract: Hainan province is the only tropical and island province in China, with the unique climatic and geographical features. It has a different energy performance in public buildings from those in the inland areas. This paper investigated the current situation of energy consumption of the sampled public buildings, including 26 hotels, 24 office buildings and 7 department stores, through analyzing utility billing data, on-site survey and measurement, and site operational records. The breakdowns of energy consumption found out that the electricity dominated the energy consumption for all the sampled buildings and the air conditioning system played a key role in the energy consumption of major building services. By analyzing the main factors for building energy consumption, the annual average energy use indexes were calculated to be 141.63kWh/m2, 87.70kWh/m2, and 166.95kWh/m2 for hotels, office buildings and department stores, respectively. Besides, the comparison between Hainan province and other areas revealed that the building energy consumption of this tropical province was at a low level. Because indoor environment quality can influence the health and work efficiency of humans in buildings, field tests on the indoor environment quality were also carried out, and the performance of indoor environment quality was rated. [Copyright &y& Elsevier]