Building energy consumption accounts for 36% of the overall energy end use worldwide and is growing rapidly as developing countries continue to urbanize. Understanding the energy use at urban scale will lay the foundation for identification of energy efficiency opportunities to be deployed at speed. China has almost half of global new constructions and plays an important role in building suitability. However, an open source national building energy consumption database is not available in China. To provide data support for building energy consumptions, this paper used a simulation method to develop an urban building energy consumption database for a pilot city in Wuhan, China. First, residential, small, and large office building archetype energy models were created in EnergyPlus to represent typical building energy consumption inWuhan. The baseline reference model simulation results were further validated using survey data from the literature. Second, stochastic simulations were conducted to consider different design parameters and occupants' energy usage intensity scenarios, such as thermal properties of the building envelope, lighting power density, equipment power density, HVAC (heating, ventilation and air conditioning) schedule, etc. A building energy consumption database was generated for typical building archetypes. Third, data-driven regression analysis was conducted to support quick building energy consumption prediction using key highlevel building information inputs. Finally, a web-based urban energy platform and an interface were developed to support further third-party application development. The research is expected to provide fast energy efficiency building design solutions for urban planning, new constructions as well as building retrofits.

Effective urban ventilation through decent urban planning and building design can alleviate the deterioration of the urban built environment. However, natural ventilation requirements and guidelines in current building codes and standards are either qualitative or quantitative but subject to an absolute indoor airspeed threshold without considering the outdoor wind environment. To fill this gap, this paper develops an urban-scale coupled indoor and outdoor computational fluid dynamics (CFD) model and defines a novel ventilation index to assess natural ventilation potential. The index considers wind environments of both indoor and outdoor spaces. First, the coupled CFD model is developed to study wind-driven cross ventilation in high-density cities. A 3D isothermal CFD simulation is solved using the RNG k-ε turbulent model. The simulation results are compared with wind tunnel experiment data from literature. Second, six key design variables are used to generate 3840 parametric design variations for natural ventilation assessment. Third, a novel integrated index CIOIv (coupled indoor and outdoor interaction) is proposed to evaluate the wind speed ratio between the indoor area and outdoor reference area. For demonstration, CIOIv,F1 is used to represent CIOIv on the ground level. Data-driven CIOIv,F1 models are developed to predict indoor building ventilation potential for quick early design support. Compared with multivariate linear regression model, the Gradient Boosting non-linear model displays much higher prediction accuracy (mean absolute percentage error = 0.16 with R2 = 0.8). In early design stage, designers and engineers can skip the computational expensive CFD simulations and use this data-drive model to quickly check the building natural ventilation potentials of different design options in an urban environment.

In this paper, we provide two types of sufficient conditions for ensuring the quadratic growth conditions of a class of constrained convex symmetric and non-symmetric matrix optimization problems regularized by nonsmooth spectral functions. These sufficient conditions are derived via the study of the $\mathcal{C}^2$-cone reducibility of spectral functions and the metric subregularity of their subdifferentials, respectively. As an application, we demonstrate how quadratic growth conditions are used to guarantee the desirable fast convergence rates of the augmented Lagrangian methods (ALM) for solving convex matrix optimization problems. Numerical experiments on an easy-to-implement ALM applied to the fastest mixing Markov chain problem are also presented to illustrate the significance of the obtained results.

Improving the efficiency of room air conditioners (RACs) could provide significant energy and associated emissions savings, particularly in emerging economies with hot climates where the cooling demand is expected to increase dramatically. To help accelerate efficiency improvements, this study identifies “best-in-class” high-efficiency RAC components and products. The findings show that manufacturers tend to minimize manufacturing costs by using RAC designs that are readily available or standardized to their production, and they share components across various models. High-efficiency RAC models use advanced compressor technologies optimized at a low frequency, large heat exchangers with thermodynamically effective materials and designs, highly efficient direct current fan motors, advanced metering devices, and smart sensors for temperature and humidity control. Recently RAC manufacturers have been improving seasonal efficiency – better reflecting part-load operation – especially via variable-speed (inverter) drives for compressor motors. Recent highest-efficiency RAC models use low global warming potential (GWP) refrigerants, having transitioned from conventional high-GWP refrigerants, in regions where RACs that use these low-GWP refrigerants are commercially available. Recently demonstrated innovative technologies show trends toward smart hybrid designs, evaporative cooling, and solid-state materials beyond the conventional vapor-compression technology. This information could help policymakers improve their RAC market-transformation programs to align with the most-efficient global technology.

Fires in tunnels have attracted special attention in recent years due to catastrophic fires, which cause huge human and economic losses. For accurate fire modelling and ventilation system design, it is critical to understand the correct radiative fraction (Xr) of the flame. Recent study has proved that Xr decreases with the increase of longitudinal air velocity in heptane pool fires. However, the impacts of longitudinal air velocity and fuel flow rate on Xr in propane or liquefied petroleum gas tunnel fires have not been studied. To fill the gap, this paper conducts two sets of fire experiments in a 1/20 reduced-scale wind tunnel using a porous burner. Unique visible flame shapes are observed and described by the ‘back-to-back conical frustum’. The geometric parameters and their variations with the momentum flux ratio are discussed. In comparison with the experimental results, the radiation model using the proposed flame shape shows an 80 % accuracy. The predictive radiative fractions of the two tests are calculated and compared with other tests from literature. The coupling effects of longitudinal air velocity and fuel flow rate on the flame radiative fraction are studied in detail. And their influences on key parameters of smoke extraction system are analyzed.

Increasing incomes, electrification, and urbanization—as well as a warming world—are driving up the global stock of air conditioners (ACs), particularly in emerging economies with hot climates. AC energy consumption is expected to increase substantially as the global stock of room ACs rises to 1.5 billion in 2030 and 2.5 billion in 2050. Hence, improving AC energy efficiency will be critical to reducing AC energy, cost (consumer lifecycle cost, electricity generation cost, etc.), peak load, and emissions impacts. The 2016 Kigali Amendment to the Montreal Protocol offers an opportunity to improve AC energy efficiency in tandem with the phasedown of high global warming potential (GWP) hydrofluorocarbon (HFC) refrigerants. Based on the most recent information, a literature review, and interviews with manufacturers and industry experts, we find the main barriers to deploying high-efficiency ACs include concerns about market demand and cost, which could be mitigated by appropriately improved design of market-transformation programs such as standards and labeling, incentive, and procurement programs. The main barriers to the low-GWP refrigerant transition include the need for timely revision of safety standards and associated costs for capacity-building activities allowing safe use of low-GWP refrigerants in ACs. Policy action and the market transformation can be accelerated by advancing the refrigerant transition and efficiency improvements in parallel.

Building energy consumption accounts for 30% of the overall energy end use worldwide. This number is even much higher in urban areas. With rapid urbanization in China, cities are expanding with new constructions. It is essential to create an updated urban-scale building energy consumption database to represent energy use for different types of buildings in China, which could help urban planners, managers and decision makers to understand temporal and spatial building energy consumption distribution and ensure required electricity and/or gas supply. However, such urban-scale database is rarely found in China. This paper creates baseline EnergyPlus models for residential, small office and large office buildings and validates the baseline models using survey data from literature. Parametric simulations are conducted to consider different design factors, such as building enclosure, lighting power density, equipment power density, HVAC schedule, etc. In total 351 EnergyPlus models are generated to cover different energy use intensity scenarios. Data-driven regression analysis is conducted to predict building energy consumption using building price/rent. The prediction results are expected to provide design decision support for urban planning and power distribution for new constructions.

Artificial intelligence has emerged as a technology to enhance productivity and improve life quality. However, its role in building energy efficiency and carbon emission reduction has not been systematically studied. This study evaluated artificial intelligence's potential in the building sector, focusing on medium office buildings in the United States. A methodology was developed to assess and quantify potential emissions reductions. Key areas identified were equipment, occupancy influence, control and operation, and design and construction. Six scenarios were used to estimate energy and emissions savings across representative climate zones. Here we show that artificial intelligence could reduce cost premiums, enhancing high energy efficiency and net zero building penetration. Adopting artificial intelligence could reduce energy consumption and carbon emissions by approximately 8% to 19% in 2050. Combining with energy policy and low-carbon power generation could approximately reduce energy consumption by 40% and carbon emissions by 90% compared to business-as-usual scenarios in 2050.

This report reviews the latest global and China-specific market trends as well as latest technology and policy trends related to commercial refrigeration equipment, with emphasis on refrigerated display cabinets. Section 2 reviews the global and China market trends for commercial refrigeration equipment. Section 3 presents the latest technology options for improving energy performance and compares global Best Available Technologies (BAT) to regional and China-specific baselines, and discusses low-GWP refrigerant options for commercial refrigeration equipment. Section 4 reviews global energy efficiency standards and labeling programs, and compares international efficiency standard requirements with China’s proposed and existing standard requirements. Section 5 ends with a review of key findings and conclusions.