Typical energy-efficient retrofit studies based on urban building energy models face challenges in quickly obtaining appropriate retrofit solutions and often ignore the unexpected outcomes caused by inherent model unc...Typical energy-efficient retrofit studies based on urban building energy models face challenges in quickly obtaining appropriate retrofit solutions and often ignore the unexpected outcomes caused by inherent model uncertainty.To solve it,this study proposes a decision support framework that integrates a hybrid urban building energy model(UBEM)method,NSGA-II,and TOPSIS to obtain rapidly the optimal energy-efficient retrofit solutions that take into account model uncertainty.The study took the building groups in Sipailou campus as a case study and identified 76“stable solutions”and 149“active solutions”that minimize energy consumption,carbon emission,and life-cycle cost(LCC)over 30 years from 40,353,607 retrofit schemes.Key findings include that when considering model uncertainty,the quantities,types,and ranks of optimal retrofit solutions have changed.When the error of baseline UBEM validation is within±5%and considering uncertainty transmission from energy simulation to ANN model,the energy-saving potential of optimal retrofit schemes has expanded from[63.78,65.05]%to[60,68.75]%,carbon-saving potential has shifted from[63.69,64.09]%to[59.92,67.79]%,and the LCC has changed from[−40.68,14.59]×10^(6)to[−38.25,16.97]×10^(6)Yuan.This study provides decision makers with a scientific approach to consider the potential uncertainties and risks associated with optimal retrofit solutions.展开更多
Urban building energy modeling(UBEM)plays a crucial role in analyzing building energy use and has shown that large-scale UBEM can drive energy efficiency and sustainable development through urban retrofitting.However,...Urban building energy modeling(UBEM)plays a crucial role in analyzing building energy use and has shown that large-scale UBEM can drive energy efficiency and sustainable development through urban retrofitting.However,large-scale UBEM presents challenges,including data acquisition workload,frequent parameter adjustments,and long simulation times.Moreover,the workflow connection between UBEM and urban retrofitting pathways remains unclear.Thus,this study proposes a framework that combines a fast,large-scale UBEM method in a Python environment with renewable energy integration to create energy demand-supply networks.The proposed UBEM method utilizes R-tree for geometric repairs,while EPPY efficiently batch-sets simulation parameters based on building function and performs batch simulations with EnergyPlus to quantify energy demand.Energy demand-supply networks are constructed through an improved gravitational model that considers location and functional mix,along with social network analysis.The framework was applied to Nanjing’s historic city center in Jiangsu,China,covering 23,279 buildings across 551 blocks with six functional categories,totaling 54,232,464 m2 of building area.The energy use map reveals that high energy use intensity blocks(over 175 kWh/(m2·year))are distributed in the southern,particularly in commercial and old residential areas,while educational blocks have the highest photovoltaic(PV)potential.The simulation time using the multi-threaded EPPY method was only 14.1%of that with the conventional Ladybug tool for 75 buildings,and about 46.2%for ten urban blocks.Even with PV potential considered,84.2%of blocks have energy demand exceeding supply,necessitating additional retrofitting.Combined retrofits are more effective than individual retrofits,achieving up to a 16.7%energy savings.This study provides new insights into large-scale UBEM and offers valuable decision-making support for energy-efficient urban retrofitting.展开更多
基金sponsored by National Natural Science Foundation of China(Grant Nos.52394224 and 52208011).
文摘Typical energy-efficient retrofit studies based on urban building energy models face challenges in quickly obtaining appropriate retrofit solutions and often ignore the unexpected outcomes caused by inherent model uncertainty.To solve it,this study proposes a decision support framework that integrates a hybrid urban building energy model(UBEM)method,NSGA-II,and TOPSIS to obtain rapidly the optimal energy-efficient retrofit solutions that take into account model uncertainty.The study took the building groups in Sipailou campus as a case study and identified 76“stable solutions”and 149“active solutions”that minimize energy consumption,carbon emission,and life-cycle cost(LCC)over 30 years from 40,353,607 retrofit schemes.Key findings include that when considering model uncertainty,the quantities,types,and ranks of optimal retrofit solutions have changed.When the error of baseline UBEM validation is within±5%and considering uncertainty transmission from energy simulation to ANN model,the energy-saving potential of optimal retrofit schemes has expanded from[63.78,65.05]%to[60,68.75]%,carbon-saving potential has shifted from[63.69,64.09]%to[59.92,67.79]%,and the LCC has changed from[−40.68,14.59]×10^(6)to[−38.25,16.97]×10^(6)Yuan.This study provides decision makers with a scientific approach to consider the potential uncertainties and risks associated with optimal retrofit solutions.
基金sponsored by the National Natural Science Foundation of China(No.52394224,No.52208011).Any opinions,findings,conclusions,or recommendations expressed in this study are those of the authors and do not necessarily reflect the views of those organizations.
文摘Urban building energy modeling(UBEM)plays a crucial role in analyzing building energy use and has shown that large-scale UBEM can drive energy efficiency and sustainable development through urban retrofitting.However,large-scale UBEM presents challenges,including data acquisition workload,frequent parameter adjustments,and long simulation times.Moreover,the workflow connection between UBEM and urban retrofitting pathways remains unclear.Thus,this study proposes a framework that combines a fast,large-scale UBEM method in a Python environment with renewable energy integration to create energy demand-supply networks.The proposed UBEM method utilizes R-tree for geometric repairs,while EPPY efficiently batch-sets simulation parameters based on building function and performs batch simulations with EnergyPlus to quantify energy demand.Energy demand-supply networks are constructed through an improved gravitational model that considers location and functional mix,along with social network analysis.The framework was applied to Nanjing’s historic city center in Jiangsu,China,covering 23,279 buildings across 551 blocks with six functional categories,totaling 54,232,464 m2 of building area.The energy use map reveals that high energy use intensity blocks(over 175 kWh/(m2·year))are distributed in the southern,particularly in commercial and old residential areas,while educational blocks have the highest photovoltaic(PV)potential.The simulation time using the multi-threaded EPPY method was only 14.1%of that with the conventional Ladybug tool for 75 buildings,and about 46.2%for ten urban blocks.Even with PV potential considered,84.2%of blocks have energy demand exceeding supply,necessitating additional retrofitting.Combined retrofits are more effective than individual retrofits,achieving up to a 16.7%energy savings.This study provides new insights into large-scale UBEM and offers valuable decision-making support for energy-efficient urban retrofitting.
