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Using Residential and Office Building Archetypes for Energy Efficiency Building Solutions in an Urban Scale: A China Case Study

Journal Article · · Energies (Basel)
DOI:https://doi.org/10.3390/en13123210· OSTI ID:1695727
 [1];  [1]
  1. Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)
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.
Research Organization:
Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States)
Sponsoring Organization:
Energy Foundation China; USDOE Office of Science (SC)
Grant/Contract Number:
AC02-05CH11231
OSTI ID:
1695727
Journal Information:
Energies (Basel), Journal Name: Energies (Basel) Journal Issue: 12 Vol. 13; ISSN 1996-1073
Publisher:
MDPI AGCopyright Statement
Country of Publication:
United States
Language:
English

References (9)

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Automatic generation and simulation of urban building energy models based on city datasets for city-scale building retrofit analysis journal November 2017
Data-driven Urban Energy Simulation (DUE-S): A framework for integrating engineering simulation and machine learning methods in a multi-scale urban energy modeling workflow journal September 2018
A roadmap for China to peak carbon dioxide emissions and achieve a 20% share of non-fossil fuels in primary energy by 2030 journal April 2019
Inter-building effect: Simulating the impact of a network of buildings on the accuracy of building energy performance predictions journal December 2012
Urban building energy modeling – A review of a nascent field journal February 2016
Modeling Boston: A workflow for the efficient generation and maintenance of urban building energy models from existing geospatial datasets journal December 2016
Scenarios of energy efficiency and CO2 emissions reduction potential in the buildings sector in China to year 2050 journal October 2018

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