摘要：能源危机与碳排放已成为全球化问题，尤其在冬季采暖能耗巨大的我国严寒地区较为严重。此外，包括大空间建筑在内的公共建筑消耗大量能源，从而引起广泛关注。然而，绿色建筑性能评价时常遇到复杂的权衡问题。多目标优化方法可以有效解决这一问题，帮助设计师获得更多可行方案，并在设计决策方面存在较大优势。与此同时，BIM技术的发展使整合几何建模、性能模拟工具及优化算法成为可能，并在建筑全生命周期信息管理上较为突出。本文基于Revit-Dynamo这一参数化平台，旨在提出一种基于BIM的建筑多绿色性能目标优化方法，在使采光性能最优化的同时降低能耗与碳排放水平。文章详细阐述了多目标优化设计流程，并通过案例研究佐证其适用性。实验结果表明，建筑绿色性能随着迭代次数的增加而逐步提升，进而证明该方法在提升严寒地区大空间建筑绿色性能方面的有效性。

关键词: BIM;多性能目标优化；采光；能耗；碳排放；严寒地区

1 背景概述

随着化石燃料的短缺和全球变暖的威胁，能源消耗和碳排放引起了广泛的关注，特别是在建筑业。鉴于冬季的寒冷气候，我国严寒地区建筑与其他地区相比，采暖能耗和二氧化碳排放量十分可观。根据最新数据显示，我国2016年度建筑碳排放总量多达19.6亿吨，占国内碳排放总量的19%。采暖地区的平均碳排放量为2.66吨/人，是非采暖区碳排放量的2倍。对严寒地区来说，以黑龙江省为例，其碳排放密度为56 kgCO2/m2,多于北方其余采暖区18 kgCO2/m2。由此可见，严寒地区与我国其余地区相比，节能减排任务迫在眉睫。与此同时，包括大空间建筑在内的公共建筑碳排放密度水平同样较高，约为我国其他类型建筑的2.09倍。因此，探讨严寒地区大空间建筑的绿色性能具有重大意义。大空间建筑的绿色性能主要包括采光性能、建筑能耗与建筑碳排放。值得关注的是，采光性能提升的同时势必会引起能耗与碳排放量的增加。为有效解决这一复杂的决策权衡问题，笔者引入多目标优化算法，在初步创作方案的基础上求得采光、能耗与碳排放性能目标均优的设计方案。随着计算性设计工具的蓬勃发展，BIM逐渐走上建筑设计的舞台，其在建筑全生命周期信息管理上存在显著优势。同时，BIM的参数化编程技术已趋于成熟，可有效整合几何建模、性能模拟工具及优化算法，为严寒地区大空间建筑的绿色性能多目标优化设计提供契机。在此背景下，本文提出一种基于BIM平台的绿色性能导向设计决策方法，优化严寒地区大空间建筑的采光、能耗与碳排放性能。

2 绿色性能导向的优化设计流程

本方法工作流程引入遗传优化算法，以获得优化问题的性能目标最优值与对应的建筑形体决策变量数值。整个工作流程包括确定优化目标、决策变量选择、参数化建模模拟与多目标优化四个子流程。在第一个子流程中中，需根据严寒地区气候特征及可选择的模拟引擎设定采光、能耗与碳排放为优化性能目标。随后，应根据与优化目标的关联及形态改变可能性选择决策变量，即建筑形体要素。在此条件下，优化目标和决策变量得以集成到建筑信息模型中，进而进入参数模拟建模子流程。在这一子流程中，BIM信息模型整合了两类信息，分别为环境信息与材料属性信息。环境信息包括地理位置信息与气象信息数据。材料属性信息可以划分为不透明材料（墙体、地面、屋面等）与透明材料（窗）。整合了信息的BIM模型分别进入Radiance、Daysim模拟采光性能指标，进入Green Building Studio云端模拟能耗与碳排放数据。在多目标优化子流程中，运用NSGA-II遗传算法通过迭代计算来获得最优方案。该子流程获得的是与设置种群数量相同数量的一系列多样化方案。设计师可在一系列方案集中选择满意的方案。

3 绿色性能导向的优化设计平台

Dynamo是一款基于Revit软件的可视化编程插件，通过连接电池节点与Python编程实现。该Revit-Dynamo多目标优化设计平台由三个部分构成，分别是几何建模、性能模拟与NSGA-II算法优化，如图2所示。在几何建模模块中，可通过Dynamo参数化编程轻松实现创建建筑构件、增加建筑构造层次、修改尺寸与位置等功能。性能模拟模块包含碳排放、能耗与采光模拟。碳排放与能耗模拟部分采用基于DOE-2.2的Green Building Studio云计算，采光模拟引擎则为Radiance与Daysim. 通过调用Ladybug、Honeybee等程序包可沟通不同软件之间的接口。NSGA-II算法优化模块为使用者提供种群数量、目标数量、迭代次数等参数设置界面，最终得出非支配Pareto解集。在此平台中，更新的数据均会反馈给BIM信息模型，从而改变模型的形态与属性，进入下一次的模拟与优化过程。

4 绿色性能导向的优化设计目标

（1）DA

DA的概念由Reinhart和Walkenhorst在2001年正式提出。该指标以工作平面照度为依据，基于地域气候，对采光进行动态评价。其概念为全年工作时间中仅靠自然采光即可达到最小照度要求的时间百分比，其中最常用的指标为300lx时的DA值，即DA300.

（2）UDI

UDI 2005年由Nabil与Mardaljevic提出，同样是一种评价采光的动态指标。其定义为年度工作时间内平面照度100-2000lx的时间百分比。工作平面照度100lx以下代表采光不充分，2000lx以上会引起引起视觉不舒适的眩光，因此在计算UDI值时，100-2000lx被视为有效，即UDI100-2000.

（3）E

（4）EB

5 案例研究

（1）案例选取

本研究以严寒地区为地理研究范围，故选择哈尔滨市（126.54°E, 45.54°N）作为我国严寒地区城市的代表。该地区气象数据可从EPW格式文件中获得，进而输入至模拟引擎中。其中与采光模拟计算相关的数据有太阳辐射与地表照度；能耗及碳排放模拟计算的数据包括温度、湿度、风速与太阳辐射。研究选择的案例为哈尔滨市双城区一大空间建筑，其地理区位与BIM信息化建模过程如图3-4所示。

（2）决策变量选择

建筑形体决策变量的选择与优化结果密切相关，从而形成决策变量与优化目标之间的映射关系。因此，所选建筑形态决策变量需直接影响建筑采光、能耗与碳排放性能。建筑最终选取一层进深、二层开间与三类窗高度为决策变量进行优化，如图5所示。五个变量的取值范围分别为21-24.9m、62.4-66.3m、600-3600mm、600-2100mm、600-1800mm.

（3）优化参数设置

优化前需对建筑材料的光学属性、热工物理属性及优化算法参数进行设定。材料光学属性需考虑不透光材料的反射率与透光材料的透射率，具体参数如表1所示。热工物理属性由构造层厚度、导热系数、材料密度、比热容、总厚度与总热阻构成，具体参数详见表2。优化算法参数包括精英率、突变概率、交叉概率、突变分布指数、交叉分布指数、种群数量六项参数，如表3所示。

6 结果分析

(1)非支配解演进过程分析

    如图6所示，在三维坐标系中，颜色最深的为最终的迭代运算结果，即第50代。第30代、15代、3代也依次置于图中，颜色由深至浅。可以看到，随着迭代次数的增加，解的分布逐渐由分散变为聚拢，且逐渐逼近坐标轴。

为进一步探索非支配解的演进过程，笔者选取DA与EB两个指标用于分析。如图7-a所示，第3代的解分布较为分散，且多数解的建筑绿色性能水平较差。随着优化过程的推进，解分布更加集中，逐渐形成清晰的Pareto前缘，且采光性能更优、碳排放量更少的解数量逐渐增加，如图7-b、7-c、7-d所示。

图8显示了四个目标的最优与最劣性能值。可以看到，四个目标的最优解均来自第50代的非支配解，而最劣解均来自前代的支配解。因此可以充分证明，运用遗传优化算法进行严寒地区大空间建筑的数字化节能设计在绿色性能水平提升方面行之有效。

（2）多绿色性能权衡能力与设计可能性探索能力

运用基于BIM的建筑多绿色性能目标优化方法最终筛选出的综合最优方案对四项性能目标的权衡较好，且各单项性能最优方案也呈现出较好的均衡性，如图9所示。随后，为验证该方法权衡多绿色性能与设计可能性的探索能力，笔者应用SOM神经网络对全部设计可能性进行聚类，探索了100类设计可能中的50类，说明集成工具平台具有权衡多绿色性能与设计可能性的探索能力（图10）。

（3）建筑绿色性能改善程度比较

分析实验得出的能耗、碳排放、DA、UDI单项性能和综合性能相对最优方案，结果表明：运用基于BIM的建筑多绿色性能目标优化方法所得综合方案相比既有方案在多项性能上均有提升（图11）。

（4）多绿色性能权衡与决策

优化过程所得方案可供设计师在多个绿色性能目标中作出取舍，但大量的方案及目标之间的复杂关系使决策依然不够方便快捷。由于DA超过50%左右时可以认为空间采光良好，所以笔者选取DA值为50%作为边界条件。如图12所示，应用SOM神经网络进行聚类，根据边界条件可选出5组待选方案，其性能目标值如表4所示。通过权衡，最终确定方案E为性能相对最优方案，如图13所示。

7 结语

    我国严寒地区建筑由于气候原因，冬季能耗与碳排放量巨大。而大空间建筑由于其体积较大，层高较高而消耗大量能源。因此，探讨严寒地区大空间建筑的节能减排问题具有重大的社会意义。然而在降低能耗与碳排放量的同时，严寒地区大空间建筑的采光性能会因之减弱。为解决这一问题，本文基于BIM平台提出了一种数字化节能设计方法。通过案例研究表明，该方法在严寒地区大空间建筑的综合性能提升方面较为有效，并为BIM平台的的严寒地区大空间建筑综合信息集成一体化打下一定的研究基础。

参考文献

[1] 清华大学建筑节能研究中心.中国建筑节能年度发展研究报告2018.北京：中国建筑工业出版社，2018.

[2] C.H. Hansen, O. Gudmundsson, N. Detlefsen, Cost efficiency of district heating for low energy buildings of the future, Energy, Volume 177, 2019, 77-86.

[3] Kyriaki Foteinaki, Rongling Li, Alfred Heller, Carsten Rode, Heating system energy flexibility of low-energy residential buildings, Energy and Buildings, Volume 180, 2018, 95-108.

[4] Francesco Reda, Zarrin Fatima, Northern European nearly zero energy building concepts for apartment buildings using integrated solar technologies and dynamic occupancy profile: Focus on Finland and other Northern European countries, Applied Energy, Volume 237, 2019, 598-617.

[5] Lorenzo Belussi, Benedetta Barozzi, Alice Bellazzi, Ludovico Danza, Anna Devitofrancesco, Carlo Fanciulli, Matteo Ghellere, Giulia Guazzi, Italo Meroni, Francesco Salamone, Fabio Scamoni, Chiara Scrosati, A review of performance of zero energy buildings and energy efficiency solutions, Journal of Building Engineering, Volume 25, 2019, 100772.

[6] Jaehun Sim, Jehean Sim, The effect of new carbon emission reduction targets on an apartment building in South Korea, Energy and Buildings, Volume 127,2016, 637-647.

[7] Shreshth Nagpal, Jared Hanson, Christoph Reinhart, A framework for using calibrated campus-wide building energy models for continuous planning and greenhouse gas emissions reduction tracking, Applied Energy, Volume 241, 2019, 82-97.

[8] Yue Teng, Kaijian Li, Wei Pan, Thomas Ng, Reducing building life cycle carbon emissions through prefabrication: Evidence from and gaps in empirical studies, Building and Environment, Volume 132, 2018, 125-136.

[9] Ting Luo, Yongtao Tan, Craig Langston, Xiaolong Xue, Mapping the knowledge roadmap of low carbon building: A scientometric analysis, Energy and Buildings, Volume 194, 2019, 163-176.

[ 0] Seok-Gil Yong, Jong-Hyun Kim, Yuseong Gim, Jinho Kim, Jinkyun Cho, Hiki Hong, Young-Jin Baik, Junemo Koo, Impacts of building envelope design factors upon energy loads and their optimization in US standard climate zones using experimental design, Energy and Buildings, Volume 141, 2017, 1-15.

[1 ] Ming-Der Yang, Min-Der Lin, Yu-Hao Lin, Kang-Ting Tsai, Multiobjective optimization design of green building envelope material using a non-dominated sorting genetic algorithm, Applied Thermal Engineering, Volume 111, 2017, 1255-1264.

[ 2] Pengyuan Shen, William Braham, Yunkyu Yi, Eric Eaton, Rapid multi-objective optimization with multi-year future weather condition and decision-making support for building retrofit, Energy, Volume 172, 2019, 892-912.

[ 3] Ayca Kirimtat, Ondrej Krejcar, Berk Ekici, M. Fatih Tasgetiren, Multi-objective energy and daylight optimization of amorphous shading devices in buildings, Solar Energy, Volume 185, 2019, 100-111.

[ 4] Fabrizio Ascione, Nicola Bianco, Gerardo Maria Mauro, Giuseppe Peter Vanoli, A new comprehensive framework for the multi-objective optimization of building energy design: Harlequin, Applied Energy, Volume 241, 2019, 331-361.

[ 5] Ehsan Kamel, Ali M. Memari, Review of BIM's application in energy simulation: Tools, issues, and solutions, Automation in Construction, Volume 97, 2019, 164-180.

[ 6] Rodger E. Edwards, Eric Lou, Anas Bataw, Syahrul Nizam Kamaruzzaman, Christopher Johnson, Sustainability-led design: Feasibility of incorporating whole-life cycle energy assessment into BIM for refurbishment projects, Journal of Building Engineering, Volume 24, 2019, 100697.

[ 7] Vincent J.L. Gan, M. Deng, K.T. Tse, C.M. Chan, Irene M.C. Lo, Jack C.P. Cheng, Holistic BIM framework for sustainable low carbon design of high-rise buildings, Journal of Cleaner Production, Volume 195, 2018, 1091-1104.

[ 8] U. Vitiello, V. Ciotta, A. Salzano, D. Asprone, G. Manfredi, E. Cosenza, BIM-based approach for the cost-optimization of seismic retrofit strategies on existing buildings, Automation in Construction, Volume 98, 2019, 90-101.

[ 9] Worawan Natephra, Nobuyoshi Yabuki, Tomohiro Fukuda, Optimizing the evaluation of building envelope design for thermal performance using a BIM-based overall thermal transfer value calculation, Building and Environment, Volume 136, 2018, 128-145.

[20] Mohammad Rahmani Asl, Saied Zarrinmehr, Michael Bergin, Wei Yan, BPOpt: A framework for BIM-based performance optimization, Energy and Buildings, Volume 108, 2015, 401-412.

[2 ] Mohammad Rahmani Asl, Michael Bergin, Adam Menter, Wei Yan, BIM-based Parametric Building Energy Performance Multi-Objective Optimization, 32nd International Conference on Education and Research in Computer Aided Architectural Design in Europe, 2014: 455-464.

[22] Jungsik Choi, Jihye Shin, Minchan Kim, Inhan Kim, Development of openBIM-based energy analysis software to improve the interoperability of energy performance assessment, Automation in Construction, Volume 72, Part 1, 2016, 52-64.

[23] Youngjib Ham, Mani Golparvar-Fard, Mapping actual thermal properties to building elements in gbXML-based BIM for reliable building energy performance modeling, Automation in Construction, Volume 49, Part B, 2015, 214-224.

[24] Ahmad Eltaweel, Yuehong Su, Controlling venetian blinds based on parametric design; via implementing Grasshopper’s plugins: A case study of an office building in Cairo, Energy and Buildings, Volume 139, 2017, 31-43.

[25] Wei Yu, Baizhan Li, Hongyuan Jia, Ming Zhang, Di Wang, Application of multi-objective genetic algorithm to optimize energy efficiency and thermal comfort in building design, Energy and Buildings, Volume 88, 2015, 135-143.

[26] Yunsong Han, Hong Yu, Cheng Sun, Simulation-Based Multiobjective Optimization of Timber-Glass Residential Buildings in Severe Cold Regions, sustainability, Volume 9, 2017, 2353.

[27] Binghui Si, Jianguo Wang, Xinyue Yao, Xing Shi, Xing Jin, Xin Zhou, Multi-objective optimization design of a complex building based on an artificial neural network and performance evaluation of algorithms, Advanced Engineering Informatics, Volume 40, 2019, 93-109.

[28] 孙澄,韩昀松.光热性能考虑下的严寒地区办公建筑形态节能设计研究[J].建筑学报,2016(02):38-42.

[29] Yujie Lu, Zhilei Wu, Ruidong Chang, Yongkui Li, Building Information Modeling (BIM) for green buildings: A critical review and future directions, Automation in Construction, Volume 83, 2017, 134-148.

[30] Jong Bum Kim, WoonSeong Jeong, Mark J. Clayton, Jeff S. Haberl, Wei Yan, Developing a physical BIM library for building thermal energy simulation, Automation in Construction, Volume 50, 2015, 16-28.

[3 ] Yun Kyu Yi, Building facade multi-objective optimization for daylight and aesthetical perception, Building and Environment, Volume 156, 2019, 178-190.

[32] Mitja Ko?ir, Nata?a Igli?, Roman Kuni?, Optimisation of heating, cooling and lighting energy performance of modular buildings in respect to location’s climatic specifics, Renewable Energy, Volume 129, Part A, 2018, 527-539.

[33] Christoph F. Reinhart, Oliver Walkenhorst, Validation of dynamic RADIANCE-based daylight simulations for a test office with external blinds, Energy and Buildings, Volume 33, Issue 7, 2001, 683-697.

[34] Christoph F. Reinhart, Jan Wienold, The daylighting dashboard – A simulation-based design analysis for daylit spaces, Building and Environment, Volume 46, Issue 2, 2011, 386-396.

[35] GB 50033-2013. Standard for Daylighting Design of Buildings; China Architecture & Building Press: Beijing, China, 2013; p. 8.

[36] Yu Bian, Yuan Ma, Analysis of daylight metrics of side-lit room in Canton, south China: A comparison between daylight autonomy and daylight factor, Energy and Buildings, Volume 138, 2017, 347-354.

[37] Nabil, A. Useful daylight illuminance: A new paradigm for assessing daylight in buildings. Light. Res. Technol. 2005, 37, 41–59.

[38] Azza Nabil, John Mardaljevic, Useful daylight illuminances: A replacement for daylight factors, Energy and Buildings, Volume 38, Issue 7, 2006, 905-913.

[39] Umberto Berardi, Hamid Khademi Anaraki, Analysis of the Impacts of Light Shelves on the Useful Daylight Illuminance in Office Buildings in Toronto, Energy Procedia, Volume 78, 2015, 1793-1798.

[40] 清华大学建筑节能研究中心.中国建筑节能年度发展研究报告2016.北京：中国建筑工业出版社，2016.

[41] Drury B. Crawley. Which Weather Data Should You Use for Energy Simulations of Commercial Buildings?, ASHRAE Transactions, Vol. 104, Pt. 2, 1998, 498-515.

[42] Tarun Kumar Gupta, Khalid Raza, Chapter 7 - Optimization of ANN Architecture: A Review on Nature-Inspired Techniques, Editor(s): Nilanjan Dey, Surekha Borra, Amira S. Ashour, Fuqian Shi, Machine Learning in Bio-Signal Analysis and Diagnostic Imaging, Academic Press, 2019, 159-182.

[43] Guohui Feng, Dandan Chi, Xiaolong Xu, Baoyue Dou, Yixin Sun, Yao Fu, Study on the Influence of Window-wall Ratio on the Energy Consumption of Nearly Zero Energy Buildings, Procedia Engineering, Volume 205, 2017, 730-737.

[44] Peng Xue, Qian Li, Jingchao Xie, Mengjing Zhao, Jiaping Liu, Optimization of window-to-wall ratio with sunshades in China low latitude region considering daylighting and energy saving requirements, Applied Energy, Volumes 233–234, 2019, 62-70.

[45] [46] Ehrgott, M. Vilfredo Pareto and multi-objective optimization. Doc. Math. 2012, 447–453.

[47]孙澄,韩昀松.绿色性能导向下的建筑数字化节能设计理论研究[J].建筑学报,2016(11):89-93. 

分享让更多人看到