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2020年第3期   DOI:10.19830/j.upi.2018.460
城市尺度建筑节能规划的国际经验及启示
Building Energy Efficiency Planning at Urban Scale: International Experience and Inspiration

冷红 宋世一

Leng Hong, Song Shiyi

关键词:城市节能;建筑能耗;建筑节能规划;城市模型;可持续发展;国际经验

Keywords:Urban Energy Efficiency; Building Energy Consumption; Building Energy Efficiency Planning; Urban Model; Sustainable Development; International Experience

摘要:

建筑能耗是城市能耗的主体。在城市规划层面进行大尺度建筑节能,能够在宏观层面有效把控城市尺度建筑能耗水平,降低建筑总体能耗;能够灵活协调现有城市空间环境建设管理与城市节能的关系,有效减少能耗花费,综合考虑各方面影响因素,制定科学有效的规划决策。本文对比了伦敦、纽约、东京、多伦多四个发达国家城市在城市尺度建筑节能规划方面的专项规划策略、规划政策管理、技术研究以及节能规划效果四方面经验,详细阐述了城市空间形态、城市微气候环境以及用能行为这三个影响城市尺度建筑节能规划的主要因素,并从规划框架建构、数据收集处理和跨学科技术支持三方面探讨了当前进行城市尺度建筑节能规划面临的困难与挑战,最终提出在宏观专项规划体系、社会监管、数据信息及跨领域合作等四方面的思考。


Abstract:

Building energy consumption is the main component of urban energy consumption. Controlling building energy consumption at the level of urban planning can effectively control the energy consumption at the macro level and reduce the overall building energy consumption; it can flexibly coordinate the relationship between the built environment management and urban energy conservation; it can effectively reduce energy consumption costs; it can comprehensively consider all aspects of influencing factors and formulate scientific and effective planning decisions. This paper compares the special planning strategies, planning policy management, technical research and energy efficiency planning effects of four developed cities worldwide, i.e., London, New York, Tokyo and Toronto, and elaborates on the main influencing factors of urban scale building energy efficiency planning in three aspects including urban spatial form, urban microclimate environment and energy using behavior. The paper also discusses the difficulties and challenges of building energy efficiency planning at urban scale from three aspects: planning framework construction, data collection and processing and interdisciplinary technical support. In the end, four suggestions are put forward: macro-special planning system, social supervision, data information and cross-disciplinary cooperation.


版权信息:
基金项目:国家自然科学基金“严寒地区基于城市尺度建筑能耗模型与空间分析技术整合的节能城市规划研究”(51678178)
作者简介:

冷红(通信作者),博士,哈尔滨工业大学建筑学院;寒地城乡人居环境科学与技术工业和信息化部重点实验室,教授,博士生导师。hitlaura@126.com

宋世一,哈尔滨工业大学建筑学院;寒地城乡人居环境科学与技术工业和信息化部重点实验室,博士研究生。15b934011@hit.edu.cn


译者简介:

参考文献:
  • [1] BP p.l.c. BP energy outlook: 2019 edition[R/OL]. (2019-02-14)[2020-02-21]. https://www.bp.com/content/dam/bp/business-sites/en/global/corporate/pdfs/energy-economics/energy-outlook/bp-energy-outlook-2019.pdf.

    [2] DE LA CRUZ-LOVERA C, PEREA-MORENO A J, DE LA CRUZFERNáNDEZ J L, et al. Worldwide research on energy efficiency and sustainability in public buildings[J]. Sustainability, 2017, 9(8): 1294.

    [3] 龙惟定. 建筑能耗比例与建筑节能目标[J]. 中国能源, 2005, 27(10): 23-27.

    [4] 吴志强, 申硕璞, 李欣. 关于沈阳方城旧城改造设计中的城市节能技术平台的探讨[J]. 城市发展研究, 2008( 增刊1): 122-127.

    [5] 冷红, 孙禹, 田玮. 基于空间相关的城市建筑能耗分布量化模型及应用——以伦敦非住宅建筑为例[J]. 地域研究与开发, 2015, 34(6): 82-88.

    [6] 李兆坚, 江亿. 我国广义建筑能耗状况的分析与思考[J]. 建筑学报, 2006(7): 30-33.

    [7] European Commission. EU climate action[EB/OL]. [2018-03-29]. https://ec.europa.eu/clima/policies/eu-climate-action_en.

    [8] DAMASSA T, GE M, TARYN F. The U.S. greenhouse gas reduction targets[R]. Washington DC: World Resource Institute, 2014.

    [9] The City of New York. Solution creating efficient buildings for all New Yorkers[EB/OL]. [2018-03-29]. https://www1.nyc.gov/site/builttolast/solution/solution.page

    [10] BLASIO B. One city: built to last[R/OL]. (2018-04-23)[2019-03-30]. https://www1.nyc.gov/assets/builttolast/downloads/OneCity.pdf.

    [11] Building Research Establishment. SBEM: Simplified Building Energy Model[EB/OL]. [2018-03-29]. https://www.bre.co.uk/page.jsp?id=706.

    [12] Office of Energy Efficiency and Renewable Energy. About building energy modelling[EB/OL]. [2018-03-29]. https://www.energy.gov/eere/buildings/about-building-energy-modeling.

    [13] 周岩, 庄智, 杨峰. 城市街区形态对热岛强度及能耗的影响[J]. 住宅科技, 2017, 37(9): 28-33.

    [14] LI D, MENASSA C C, KARATAS A. Energy use behaviors in buildings: towards an integrated conceptual framework[J]. Energy research and social science, 2017, 23: 97-112.

    [15] DELMASTRO C, MUTANI G, SCHRANZ L, et al. Building energy assessment and urban form[C]. Catania (Italy): 7th AIGE Conference, 2015: 1-6.

    [16] 王雅馨, 王一. 街区尺度下建筑群体能耗数值模拟与敏感形态因子研究[C]. 古国华, 童滋雨. 南京: 数字·文化——2017 全国建筑院系建

    筑数字技术教学研讨会暨DADA2017 数字建筑国际学术研讨会论文集, 2017: 502-506.

    [17] KO Y. Urban form and residential energy use: a review of design principles and research findings[J]. Journal of planning literature, 2013, 28(4): 327-351.

    [18] 里德·尤因, 荣芳, 秦波, 等. 城市形态对美国住宅能源使用的影响[J]. 国际城市规划, 2013, 28(2): 31-40.

    [19] WILSON B. Urban form and residential electricity consumption: evidence from Illinois, USA[J]. Landscape and urban planning, 2013, 115: 62-71.

    [20] SILVA M, OLIVEIRA V, LEAL V. Urban form and energy demand: a review of energy-relevant urban attributes[J]. Journal of planning literature, 2017, 32(4): 346-365.

    [21] TSIRIGOTI D, BIKAS D. A cross scale analysis of the relationship between energy efficiency and urban morphology in the Greek city context[J]. Procedia environmental sciences, 2017, 38: 682-687.

    [22] LI C, SONG Y, KAZA N. Urban form and household electricity consumption: a multilevel study[J]. Energy and buildings, 2018, 158:181-193.

    [23] JURELIONIS A, BOURIS D. Impact of urban morphology on infiltrationinduced building energy consumption[J]. Energies, 2016, 9(3): 177.

    [24] URQUIZO J, CALDERóN C, JAMES P. Metrics of urban morphology and their impact on energy consumption: a case study in the United Kingdom[J]. Energy research and social science, 2017, 32: 193-206.

    [25] 冷红, 袁青. 城市微气候环境控制及优化的国际经验及启示[J]. 国际城市规划, 2014, 29(6): 114-119.

    [26] PISELLO A L, PIGNATTA G, CASTALDO V L, et al. The impact of local microclimate boundary conditions on building energy performance[J]. Sustainability, 2015, 7(7): 9207-9230.

    [27] SUN Y, HEO Y, TAN M, et al. Uncertainty quantification of microclimate variables in building energy models[J]. Journal of building performance simulation, 2014, 7(1): 17-32.

    [28] LEE G, JEONG Y. Impact of urban and building form and microclimate on the energy consumption of buildings—based on statistical analysis[J]. Journal of Asian architecture & building engineering, 2017, 16(3): 565-572.

    [29] DORER V, ALLEGRINI J, OREHOUNIG K, et al. Modelling the urban microclimate and its impact on the energy demand of buildings and building clusters[C]. Chambery, France: Proceedings of 13th International Conference of the International Building Performance Simulation Association, 2013: 3483-3489.

    [30] MALYS L, MUSY M, INARD C. Microclimate and building energy consumption: study of different coupling methods[J]. Advances in building energy research, 2015, 9(2): 151-174.

    [31] YOUSEFI F, GHOLIPOUR Y, WEI Y. A study of the impact of occupant behaviors on energy performance of building envelopes using occupants’data[J]. Energy and buildings, 2017, 148: 182-198.

    [32] BARTHELMES V M, BECCHIO C, FABI V, et al. Occupant behaviour lifestyles and effects on building energy use: investigation on high and low performing building features[J]. Energy procedia, 2017, 140: 93-101.

    [33] MAGALH?ES S M C, LEAL V M S, HORTA I M. Modelling the relationship between heating energy use and indoor temperatures in residential buildings through artificial neural networks considering occupant behavior[J]. Energy and buildings, 2017, 151: 332-343.

    [34] 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.

    [35] CARAGLIU A, BO C D. Smartness and European urban performance: assessing the local impacts of smart urban attributes[J]. Innovation the European journal of social science research, 2012, 25(2): 97-113.

    [36] 田玮, 魏来, 朱丽, 等. 城市规模的建筑能耗研究综述[J]. 建筑节能, 2016(2): 59-64.

    [37] DELMASTRO C, MUTANI G, PASTORELLI M, et al. Urban morphology and energy consumption in Italian residential buildings[C]. Birmingham (UK): International Conference on Renewable Energy Research and Applications. IEEE, 2016: 1603-1608.

    [38] GüNERALP B, ZHOU Y, ?RGE-VORSATZ D, et al. Global scenarios of urban density and its impacts on building energy use through 2050[J]. Proceedings of the National Academy of Sciences. 2017, 114 (34): 8945-8950.

    [39] BOUKARTA S, BEREZOWSKA E. Exploring the energy implication of urban density in residential buildings[J]. Journal of applied engineering sciences, 2017, 7(1): 7-14.

    [40] HUANG P, HUANG G, SUN Y. Uncertainty-based life-cycle analysis of near-zero energy buildings for performance improvements[J]. Applied energy, 2018, 213(3): 486-498.

    [41] MARSZAL A J, HEISELBERG P, BOURRELLE J S, et al. Zero energy building – a review of definitions and calculation methodologies[J]. Energy and buildings, 2014, 43(4): 971-979.

    [42] ESEN H, ESEN M, OZSOLAK O. Modelling and experimental performance analysis of solar-assisted ground source heat pump system[J]. Journal of experimental and theoretical artificial intelligence, 2015, 29(1): 1-17.

    [43] GRANDJEAN A, ADNOT J, BINET G. A review and an analysis of the residential electric load curve models[J]. Renewable and sustainable energy reviews, 2012, 16(9): 6539-6565.

    [44] 杨秀, 魏庆芃, 江亿. 建筑能耗统计方法探讨[J]. 建筑节能, 2007, 28(1):12-16.

    [45] MOGHADAM S T, DELMASTRO C, CORGNATI S P, et al. Urban energy planning procedure for sustainable development in the built environment: a review of available spatial approaches[J]. Journal of cleaner production, 2017, 165: 811-827.

    [46] AMASYALI K, EL-GOHARY N M. A review of data-driven building energy consumption prediction studies[J]. Renewable and sustainable energy reviews, 2018, 81: 1192-1205.

    [47] HELLERSTEIN J M. Quantitative data cleaning for large databases[R/OL]. White paper, United Nations Economic Commission for Europe. (2008-03-13)[2018-09-28]. https://dsf.berkeley.edu/jmh/papers/cleaning-unece.pdf.

    [48] LIU Y, LIU T, YE S, et al. Cost-benefit analysis for energy efficiency retrofit of existing buildings: a case study in China[J]. Journal of cleaner production, 2017, 177: 493-506.

    [49] 冯可梁, 李越铭, 田昕. 北京市建筑能耗统计误差来源分析[J]. 暖通空调, 2014, 44(4): 68-71.

    [50] YANG X, ZHAO L, BRUSE M, et al. An integrated simulation method for building energy performance assessment in urban environments[J]. Energy and buildings, 2012, 54(6): 243-251.

    [51] GUATTARI C, EVANGELISTI L, BALARAS C A. On the assessment of urban heat island phenomenon and its effects on building energy performance: a case study of Rome (Italy)[J]. Energy and buildings, 2017, 158: 605-615.

    [52] WANG H, SHEN Q, TANG B S. GIS-based framework for supporting land use planning in urban renewal: case study in Hong Kong[J]. Journal of urban planning and development, 2014, 141(3): 05014015.

    [53] MEYER S R, JOHNSON M L, LILIEHOLM R J, et al. Development of a stakeholder-driven spatial modeling framework for strategic landscape planning using Bayesian networks across two urban-rural gradients in Maine, USA[J]. Ecological modelling, 2014, 291: 42-57.

    [54] LEVY S, MARTENS K, HEIJDEN R V D. Agent-based models and selforganization: addressing common criticisms and the role of agent-based modelling in urban planning[J]. Town planning review, 2016, 87(3): 321-338.

    [55] ROBINSON C, DILKINA B, HUBBS J, et al. Machine learning approaches for estimating commercial building energy consumption[J]. Applied energy, 2017, 208: 889-904.

    [56] SEYEDZADEH S, RAHIMIAN F P, GLESK I, et al. Machine learning for estimation of building energy consumption and performance: a review[J]. Visualization in engineering, 2018, 6: 5. DOI: https://doi.org/s40327-018-0064-7

    [57] DENG H, FANNON D, ECKELMAN M J. Predictive modeling for US commercial building energy use: a comparison of existing statistical and machine learning algorithms using CBECS microdata[J]. Energy and buildings, 2018, 163: 34-43.

    [58] SWAN L G, UGURSAL V I. Modeling of end-use energy consumption in the residential sector: a review of modeling techniques[J]. Renewable and sustainable energy reviews, 2009, 13(8): 1819-1835.

    [59] FRAYSSINET L, MERLIER L, KUZNIK F, et al. Modeling the heating and cooling energy demand of urban buildings at city scale[J]. Renewable and sustainable energy reviews, 2017, 81(2): 2318-2327.

    [60] KAVGIC M, MAVROGIANNI A, MUMOVIC D, et al. A review of bottomup building stock models for energy consumption in the residential sector[J]. Building and environment, 2010, 45(7): 1683-1697.

    [61] BURNETT J W, MADARIAGA J. A top-down economic efficiency analysis of U.S. household energy consumption[J]. The energy journal, 2017, 39: 4.

    [62] URQUIZO J, CALDERóN C, JAMES P. Understanding the complexities of domestic energy reductions in cities: integrating data sets generally available in the United Kingdom’s local authorities[J]. Cities, 2018, 74: 292-307.

    [63] TIAN W, CHOUDHARY R. A probabilistic energy model for non-domestic building sectors applied to analysis of school buildings in Greater London[J]. Energy and buildings, 2012, 54(6): 1-11.

    [64] CORGNATI S P, FABRIZIO E, FILIPPI M, et al. Reference buildings for cost optimal analysis: method of definition and application[J]. Applied energy, 2013, 102: 983-993.

    [65] CHENG V, STEEMERS K. Modelling domestic energy consumption at district scale: a tool to support national and local energy policies[J]. Environmental modelling and software, 2011, 26(10): 1186-1198.

    [66] BALLARINI I, CORGNATI S P, CORRADO V. Use of reference buildings to assess the energy saving potentials of the residential building stock: the experience of Tabula project[J]. Energy policy, 2014, 68: 273-284.

    [67] KAZAS G, FABRIZIO E, PERINO M. Energy demand profile generation with detailed time resolution at an urban district scale: a reference building approach and case study[J]. Applied energy, 2017, 193: 243-262.

    [68] KADIAN R, DAHIYA R P, GARG H P. Energy-related emissions and mitigation opportunities from the household sector in Delhi[J]. Energy policy, 2007, 35(12): 6195-6211.

    [69] QUAN SJ, LI Q, AUGENBROE G, et al. Urban data and building energy modeling: a GIS-based urban building energy modeling system using the Urban-EPC engine[M] // GEERTMAN S, FERREIRA J, GOODSPEED R, et al, eds. Planning support systems and smart cities. Boston: Springer, 2015: 447-469

    [70] WALTER T, SOHN M D. A regression-based approach to estimating retrofit savings using the building performance database[J]. Applied energy, 2016, 179: 996-1005.

    [71] MA J, CHENG J C P. Estimation of the building energy use intensity in theurban scale by integrating GIS and big data technology[J]. Applied energy, 2016, 183: 182-192.

    [72] NEWSHAM G R, DONNELLY C L. A model of residential energy end-use in Canada: using conditional demand analysis to suggest policy options for community energy planners[J]. Energy policy, 2013, 59(3): 133-142.

    [73] SILVA M C, HORTA I M, LEAL V, et al. A spatially-explicit methodological framework based on neural networks to assess the effect of urban form on energy demand[J]. Applied energy, 2017, 202: 386-398.

    [74] 江亿, 李霞, 陶然. 中国特色的建筑节能之路[J]. 景观设计学, 2018, (3): 60-64.

    [75] HERBST A, TORO F, REITZE F, et al. Introduction to energy systems modelling[J]. Swiss journal of economics and statistics, 2012, 148(2): 111-135.

    [76] MIRAKYAN A, GUIO R D. Integrated energy planning in cities and territories: a review of methods and tools[J]. Renewable and sustainable energy reviews, 2013, 22(8): 289-297.

    [77] 龙惟定. 对建筑节能2.0 的思考[J]. 暖通空调, 2016, 46(8): 1-12.


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