The performance and analysis of office building energy consumption in the west of Inner Mongolia Autonomous Region, China

2016 ◽  
Vol 127 ◽  
pp. 499-511 ◽  
Author(s):  
Shilei Lu ◽  
Shaoqun Zheng ◽  
Xiangfei Kong
2013 ◽  
Vol 409-410 ◽  
pp. 606-611 ◽  
Author(s):  
Zhen Yu ◽  
Wei Lin Zhang ◽  
Ting Yong Fang

Using the energy consumption simulation software to research the HVAC in fall air conditioning mode, different building orientation and window-wall ratio of the office building energy consumption. The study found that the heating energy consumption, air-conditioning energy consumption and total energy consumption is gradually increased with the increase of the window-wall ratio under the same orientation. The result provides some reference for public buildings in setting of building orientation and window-wall ratio.


Energies ◽  
2020 ◽  
Vol 13 (12) ◽  
pp. 3210 ◽  
Author(s):  
Chao Ding ◽  
Nan Zhou

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 in Wuhan. 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 high- level 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.


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