Energy assessment of office buildings in China using China building energy codes and LEED 2.2

Chen, Hua; Lee, Wai Ling; Wang, Xiaolin

doi:10.1016/j.enbuild.2014.10.034

Cited by 33 publications

(7 citation statements)

References 13 publications

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“…Chen et al [62] studied three LEED-certified office buildings in China in order to assess their energy performance. One of these buildings was located in Beijing and two in Shanghai.…”

Section: General Findingsmentioning

confidence: 99%

Are LEED-Certified Buildings Energy-Efficient in Practice?

2019

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Depletion of natural resources and climate change are undoubtedly the biggest challenges that humankind faces today. Here, buildings have a crucial role since they consume the majority, i.e., 30% to 40% of the total energy resources. Green building certification is one of the solutions to limit the energy use in buildings. In addition, it is seen to indicate a consideration for sustainability aspects in construction. LEED is the most widely used certificate worldwide. However, recently some critics have raised doubts about LEED and whether it actually implies sustainability. Most of the criticism has been targeted to the energy aspects of LEED. Nevertheless, there is no consensus on the usefulness of LEED: is it really beneficial for the environment, and is it worth of the money and time invested on the certification process? In this study a critical analysis of the literature to find an answer to this question is presented. Altogether 44 peer reviewed articles dealing with the abovementioned issue were selected out of 164 search result. Based on the studied material, the different aspects of LEED from the viewpoint of energy-efficiency are discussed. From the 44 reviewed articles, ten articles state that LEED certificate indicates energy efficiency while eight papers end up with an opposite conclusion. The rest of the papers do not take any stand on this matter. The study showed that energy efficiency of LEED-certified buildings is questionable especially at lower levels, i.e., certified. Therefore, it is recommended to modify the Energy and Atmosphere category of LEED in order to improve the actual energy performance of buildings.

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“…Chen et al [62] studied three LEED-certified office buildings in China in order to assess their energy performance. One of these buildings was located in Beijing and two in Shanghai.…”

Section: General Findingsmentioning

confidence: 99%

Are LEED-Certified Buildings Energy-Efficient in Practice?

2019

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“…The CAV system was analyzed for purposes of comparison with the energy-conserving system and the conventional VAV system. The CAV always supplies a fixed amount of air to the indoors and adjusts the temperature via heat exchange in the coils within the air conditioner [49,50]. The UFAD system ( Figure 8C) is an air-conditioning method that cools by focusing on the occupied zone, which is the space between the floor surface and a height of approximately 2 m [51,52].…”

Section: Selection Of Hvac System and Simulation Input Conditionsmentioning

confidence: 99%

Detailed Comparison of the Operational Characteristics of Energy-Conserving HVAC Systems during the Cooling Season

2019

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To provide useful information concerning energy-conserving heating, ventilation, and air-conditioning (HVAC) systems, this study used EnergyPlus to analyze in detail their operational characteristics and energy performance. This study also aimed to understand the features of the systems under consideration by investigating the dry-bulb temperature, relative humidity, and airflow rate at major nodes in each system’s schematic. Furthermore, we analyzed the indoor environment created by each HVAC system, as well as examining the cooling energy consumptions and CO2 emissions. The HVAC systems selected for this study are the variable air volume (VAV) commonly used in office buildings (base-case model), constant air volume (CAV), under-floor air distribution (UFAD), and active chilled beam (ACB) with dedicated outdoor air system (DOAS). For the same indoor set-point temperature, the CAV’s supply airflow was the highest, and VAV and UFAD were operated by varying the airflow rate according to the change of the space thermal load. ACB with DOAS was analyzed as being able to perform air conditioning only with the supply airflow constantly fixed at a minimum outdoor air volume. The primary cooling energy was increased by about 23.3% by applying CAV, compared to VAV. When using the UFAD and ACB with DOAS, cooling energy was reduced by 11.3% and 23.1% compared with VAV, respectively.

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“…where , , are unknown constant parameters. and on the right of (2) can be replaced by (3) and 4, and the following can be obtained.…”

Section: Calculation Of Co 2 Emissionsmentioning

confidence: 99%

“…With the acceleration of urbanization and industrialization, the average annual growth rate of China's building energy consumption has exceeded 10% in the past two decades [2]. And, Chen et al noted that the construction industry contributed 28% of China's total CO 2 emissions in 2011 and projected to be 35% by 2020, with construction industry playing an increasingly large role [3]. Considering the increasing impact of construction industry on CO 2 emissions, it is necessary to study the factors affecting CO 2 emissions in construction industry and predict CO 2 emissions in construction industry to provide recommendations to policy makers.…”

Section: Introductionmentioning

confidence: 99%

Forecasting CO₂ Emissions in China’s Construction Industry Based on the Weighted Adaboost-ENN Model and Scenario Analysis

Zhou

et al. 2019

Journal of Energy

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As a pillar industry of national economy, China’s construction industry is still facing the status of substantial energy consumption and high CO2 emissions, which is a key field of energy conservation and emission reduction. In CO2 emissions research, it is essential to focus on analyzing the present and future trends of CO2 emissions in China’s construction industry. This article introduces a novel prediction model, in which the weighted algorithm is combined with Elman neural network (ENN) optimized by Adaptive Boosting algorithm (Adaboost) for evaluating future CO2 emissions in China’s construction industry. Firstly, logarithmic mean Divisia index (LMDI) is used to decompose CO2 emissions into economy, structural, intensity, and population indicators, posing as inputs to the weighted Adaboost-ENN model. Then, through comparison with other three models based on the data of total CO2 emissions in China’s construction industry during 2004-2016, there is evidence that the proposed model makes a favorable prediction performance. On this basis, we employ scenario analysis to predict future trend of CO2 emissions in China’s construction industry. It can be found that the peak of CO2 emissions in China’s construction industry will be achieved before 2030 in high carbon scenario (HS) and baseline carbon scenario (BS), whereas it will not be realized in low carbon scenario (LS). Finally, the specific policy recommendations related to energy conservation and emission reduction in China’s construction industry are proposed.

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Energy assessment of office buildings in China using China building energy codes and LEED 2.2

Cited by 33 publications

References 13 publications

Are LEED-Certified Buildings Energy-Efficient in Practice?

Are LEED-Certified Buildings Energy-Efficient in Practice?

Detailed Comparison of the Operational Characteristics of Energy-Conserving HVAC Systems during the Cooling Season

Forecasting CO₂ Emissions in China’s Construction Industry Based on the Weighted Adaboost-ENN Model and Scenario Analysis

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Energy assessment of office buildings in China using China building energy codes and LEED 2.2

Cited by 33 publications

References 13 publications

Are LEED-Certified Buildings Energy-Efficient in Practice?

Are LEED-Certified Buildings Energy-Efficient in Practice?

Detailed Comparison of the Operational Characteristics of Energy-Conserving HVAC Systems during the Cooling Season

Forecasting CO2 Emissions in China’s Construction Industry Based on the Weighted Adaboost-ENN Model and Scenario Analysis

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Forecasting CO₂ Emissions in China’s Construction Industry Based on the Weighted Adaboost-ENN Model and Scenario Analysis