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Urban building energy modeling has become an efficient way to understand urban building energy use and explore energy conservation and emission reduction potential. This paper introduced a method to identify archetype buildings and generate urban building energy models for city-scale buildings where public building information was unavailable. A case study was conducted for 68, 966 buildings in Changsha city, China. First, clustering and random forest methods were used to determine the building type of each building footprint based on different GIS datasets. Then, the convolutional neural network was employed to infer the year built of commercial buildings based on historical satellite images from multiple years. The year built of residential buildings was collected from the housing website. Moreover, twenty-two building types and three vintages were selected as archetype buildings to represent 59, 332 buildings, covering 87.4% of the total floor area. Ruby scripts leveraging on OpenStudio-Standards were developed to generate building energy models for the archetype buildings. Finally, monthly and annual electricity and natural gas energy use were simulated for the blocks and the entire city by EnergyPlus. The total electricity and natural gas use for the 59, 332 buildings was 13, 864 GWh and 23.6 × 106 GJ. Three energy conservation measures were evaluated to demonstrate urban energy saving potential. The proposed methods can be easily applied to other cities in China.
Urban building energy modeling has become an efficient way to understand urban building energy use and explore energy conservation and emission reduction potential. This paper introduced a method to identify archetype buildings and generate urban building energy models for city-scale buildings where public building information was unavailable. A case study was conducted for 68, 966 buildings in Changsha city, China. First, clustering and random forest methods were used to determine the building type of each building footprint based on different GIS datasets. Then, the convolutional neural network was employed to infer the year built of commercial buildings based on historical satellite images from multiple years. The year built of residential buildings was collected from the housing website. Moreover, twenty-two building types and three vintages were selected as archetype buildings to represent 59, 332 buildings, covering 87.4% of the total floor area. Ruby scripts leveraging on OpenStudio-Standards were developed to generate building energy models for the archetype buildings. Finally, monthly and annual electricity and natural gas energy use were simulated for the blocks and the entire city by EnergyPlus. The total electricity and natural gas use for the 59, 332 buildings was 13, 864 GWh and 23.6 × 106 GJ. Three energy conservation measures were evaluated to demonstrate urban energy saving potential. The proposed methods can be easily applied to other cities in China.
Ang YQ, Berzolla ZM, Reinhart CF (2020). From concept to application: A review of use cases in urban building energy modeling. Applied Energy, 279: 115738.
Buckley N, Mills G, Reinhart C, et al. (2021). Using urban building energy modelling (UBEM) to support the new European Union's Green Deal: Case study of Dublin Ireland. Energy and Buildings, 247: 111115.
Cerezo Davila C, Reinhart CF, Bemis JL (2016). Modeling Boston: A workflow for the efficient generation and maintenance of urban building energy models from existing geospatial datasets. Energy, 117: 237–250.
Changsha Bureau of Statistics (2019). 2019 Changsha Statistical Yearbook. Beijing: China Statistics Press. (in Chinese)
Chen Y, Hong T, Piette MA (2017). Automatic generation and simulation of urban building energy models based on city datasets for city-scale building retrofit analysis. Applied Energy, 205: 323–335.
Chen Y, Hong T, Luo X, et al. (2019). Development of city buildings dataset for urban building energy modeling. Energy and Buildings, 183: 252–265.
Chen Y, Deng Z, Hong T (2020). Automatic and rapid calibration of urban building energy models by learning from energy performance database. Applied Energy, 277: 115584.
Ding C, Zhou N (2020). Using residential and office building archetypes for energy efficiency building solutions in an urban scale: A China case study. Energies, 13: 3210.
Ding Y, Han S, Tian Z, et al. (2022). Review on occupancy detection and prediction in building simulation. Building Simulation, 15: 333–356.
Deng Z, Chen Y, Pan X, et al. (2021). Integrating GIS-based point of interest and community boundary datasets for urban building energy modeling. Energies, 14: 1049.
Fan C, Yan D, Xiao F, et al. (2021). Advanced data analytics for enhancing building performances: From data-driven to big data-driven approaches. Building Simulation, 14: 3–24.
Fernandez J, del Portillo L, Flores I (2020). A novel residential heating consumption characterisation approach at city level from available public data: Description and case study. Energy and Buildings, 221: 110082.
Ferrando M, Causone F, Hong T, et al. (2020). Urban building energy modeling (UBEM) tools: A state-of-the-art review of bottom-up physics-based approaches. Sustainable Cities and Society, 62: 102408.
Happle G, Fonseca JA, Schlueter A (2018). A review on occupant behavior in urban building energy models. Energy and Buildings, 174: 276–292.
Hong T, Chen Y, Luo X, et al. (2020). Ten questions on urban building energy modeling. Building and Environment, 168: 106508.
Issermann M, Chang FJ, Kow PY (2021). Interactive urban building energy modelling with functional mockup interface of a local residential building stock. Journal of Cleaner Production, 289: 125683.
Li W, Zhou Y, Cetin KS, et al. (2018a). Developing a landscape of urban building energy use with improved spatiotemporal representations in a cool-humid climate. Building and Environment, 136: 107–117.
Li X, Yao R, Liu M, et al. (2018b). Developing urban residential reference buildings using clustering analysis of satellite images. Energy and Buildings, 169: 417–429.
Li Y, Wang C, Zhu S, et al. (2020a). A comparison of various bottom-up urban energy simulation methods using a case study in Hangzhou, China. Energies, 13: 4781.
Li Z, Lin B, Zheng S, et al. (2020b). A review of operational energy consumption calculation method for urban buildings. Building Simulation, 13: 739–751.
Liu Y, Tian W, Zhou X (2021). Energy and carbon performance of urban buildings using metamodeling variable importance techniques. Building Simulation, 14: 535–547.
Mohammadiziazi R, Copeland S, Bilec MM (2021). Urban building energy model: Database development, validation, and application for commercial building stock. Energy and Buildings, 248: 111175.
MOHURD (2001). JGJ 134-2001: Design Standard for Energy Efficiency of Residential Buildings in Hot Summer and Cold Winter Zone. Ministry of Housing and Urban–Rural Development of China. Beijing: China Architecture & Building Press. (in Chinese)
MOHURD (2003). GB 50096-1999: Design Code for Residential Buildings. Ministry of Housing and Urban–Rural Development of China. Beijing: China Architecture & Building Press. (in Chinese)
MOHURD (2004). GB 50034-2004: Standard for Lighting Design of Buildings. Ministry of Housing and Urban–Rural Development of China. Beijing: China Architecture & Building Press. (in Chinese)
MOHURD (2005). GB 50189-2005: Design Standard for Energy Efficiency of Public Buildings. Ministry of Housing and Urban– Rural Development of China. Beijing: China Architecture & Building Press. (in Chinese)
MOHURD (2010). JGJ 134-2010: Design Standard for Energy Efficiency of Residential Buildings in Hot Summer and Cold Winter Zone. Ministry of Housing and Urban–Rural Development of China. Beijing: China Architecture & Building Press. (in Chinese)
MOHURD (2013). GB 50034-2013: Standard for Lighting Design of Buildings. Ministry of Housing and Urban–Rural Development of China. Beijing: China Architecture & Building Press. (in Chinese)
MOHURD (2015). GB 50189-2015: Design Standard for Energy Efficiency of Public Buildings. Ministry of Housing and Urban– Rural Development of China. Beijing: China Architecture & Building Press. (in Chinese)
MOHURD (2016). GB 50176-2016: Code for Thermal Design of Civil Building. Ministry of Housing and Urban–Rural Development of China. Beijing: China Architecture & Building Press. (in Chinese)
MOHURD (2018). JGJ/T 449-2018: Standard for Green Performance Calculation of Civil Buildings. Ministry of Housing and Urban– Rural Development of China. Beijing: China Architecture & Building Press. (in Chinese)
Monteiro CS, Costa C, Pina A, et al. (2018). An urban building database (UBD) supporting a smart city information system. Energy and Buildings, 158: 244–260.
Pasichnyi O, Wallin J, Kordas O (2019). Data-driven building archetypes for urban building energy modelling. Energy, 181: 360–377.
Reinhart CF, Cerezo Davila C (2016). Urban building energy modeling—A review of a nascent field. Building and Environment, 97: 196–202.
Tardioli G, Kerrigan R, Oates M, et al. (2018). Identification of representative buildings and building groups in urban datasets using a novel pre-processing, classification, clustering and predictive modelling approach. Building and Environment, 140: 90–106.
Tong Z, Luo Y, Zhou J (2021). Mapping the urban natural ventilation potential by hydrological simulation. Building Simulation, 14: 351–364.
Tsinghua University Building Energy Research Centre (2020). 2020 Annual Report on China Building Efficiency. Beijing: China Architecture & Building Press. (in Chinese)
Wang X, Feng W, Cai W, et al. (2019). Do residential building energy efficiency standards reduce energy consumption in China?—A data-driven method to validate the actual performance of building energy efficiency standards. Energy Policy, 131: 82–98.
Xu P, Huang J, Shen P, et al. (2013). Commercial building energy use in six cities in southern China. Energy Policy, 53: 76–89.
Yang T, Zhang X (2016). Benchmarking the building energy consumption and solar energy trade-offs of residential neighborhoods on Chongming Eco-Island, China. Applied Energy, 180: 792–799.
Ye Y, Hinkelman K, Zhang J, et al. (2019). A methodology to create prototypical building energy models for existing buildings: A case study on U. S. religious worship buildings. Energy and Buildings, 194: 351–365.
Zhang Y, Hu S, Yan D, et al. (2021). Exploring cooling pattern of low-income households in urban China based on a large-scale questionnaire survey: A case study in Beijing. Energy and Buildings, 236: 110783.
Zhou X, Liu T, Yan D, et al. (2021). An action-based Markov chain modeling approach for predicting the window operating behavior in office spaces. Building Simulation, 14: 301–315.
This paper is supported by the National Natural Science Foundation of China (NSFC) through Grant No. 51908204 and the Natural Science Foundation of Hunan Province of China through Grant No. 2020JJ3008.