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• The data requirements of quota methods are summarized, and a city-scale database containing 670 credible government office buildings is established. • By comparing the processes, the results and applicability, the selection basis of quota methods under different conditions is refined. • A flow chart of the formulation process and method selection of city-level building energy consumption quotas is constructed. • It is proposed that special attention should be paid to the outliers, and the characteristics of outliers in Chongqing are summarized. Energy consumption quota policies are effective management measures to promote energy conservation and emission reduction in China. Cities need to choose an appropriate method to formulate local energy consumption quota standards. This paper is based on the data of 670 government office buildings in Chongqing and the selected quantile method (QM), quota-level method (QLM) and multiple linear regression (MLR) method, which are widely used in the formulation of city-level energy quotas. By comparing their interpretability, evaluation, consistency and stability, the application conditions and applicability of each method are summarized. MLR has the strongest explanatory power but the highest requirements for the technology and information dimensions. The QLM is more explanatory and stable than the QM and is more suitable for the initial stage of energy quota management. When using the QLM, we should consider scientific classifications and reasonable quota levels. Special attention should be given to "outliers" with abnormal evaluations of the quota results. The outliers in Chongqing are characterized as buildings that have been used for more than 10 years and have an area of less than 5000 m2. Finally, this paper constructed a flow chart of the formulation process and method selection to help policymakers better understand how to choose the most appropriate method and identify inefficient buildings. [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]

• A novel three-stage decision-making approach for office buildings design considering 'Energy','Comfort', and 'Cost'. • The establishment of an optimization and decision-making model by applying NSGA-II and TOPSIS methods. • Two-year monitored data and post-occupancy evaluation for energy audit and thermal comfort. • The evidence of a case study demonstrates the feasibility of the proposed method and solutions. China, with the largest energy consumption system in the world, faces numerous challenges in achieving the government's commitment to reach a carbon-peak and carbon–neutral target. As the most common public building type in terms of floor area, office buildings have great potential for energy saving and emissions reduction. To meet this target, building designers target passive solutions that can meet the thermal comfort needs of occupants and also reduce energy consumption. This study aims to develop a decision-making method to select optimal solutions from among tens of thousands of design options considering the factors of energy consumption, comfort, and cost. We developed a novel optimization decision approach with the above-mentioned three objectives. The model consists of three stages: 1) the establishment of the reference building model, 2) sensitivity analysis to identify the main influencing variables and 3) the establishment of the optimization and decision-making model by applying NSGA-II and TOPSIS methods. By applying this three-stage decision-making model, this paper first proposes cost-effective passive design solutions for office buildings throughout the Hot Summer and Cold Winter climate zone. Finally, an office building in Shanghai was chosen as a case study to demonstrate the practical implementation of the proposed solutions through a post-occupancy evaluation with a two-year energy auditing and thermal comfort survey. It is evident that the proposed solutions provide support for the new low energy building design guide for office buildings along with necessary revisions to the existing standards for the Hot Summer and Cold Winter climate zone in China. [ABSTRACT FROM AUTHOR]

China building energy codes (CBEC) have been introduced for over two decades but little has been publicized in literature. LEED on the contrary is the most publicized building environmental assessment scheme. To enable better understanding of the compliance standards of CBEC, this paper presents the energy performance assessment results (represented by energy and energy cost savings) of three office buildings in China (one in Beijing and two in Shanghai) using the current versions of CBEC and LEED. The energy and energy cost savings of the three buildings were predicted based on hour-by-hour simulations using the weather data and energy tariffs of Beijing and Shanghai where the three studied buildings are located, and their actual building and system characteristics. The study revealed that LEED in general sets more stringent requirements than CBEC in indoor design conditions, building envelope characteristics and air-conditioning system features. Amongst various building end uses, energy use for air-conditioning was found dominating the assessment results, and the use of energy efficient measures not forming part of the baseline criteria, could lead to 2 to 5% reduction in the overall building energy use. The two schemes were benchmarked against BEAM Plus and their weaknesses were also unveiled. [ABSTRACT FROM AUTHOR]

A large part of energy consumption in homes, offices and commercial spaces is related to Heating, Ventilation and Air-conditioning (HVAC) devices. The effective parameter on the consumption of HVAC systems is internal heat gains that arise from occupants, electric equipment and lighting. In order to reduce the energy consumption of these systems, internal heat gains should be predicted accurately. Since there are few investigations performed on the prediction of internal heat gains, in this paper, three predictive models, namely multiple regression model, Levenberg–Marquardt back-propagation (LM-BP) model and similar days method based on combined weights, have been deployed. By assessing the influential factors on internal heat gains, fundamental theories, structures, equations and parameters of these models are thoroughly proposed. To examine the prediction techniques, an office building in China was considered. It was found that all the proposed models have high accuracy; however, the LM-BP neural network showed the most precision among other models with RMSE = 15.59, MAE = 10.16 and MAPE = 6.35. This model had a higher agreement with the actual internal heat gains compared to the predetermined working programs in the ASHRAE standard 90.1. The proposed models used in this study can lead to providing a theoretical base for scholars and engineers to improve the predictive control of HVAC systems, which plays an important role in enhancing thermal comfort, saving energy of residential buildings. [ABSTRACT FROM AUTHOR]

• A dual-objective optimization method from the perspective of designer and user. • Life-cycle energy and economy optimization for building Design Period based on HypE. • Use Low-E insulating glass, rotate the building 18°counterclockwise are beneficial. • Life cycle energy consumption and economy of the case are reduced by more than 15%. Multi-objective optimization of building life cycle energy consumption and cost is very essential for guiding design quantitatively. However, previous studies paid more attention to the potential effect of building energy saving and cost reduction based on design parameters from the perspective of designers, but ignored the potential brought by the willingness of users during operation stage. Therefore, this paper proposes a dual-objective optimization method based on the HypE algorithm, which takes into account users' energy saving intentions in order to maximize the potential of building life cycle energy saving and cost reduction. The application premise of this method is to establish energy and cost calculation model of building life cycle. The method was applied to the design of a small office building in Xi'an city of China, and the scheme were optimized by adjusting 7 design parameters and 2 parameters (cooling and heating temperature) related to the energy saving willingness of the users. The results show that the proposed method can reduce the energy consumption by 15.1%-18.8% and the cost by 2.2%-10.1% during life cycle. The results will continue to be reduced by up to 6.5% and 12.02% if the potential of uers' willingness to save energy continues to be realized. It is proved that the proposed method contributes to the comprehensive evaluation of energy saving and economic effects of architectural design during its life cycle and provides effective theoretical guidance and practical tools for architectural design and optimization. [ABSTRACT FROM AUTHOR]

Abstract: A benchmark study of the energy efficiency of private office buildings in Hong Kong was conducted in 2002 because energy efficiency was declining. In the study, private office buildings were divided into five user groups. For each group, a multiple regression model was developed to find the relationship between Energy Use Intensities (EUIs) and other factors, such as operating hours, for normalization and benchmarking purposes. In this paper we make use of the regression results to study the energy efficiency of private office buildings by different grades. In Hong Kong, office buildings are divided into three grades (A, B, and C) based on the quality of the facility, which is reflected in rental values; a Grade A office building denotes expensive luxury. We found that the EUI of Grade A office buildings is the highest, consuming over 50% of the total energy used in office buildings. Recently, the annual EUI of office buildings has improved even though Grade A floor space is increasing. This may be due to the promotion of the energy efficiency program launched in the last decade. [Copyright &y& Elsevier]

• We selected three benchmarking methods to establish building energy benchmarking models based on a sample of 45 hotel buildings in China. • We compared the three benchmarking methods in terms of consistency, robustness and explanatory ability. • The energy efficiency performance obtained from the three methods were found to be inconsistent. • We came up with suggestions based on comparative analysis for policy-makers to develop building energy benchmarking programs. A reasonable building energy efficiency benchmarking program plays an important role in energy consumption control and supervision. Previous studies have focused on the process of establishing a single benchmarking method, but few have compared the performances and outcomes of different methods. To fill this gap, this paper selects three benchmarking methods—multiple linear regression (MLR) based on Energy Star, stochastic frontier analysis (SFA) and the descriptive statistics method (DSM) based on the national energy consumption standard in China—to develop benchmarking models. We demonstrate each method using data on the energy and building characteristics of 45 four- and five-star hotel buildings located in Chongqing, China. To compare the consistency, robustness and explanatory ability of the three methods, we first utilize the Spearman rank correlation analysis to test whether these methods have consistent energy efficiency ranks and then present Sankey diagrams to further reveal the interactions of the estimated energy efficiency grades obtained from the three methods. It is found that the results of DSM and SFA are most consistent, while MLR vs. SFA and MLR vs. DSM present significant differences in evaluating building energy performance. In addition, DSM is more robust for evaluating the ranks of sampled buildings, while SFA is more robust for evaluating energy efficiency grades. Furthermore, we discuss the explanatory ability of each method. In addition to the building characteristics, the design and operational characteristics of the HVAC system have great effects on building energy consumption. Finally, we present suggestions for policy-makers regarding the development and implementation of the building energy benchmarking program in Chongqing and for the management of buildings with different energy performances to further improve the energy efficiency. [ABSTRACT FROM AUTHOR]