Journal of Zhejiang University SCIENCE A 2026 Vol.27 No.6 P.640-658

http://doi.org/10.1631/jzus.A2500365


Impact of urban heat island and global warming on multi-energy complementarity optimization of buildings: application to typical office buildings in Hangzhou, China


Author(s):  Qingqing MIAO, Xiaoyu LUO, Jiang LU, Weijun GAO, Yucong XUE, Yifan FAN, Jian GE, Jiahong ZHAO

Affiliation(s):  1. College of Civil Engineering and Architecture, Zhejiang University,Hangzhou310058,China more

Corresponding email(s):   gejian1@zju.edu.cn

Key Words:  Future climate change, Urban microclimate, Building energy performance, Building energy system, Multi-objective optimization


Share this article to: More <<< Previous Article|

Qingqing MIAO, Xiaoyu LUO, Jiang LU, Weijun GAO, Yucong XUE, Yifan FAN, Jian GE, Jiahong ZHAO. Impact of urban heat island and global warming on multi-energy complementarity optimization of buildings: application to typical office buildings in Hangzhou, China[J]. Journal of Zhejiang University Science A, 2026, 27(6): 640-658.

@article{title="Impact of urban heat island and global warming on multi-energy complementarity optimization of buildings: application to typical office buildings in Hangzhou, China",
author="Qingqing MIAO, Xiaoyu LUO, Jiang LU, Weijun GAO, Yucong XUE, Yifan FAN, Jian GE, Jiahong ZHAO",
journal="Journal of Zhejiang University Science A",
volume="27",
number="6",
pages="640-658",
year="2026",
publisher="Zhejiang University Press & Springer",
doi="10.1631/jzus.A2500365"
}

%0 Journal Article
%T Impact of urban heat island and global warming on multi-energy complementarity optimization of buildings: application to typical office buildings in Hangzhou, China
%A Qingqing MIAO
%A Xiaoyu LUO
%A Jiang LU
%A Weijun GAO
%A Yucong XUE
%A Yifan FAN
%A Jian GE
%A Jiahong ZHAO
%J Journal of Zhejiang University SCIENCE A
%V 27
%N 6
%P 640-658
%@ 1673-565X
%D 2026
%I Zhejiang University Press & Springer
%DOI 10.1631/jzus.A2500365

TY - JOUR
T1 - Impact of urban heat island and global warming on multi-energy complementarity optimization of buildings: application to typical office buildings in Hangzhou, China
A1 - Qingqing MIAO
A1 - Xiaoyu LUO
A1 - Jiang LU
A1 - Weijun GAO
A1 - Yucong XUE
A1 - Yifan FAN
A1 - Jian GE
A1 - Jiahong ZHAO
J0 - Journal of Zhejiang University Science A
VL - 27
IS - 6
SP - 640
EP - 658
%@ 1673-565X
Y1 - 2026
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/jzus.A2500365


Abstract: 
Amid global warming and urbanization, building energy systems face the dual challenge of balancing growth in energy demand with environmental sustainability and resistance to future climate change. This study proposes a predictive framework that integrates the effects of future climate change and urban microclimate into energy consumption prediction and energy system optimization for typical office buildings in Hangzhou, China. First, optimal general circulation models (GCMs) from CMIP6 are selected through a performance evaluation, and statistical downscaling is employed to generate future typical meteorological year (TMY) data. Next, the urban weather generator (UWG) is used to simulate urban heat island (UHI) effects. Empirical formulas are applied to calculate urban wind speeds, while DesignBuilder is used to model solar radiation and hourly energy consumption. These data are then used to optimize the building energy system. The results reveal that future climate change significantly increases cooling demand (28.9%–103.0%) and reduces heating demand (19.7%–52.6%), with urban microclimates further amplifying these trends. The energy system optimization demonstrates that the net present value (NPV) of future climate and urban microclimate scenarios is 5.1%–16.7% higher than that of historical climate scenarios. Additionally, future climate scenarios result in higher peak energy demand and thus necessitate larger system capacities to ensure reliability. While the initial required investment is higher, buildings optimized to account for global warming are more reliable and carry lower operational costs. We comprehensively quantify the effect of future urban microclimate on building energy systems, emphasizing its critical role in energy system planning and providing insights for addressing the challenges of climate change and urbanization.

城市热岛效应与全球变暖对建筑多能互补能源系统优化的影响:以杭州典型办公建筑为例

作者:苗青青1,2,3,罗晓予1,2,3,陆江4,高伟俊5,薛育聪6,樊一帆1,2,3,葛坚1,3,赵佳红1,2,3
机构:1浙江大学,建筑工程学院,中国杭州,310058;2浙江大学,平衡建筑研究中心,中国杭州,310028;3浙江大学,浙江大学建筑设计研究院有限公司,中国杭州,310028;4浙江科技大学,土木与建筑工程学院,中国杭州,310023;5北九州市立大学,环境工程学院,日本北九州市,8080135;6中国联合工程有限公司,中国杭州,310056
目的:1.为应对全球气候变化与城市化进程对建筑能源系统的双重挑战,本研究旨在建立一个城市背景下的未来气候预测框架,并将其集成至建筑能耗模拟与能源系统优化中。2.以中国夏热冬冷地区的典型办公建筑为对象,量化未来气候变化及城市微气候对建筑制冷、供暖及总能耗的影响,并评估其对能源系统优化的影响,为气候适应型能源规划提供科学依据。
创新点:1.将城市气候影响集成到未来气候变化框架中,综合考虑未来气候变化与城市气候对能源系统优化的影响。2.对CMIP6中的全球气候模型在夏热冬冷地区的适用性展开评估并进行降尺度处理,获得未来逐时气象数据。3.生成涵盖温度、湿度、风速、太阳辐射的全年微气候数据,满足全年能耗模拟需求。
方法:1.评估CMIP6多个全球气候模型在夏热冬冷地区的适用性,筛选三个最优模型,并通过统计降尺度获得未来两个年代的典型气象年数据。2.采用城市天气生成器(UWG)模拟城市热岛效应对温湿度的影响,采用经验公式计算城市风速。3.基于DesignBuilder建立典型办公建筑模型,考虑周围建筑群遮挡,模拟全年逐时能耗。4.将能耗数据和气象文件用于建筑能源系统优化,并分析不同气候情景下的系统容量配置和经济性。
结论:1.未来气候变化使杭州地区典型办公建筑制冷需求增加28.9%~103.0%,供暖需求降低19.7%~52.6%,总能耗增加3.9%~15.0%;城市微气候进一步放大这些趋势。2.在能源系统优化中,考虑未来气候与城市微气候的系统净现值较历史气候情景提高5.1%~16.7%。3.未来气候下的能耗峰值更高,需更大系统配置以保证可靠性;这虽然增加初始投资,但提升了运行灵活性、降低了运行成本。因此,建筑能源系统规划应纳入未来气候变化与城市微气候的双重影响。

关键词:未来气候变化;城市微气候;建筑能源性能;建筑能源系统;多目标优化

Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article

Reference

[1]AbdinZ,MéridaW,2019.Hybrid energy systems for off-grid power supply and hydrogen production based on renewable energy: a techno-economic analysis.Energy Conversion and Management,196:1068-1079.

[2]AkkoseG,AkgulCM,DinoIG,2021.Educational building retrofit under climate change and urban heat island effect.Journal of Building Engineering,40:102294.

[3]AliabadiAA,McleodRM,2023.The vatic weather file generator (VWFG v1.0.0).Journal of Building Engineering,67:105966.

[4]Al-SharafiA,SahinAZ,AyarT,et al.,2017.Techno-economic analysis and optimization of solar and wind energy systems for power generation and hydrogen production in Saudi Arabia.Renewable & Sustainable Energy Reviews,69:33-49.

[5]AnJJ,WuY,GuiCX,et al.,2023.Chinese prototype building models for simulating the energy performance of the nationwide building stock.Building Simulation,16(8):1559-1582.

[6]AshrafianT,2023.Enhancing school buildings energy efficiency under climate change: a comprehensive analysis of energy, cost, and comfort factors.Journal of Building Engineering,80:107969.

[7]BeckC,StraubA,BreitnerS,et al.,2018.Air temperature characteristics of local climate zones in the Augsburg urban area (Bavaria, southern Germany) under varying synoptic conditions.Urban Climate,25:152-166.

[8]BelcherSE,HackerJN,PowellDS,2005.Constructing design weather data for future climates.Building Services Engineering Research & Technology,26(1):49-61.

[9]BellNO,BilbaoJI,KayM,et al.,2022.Future climate scenarios and their impact on heating, ventilation and air-conditioning system design and performance for commercial buildings for 2050.Renewable and Sustainable Energy Reviews,162:112363.

[10]BoccalatteA,FossaM,ThebaultM,et al.,2023.Mapping the urban heat island at the territory scale: an unsupervised learning approach for urban planning applied to the canton of Geneva.Sustainable Cities and Society,96:104677.

[11]BuenoB,NorfordL,HidalgoJ,et al.,2013.The urban weather generator.Journal of Building Performance Simulation,6(4):269-281.

[12]ChanALS,2011.Developing future hourly weather files for studying the impact of climate change on building energy performance in Hong Kong.Energy and Buildings,43(10):2860-2868.

[13]ChenYX,RenZY,PengZW,et al.,2023.Impacts of climate change and building energy efficiency improvement on city-scale building energy consumption.Journal of Building Engineering,78:107646.

[14]ContiS,NicolosiR,RizzoSA,et al.,2012.Optimal dispatching of distributed generators and storage systems for MV islanded microgrids.IEEE Transactions on Power Delivery,27(3):1243-1251.

[15]DiasJB,da GraçaGC,SoaresPMM,2020.Comparison of methodologies for generation of future weather data for building thermal energy simulation.Energy and Buildings,206:109556.

[16]FathimaAH,PalanisamyK,2015.Optimization in microgrids with hybrid energy systems – a review.Renewable and Sustainable Energy Reviews,45:431-446.

[17]FazlollahiS,BeckerG,AshouriA,et al.,2015.Multi-objective, multi-period optimization of district energy systems: IV – a case study.Energy,84:365-381.

[18]GeorgakisC,SantamourisM,2008.On the estimation of wind speed in urban canyons for ventilation purposes—Part 1: coupling between the undisturbed wind speed and the canyon wind.Building and Environment,43(8):1404-1410.

[19]GuanLS,2009.Implication of global warming on air-conditioned office buildings in Australia.Building Research & Information,37(1):43-54.

[20]GuanLS,2012.Energy use, indoor temperature and possible adaptation strategies for air-conditioned office buildings in face of global warming.Building and Environment,55:8-19.

[21]GuarinoF,TumminiaG,LongoS,et al.,2022.An integrated building energy simulation early-design tool for future heating and cooling demand assessment.Energy Reports,8:10881-10894.

[22]HosseiniM,BigtashiA,LeeB,2021.Generating future weather files under climate change scenarios to support building energy simulation – a machine learning approach.Energy and Buildings,230:110543.

[23]HosseiniM,JavanroodiK,NikVM,2022.High-resolution impact assessment of climate change on building energy performance considering extreme weather events and microclimate – investigating variations in indoor thermal comfort and degree-days.Sustainable Cities and Society,78:103634.

[24]IPCC (Intergovernmental Panel on Climate Change),2023.Climate Change 2021 – the Physical Science Basis: Working Group I Contribution to the 6th Assessment Report of the Intergovernmental Panel on Climate Change.Technical Report,Cambridge University Press,Cambridge, UK.

[25]JiaQM,JiaHF,LiYJ,et al.,2023.Applicability of CMIP5 and CMIP6 models in China: reproducibility of historical simulation and uncertainty of future projection.Journal of Climate,36(17):5809-5824.

[26]KamalA,MahfouzA,SezerN,et al.,2023.Investigation of urban heat island and climate change and their combined impact on building cooling demand in the hot and humid climate of Qatar.Urban Climate,52:101704.

[27]LauzetN,RodlerA,MusyM,et al.,2019.How building energy models take the local climate into account in an urban context – a review.Renewable and Sustainable Energy Reviews,116:109390.

[28]LiJX,ZhangQQ,EtienneXL,2024.Optimal carbon emission reduction path of the building sector: evidence from China.Science of the Total Environment,919:170553.

[29]LiQ,ChenJY,LuoXW,2024.Estimating omnidirectional urban vertical wind speed with direction-dependent building morphologies.Energy and Buildings,303:113749.

[30]LiXS,ZhangJ,HeY,et al.,2021.Multi-objective optimization dispatching of microgrid based on improved particle swarm algorithm.Electric Power Science and Engineering,37(3):1-7(in Chinese).

[31]LitardoJ,PalmeM,Borbor-CordovaM,et al.,2020.Urban heat island intensity and buildings’ energy needs in Duran, Ecuador: simulation studies and proposal of mitigation strategies.Sustainable Cities and Society,62:102387.

[32]LuoL,AbdulkareemSS,RezvaniA,et al.,2020.Optimal scheduling of a renewable based microgrid considering photovoltaic system and battery energy storage under uncertainty.Journal of Energy Storage,28:101306.

[33]LvGQ,ZhaoK,QinYL,et al.,2022.An urban-scale method for building roofs available wind resource evaluation based on aerodynamic parameters of urban sublayer surfaces.Sustainable Cities and Society,80:103790.

[34]MacdonaldRW,GriffithsRF,HallDJ,1998.An improved method for the estimation of surface roughness of obstacle arrays.Atmospheric Environment,32(11):1857-1864.

[35]MaroufmashatA,ElkamelA,FowlerM,et al.,2015.Modeling and optimization of a network of energy hubs to improve economic and emission considerations.Energy,93:2546-2558.

[36]MathiesenP,StadlerM,KleisslJ,et al.,2021.Techno-economic optimization of islanded microgrids considering intra-hour variability.Applied Energy,304:117777.

[37]MaureeD,CoccoloS,PereraATD,et al.,2018.A new framework to evaluate urban design using urban microclimatic modeling in future climatic conditions.Sustainability,10(4):1134.

[38]MOHURD (Ministry of Housing and Urban-Rural Development),1993.Standard of Climatic Regionalization for Architecture, GB 50178-1993.National Standards of the People’s Republic of China.

[39]MOHURD (Ministry of Housing and Urban-Rural Development),2015.Design Standard for Energy Efficiency of Public Buildings, GB 50189-2015.National Standards of the People’s Republic of China.

[40]NikkhoSK,HeidarinejadM,LiuJY,et al.,2017.Quantifying the impact of urban wind sheltering on the building energy consumption.Applied Thermal Engineering,116:850-865.

[41]PereraATD,NikVM,ChenDL,et al.,2020.Quantifying the impacts of climate change and extreme climate events on energy systems.Nature Energy,5(2):150-159.

[42]PereraATD,JavanroodiK,NikVM,2021.Climate resilient interconnected infrastructure: co-optimization of energy systems and urban morphology.Applied Energy,285:116430.

[43]PereraATD,JavanroodiK,MaureeD,et al.,2023.Challenges resulting from urban density and climate change for the EU energy transition.Nature Energy,8(4):397-412.

[44]P.TootkaboniM,BallariniI,ZinziM,et al.,2021.A comparative analysis of different future weather data for building energy performance simulation.Climate,9(2):37.

[45]RanJY,SongY,ZhouSY,et al.,2024a.A bi-level optimization method for regional integrated energy system considering uncertainty and load prediction under climate change.Journal of Building Engineering,84:108527.

[46]RanJY,QiuYB,LiuJZ,et al.,2024b.Coordinated optimization design of buildings and regional integrated energy systems based on load prediction in future climate conditions.Applied Thermal Engineering,241:122338.

[47]RodriguesE,FernandesMS,CarvalhoD,2023.Future weather generator for building performance research: an open-source morphing tool and an application.Building and Environment,233:110104.

[48]SalvatiA,KolokotroniM,2023.Urban microclimate and climate change impact on the thermal performance and ventilation of multi-family residential buildings.Energy and Buildings,294:113224.

[49]SalvatiA,MontiP,RouraHC,et al.,2019.Climatic performance of urban textures: analysis tools for a Mediterranean urban context.Energy and Buildings,185:162-179.

[50]SantamourisM,GeorgakisC,NiachouA,2008.On the estimation of wind speed in urban canyons for ventilation purposes—Part 2: using of data driven techniques to calculate the more probable wind speed in urban canyons for low ambient wind speeds.Building and Environment,43(8):1411-1418.

[51]ShiYR,XiangYR,ZhangYF,2019.Urban design factors influencing surface urban heat island in the high-density city of Guangzhou based on the local climate zone.Sensors,19(16):3459.

[52]StewartID,OkeTR,2012.Local climate zones for urban temperature studies.Bulletin of the American Meteorological Society,93(12):1879-1900.

[53]TamerT,DinoIG,AkgülCM,2022.Data-driven, long-term prediction of building performance under climate change: building energy demand and BIPV energy generation analysis across turkey.Renewable and Sustainable Energy Reviews,162:112396.

[54]TaylorKE,2001.Summarizing multiple aspects of model performance in a single diagram.Journal of Geophysical Research: Atmospheres,106(D7):7183-7192.

[55]TsokaS,VelikouK,TolikaK,et al.,2021.Evaluating the combined effect of climate change and urban microclimate on buildings’ heating and cooling energy demand in a Mediterranean city.Energies,14(18):5799.

[56]WangX,2023.Study on Operation Optimization of Integrated Energy System Considering Source-Load Uncertainty Under Climate Change.PhD Thesis,North China Electric Power University,Beijing, China(in Chinese).

[57]XuanWD,MaC,KangLL,et al.,2017.Evaluating historical simulations of CMIP5 GCMs for key climatic variables in Zhejiang province, China.Theoretical and Applied Climatology,128(1-2):207-222.

[58]YangJJ,ZhangQL,PengCY,et al.,2024.Autobps-prototype: a web-based toolkit to automatically generate prototype building energy models with customizable efficiency values in China.Energy and Buildings,305:113880.

[59]YangXS,PengLLH,JiangZD,et al.,2020.Impact of urban heat island on energy demand in buildings: local climate zones in Nanjing.Applied Energy,260:114279.

[60]YinS,XiaoSY,DingXT,et al.,2024.Improvement of spatial-temporal urban heat island study based on local climate zone framework: a case study of Hangzhou, China.Building and Environment,248:111102.

[61]YoshinoH,HongTZ,NordN,2017.IEA EBC annex 53: total energy use in buildings—analysis and evaluation methods.Energy and Buildings,152:124-136.

[62]ZhangZL,2024.Optimization Configuration of Carbon Neutral Energy System Considering Climate Change, Hybrid Residual Energy Utilization, and Market Incentives. MS Thesis,Nanchang University,Nanchang, China(in Chinese).

[63]ZhaoX,MaXW,ChenBY,et al.,2022.Challenges toward carbon neutrality in China: strategies and countermeasures.Resources, Conservation and Recycling,176:105959.

[64]ZouJW,LuH,ShuC,et al.,2023.Multiscale numerical assessment of urban overheating under climate projections: a review.Urban Climate,49:101551.

[65]ZouYK,XiangK,ZhanQS,et al.,2021.A simulation-based method to predict the life cycle energy performance of residential buildings in different climate zones of China.Building and Environment,193:107663.

Open peer comments: Debate/Discuss/Question/Opinion

<1>

Please provide your name, email address and a comment





Full Text:   <934>

Summary:  <171>

Suppl. Mater.: 

CLC number: 

On-line Access: 2026-06-24

Received: 2025-07-31

Revision Accepted: 2026-01-26

Crosschecked: 2026-06-24

Cited: 0

Clicked: 1109

Citations:  Bibtex RefMan EndNote GB/T7714

 ORCID:

Jian GE

https://orcid.org/0000-0002-1619-575X

Journal of Zhejiang University-SCIENCE, 38 Zheda Road, Hangzhou 310027, China
Tel: +86-571-87952783; E-mail: cjzhang@zju.edu.cn
Copyright © 2000 - 2026 Journal of Zhejiang University-SCIENCE