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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]

• 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]

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]