DOI: 10.19830/j.upi.2018.150
A Comparative Study of Urban Energy Consumption Simulation Methods

Li Yanxia, Wu Yue, Wang Lu, Wang Chao, Shi Xing

Keywords: Urban Energy Consumption; Urban Energy - Simulation; Top-down Approach; Bottom-up Approach; Conventional Prediction Process

Abstract:

In response to global warming, promoting energy efficiency is an important research field of green buildings and ecological cities. On the building level, energy simulation technology is quite mature. However, it is more difficult to simulate energy consumption on the urban level due to the large number of individual buildings, various types, diverse structures, and complex urban facilities,  thus new methods must be developed to calculate urban energy consumption. This paper summarizes the general process of urban energy simulation aiming at the most advanced simulation methods in the world, and compares the different methods of urban energy simulation based on this process. The research of urban energy consumption simulation methods can provide powerful technical support for urban energy policy formulation, urban energy security and urban energy conservation.

Funds:

Brief Info of Author(s):

References:
  • [1] UNEP. Cities and climate change[EB/OL]. [2015-05-17]. http://www.unep.org/resourceefficiency/Policy/ResourceEfficientCities/FocusAreas/CitiesandClimateChange/tabid/101665/Default.asp.
    [2] CHALAL M, BENACHIR M, WHITE M, et al. Energy planning and forecasting approaches for supporting physical improvement strategies in the building sector: a review[J]. Renewable and sustainable energy reviews, 2016, 64: 761-776.
    [3] YANG Z, GHAHRAMANI A, BECERIK-GERBER B. Building occupancy diversity and HVAC (heating, ventilation, and air conditioning) system energy efficiency[J]. Energy, 2016, 109: 641-649.
    [4] BECKER S, FREW B A, ANDRESEN G B, et al. Renewable build-up pathways for the US: generation costs are not system costs[J]. Energy, 2015, 81: 437-445.
    [5] CHOI J-K, MORRISON D, HALLINAN K P, et al. Economic and environmental impacts of community-based residential building energy efficiency investment[J]. Energy, 2014, 78: 877-886.
    [6] LACKO R, DROBNI_C B, MORI M, et al. Stand-alone renewable combined heat and power system with hydrogen technologies for household application[J]. Energy, 2014, 77: 164-170.
    [7] COMODI G, CIOCCOLANTI L, RENZI M. Modelling the Italian household sector at the municipal scale: micro-CHP, renewables and energy efficiency[J]. Energy, 2014, 68: 92-103.
    [8] Broin E ó, MATA E, G?RANSSON A, et al. The effect of improved efficiency on energy savings in EU-27 buildings[J]. Energy, 2013, 57: 134-148.
    [9] PARSHALL L, GURNEY K, HAMMER S A, et al. Modeling energy consumption and CO2 emissions at the urban scale: methodological challenges and insights from the United States[J]. Energy policy, 2010. 38(9): 4765-82.
    [10] AGENCY I E. Cities, Towns and renewable energy: yes in my front yard[J]. SourceOCDE Environnement et développement durable, 2009(12): 1-194.
    [11] 时宁国, 解亚萍. 基于细胞自动机的城市用地仿真研究[J]. 系统仿真技术, 2010, 6(3): 192-201.
    [12] 周欣, 燕达. 建筑能耗模拟软件空调系统模拟对比研究[J]. 暖通空调, 2014, 44(4): 113-131.
    [13] 周欣, 燕达. 区域建筑负荷特性及影响因素分析[J]. 暖通空调, 2014, 44(增刊1): 264-269.
    [14] LI W, ZHOU Y, CETIN K, et al. Modeling urban building energy use: a review of modeling approaches and procedures[J]. Energy, 2017, 141: 2445-2457.
    [15] DAVILA C C, REINHART C F, BEMIS J L. Modeling Boston: a workflow for the efficient generation and maintenance of urban building energy models from existing geospatial datasets[J]. Energy, 2016, 117: 237-250.
    [16] BR?GGER M, WITTCHEN K B. Flexible building stock modelling with array-programming[C]. Proceedings of the 15th IBPSA Conference San Francisco, CA, USA, 2017.
    [17] FONSECA J A, SCHLUETER A. Integrated model for characterization of spatiotemporal building energy consumption patterns in neighborhoods and city districts[J]. Applied energy, 2015, 142: 247-265.
    [18] TONN B, WHITE D. Residential electricity use, wood use, and indoor temperature; an econometric model[J]. Energy systems and policy, 1988,12(3): 151-166.
    [19] RAFFIO G, ISAMBERT O, MERTZ G, et al. Targeting residential energy assistance[C]. Proceedings of Energy Sustainability, 2007: 489-495.
    [20] AIGNER D J, SOROOSHIAN C, KERWIN P. Conditional demand analysis for estimating residential end-use load profiles[J]. Energy, 1984, 5(3): 81-97.
    [21] DAVILA C C, REINHART C F. Urban building energy modeling-a review of a nascent field[J]. Building and environment, 2016(97), 196-202.
    [22] CHEN Y, HONG T, PIETTE M A. City-scale building retrofit analysis: a case study using CityBES[C]. Proceedings of the 15th IBPSA Conference San Francisco, CA, USA, 2017: 1084-1091.
    [23] KRüGER A, KOLBE T H. Building analysis for urban energy planning using key indicators on virtual 3D city models-the energy atlas of Berlin[C]. Proc ISPRS Congr, 2012.
    [24] GR?GER G, PLüMER L. CityGML interoperable semantic 3D city models[C]. ISPRSJ Photogramm Remote Sensing, 2012, 71: 12-33.
    [25] NOUVEL R, ZIRAK M, DASTAGEERI H, et al. Urban energy analysis based on 3D city model for national scale applications[C]. Present. IBPSA Germany Conference, vol. 8, 2014.
    [26] SARAN S, WATE P, SRIVASTAV S K, et al. CityGML at semantic level for urban energy conservation strategies[J]. AnnGIS, 2015, 21: 27-41.
    [27] ALAM N, COORS V, ZLATANOVA S, et al. Shadow effect on photo-voltaic potentiality analysis using 3D city models[C]. International Society for Photogrammetry and Remote Sensing (ISPRS), 2012.
    [28] ZHU L, MAO H. Building interior model interpolation between IFC and CityGML[J]. The open construction building technology journal, 2015(9): 170-176.
    [29] 潘毅群, 郁丛, 龙惟定, 等. 区域建筑负荷与能耗预测研究综述[J]. 暖通空调, 2015(3): 33-40.
    [30] LI C. GIS for urban energy analysis[J]. Comprehensive geographic information systems, 2018(2): 187-195.

TOP 10