Automated energy performance certificate based urban building energy modelling approach for predicting heat load profiles of districts

被引:14
|
作者
Heidenthaler, Daniel [1 ,4 ]
Deng, Yingwen [2 ]
Leeb, Markus
Grobbauer, Michael [1 ]
Kranzl, Lukas [3 ]
Seiwald, Lena [1 ]
Mascherbauer, Philipp [3 ]
Reindl, Patricia [1 ]
Bednar, Thomas [4 ]
机构
[1] Salzburg Univ Appl Sci, Dept Green Engn & Circular Design, Campus Kuchl, Markt 136a, A-5431 Kuchl, Austria
[2] Res Studio iSPACE RSA FG, Schillerstr 25-29 370-3, A-5020 Salzburg, Austria
[3] TU Wien, Energy Econ Grp, Gusshausstr 25-29-370-3, A-1040 Vienna, Austria
[4] TU Wien, Inst Mat Technol Bldg Phys & Bldg Ecol, Karlsplatz 13-207, A-1040 Vienna, Austria
关键词
District energy simulation; Building energy simulation; District heating; Archetype; Model calibration; SIMULATION; WORKFLOW; STOCK; IMPLEMENTATION; CONSUMPTION; GENERATION; STRATEGIES; SECTOR; TOOLS; UBEM;
D O I
10.1016/j.energy.2023.128024
中图分类号
O414.1 [热力学];
学科分类号
摘要
Urban building energy modelling (UBEM) for analysing buildings in their spatial and functional context is an arising method. Only a few UBEM procedures use detailed building simulation tools, which are essential for high temporal and spatial resolution. This paper aims at developing a detailed automated physical bottom-up UBEM framework based on archetypes using Energy Performance Certificate data for predicting hourly heat load profiles of residential buildings. Simulation results are compared to and validated with measurements of two district heating networks and values from the TABULA typology. A comparison of the simulated hourly heat load profile for space heating and domestic hot water with measurement data results in a CV(RMSE) of 0.3, NMBE of 0.085, R2 of 0.85 and r of 0.94 for a sample size of 66 residential buildings, solely based on an estimation of the 3 classification criteria of the archetypes (building period, building condition and building type) and an estimation of the conditioned gross floor area for each measured building. Hence, the model can be declared as calibrated according to acceptance criteria in literature.
引用
收藏
页数:18
相关论文
共 50 条
  • [21] A global challenge of accurately predicting building energy consumption under urban heat island effect
    Yang, Miao
    Wang, Haorui
    Yu, Chuck Wah
    Cao, Shi-Jie
    INDOOR AND BUILT ENVIRONMENT, 2023, 32 (03) : 455 - 459
  • [22] Seasonal performance of an energy pile heat pump system and prediction of building thermal load
    Kong, Gangqiang
    Chen, Yu
    Wang, Lehua
    Meng, Yongdong
    Yang, Qing
    APPLIED THERMAL ENGINEERING, 2024, 241
  • [23] MACHINE LEARNING BASED OPTIMIZATION APPROACH FOR BUILDING ENERGY PERFORMANCE
    Solmaz, Aslihan Senel
    2020 ASHRAE BUILDING PERFORMANCE ANALYSIS CONFERENCE AND SIMBUILD, 2020, : 69 - 76
  • [24] A novel mobility-based approach to derive urban-scale building occupant profiles and analyze impacts on building energy consumption
    Wu, Wenbo
    Dong, Bing
    Wang, Qi
    Kong, Meng
    Yan, Da
    An, Jingjing
    Liu, Yapan
    APPLIED ENERGY, 2020, 278
  • [25] Systematic investigation of building energy efficiency standard and hot water preparation systems' influence on the heat load profile of districts
    Best, Isabelle
    Braas, Hagen
    Orozaliev, Janybek
    Jordan, Ulrike
    Vajen, Klaus
    ENERGY, 2020, 197
  • [26] Automated modelling of building energy systems with mode-based control algorithms in Modelica
    Cai, Xiaoye
    Xue, Junyi
    Kuempel, Alexander
    Mueller, Dirk
    CARBON-NEUTRAL CITIES - ENERGY EFFICIENCY AND RENEWABLES IN THE DIGITAL ERA (CISBAT 2021), 2021, 2042
  • [27] Estimating the building based energy consumption as an anthropogenic contribution to urban heat islands
    Boehme, Peter
    Berger, Matthias
    Massier, Tobias
    SUSTAINABLE CITIES AND SOCIETY, 2015, 19 : 373 - 384
  • [28] Novel validated method for GIS based automated dynamic urban building energy simulations
    Nageler, P.
    Zahrer, G.
    Heimrath, R.
    Mach, T.
    Mauthner, F.
    Leusbrock, I.
    Schranzhofer, H.
    Hochenauer, C.
    ENERGY, 2017, 139 : 142 - 154
  • [29] An automated IFC-based workflow for building energy performance simulation with Modelica
    Andriamamonjy, Ando
    Saelens, Dirk
    Klein, Ralf
    AUTOMATION IN CONSTRUCTION, 2018, 91 : 166 - 181
  • [30] Comparative study of a building energy performance software (KEP-IYTE-ESS) and ANN-based building heat load estimation
    Turhan, Cihan
    Kazanasmaz, Tugce
    Uygun, Ilknur Erlalelitepe
    Ekmen, Kenan Evren
    Akkurt, Gulden Gokcen
    ENERGY AND BUILDINGS, 2014, 85 : 115 - 125