Research On Prediction And Optimization Method Of Building Space Performance Under Climate Change

This research focuses on the prediction and optimization of building space performance under the influence of climate change.As known,the impact of climate on building performance is huge.On one hand,there are differences in building performance in different climate zones,and on the other hand,there are differences in building performance in different periods in a certain region.The former has been fully valued in architectural practice and research,while the latter is often ignored.According to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change,the average temperature in most areas of China in the 21^st century may rise by 5 to 7 degrees Celsius.Severe climate change will have a non-negligible impact on building performance.The life cycle of a building is very long,and its construction requires a lot of manpower and material resources.Relevant statistical reports point out that the energy consumed and carbon emissions generated in the operation of buildings are among the highest in all production and living sectors.Improving building performance is of great significance to energy conservation and emission reduction.In the era of rapid climate change,it is far from enough to adapt the building to the local climate at a certain period of time,but also to adapt the building to the changing local climate.To achieve this goal,one of the most important aspects is to optimize the architectural design.Predicting the building performance under climate change is a prerequisite for building space performance optimization research.This research proposes a method for predicting building space performance under climate change based on parametric simulation.A future weather file generation tool for building simulation was developed on Python.The tool can extract future weather data from the general circulation model(GCM)and use the internationally popular"Morphing"calculation method to generate wether files for building simulation.Using this tool,this study predicts the building performance of some typical spaces in various climate zones of China under climate change in the 21^st century.After evaluating the applicability of various GCMs in different China.GISS-E2-R was chosen to generate a batch of weather files for building simulation from 2020 to 2099 for typical regions in each climate zone,and parametric simulation were carried out.The results show that the energy demand trends of typical building spaces in various regions are quite different in the 21^st century.In addition,this study conducted a sensitivity analysis on the impact of several passive design strategies on energy demands of typical building spaces throughout the life cycle under climate change.The analysis results show that architectural design strategies influence energy performance of building space heavily under climate change,and the adoption of appropriate design strategies is conducive to improving the building performance.On the basis of the above-mentioned research work,this research proposes a multi-step rapid optimization method to improve the building space performance under climate change.The optimization method is divided into three main steps:1.Generate a building space performance database under climate change;2.Build artificial neural network prediction models;3.Carry out multi-objective building performance optimization.Each major step contains several tasks.Except that the parametric simulation is executed on Grasshopper,a visual programming platform of Rhino,the rest of the optimization steps are all conducted on Python.This article introduces the content,related tools and precautions involved in each step in detail.Compared with building performance calculations under typical weather conditions,the computing cost of building performance calculations under climate change increases exponentially.Therefore,in this study,when performing multi-objective optimization,the artificial neural networks is selected as the surrogate model to replace the time-consuming building simulation as the fitness evaluation for the optimization algorithm,in order to shorten the time of the optimization process and make this optimization method available for actual building design tasks.At last,this study takes a typical classroom space in hot and humid areas and a typical office space in multi-climate areas as examples to conduct optimization research under climate change.The building performance of the optimized designs has been significantly improved,which proves the effectiveness of the optimization method established in this paper.From the comparative analysis of the optimization results,it can be concluded that climate change has a great influence on the design parameters of the non-dominated solutions and the corresponding building performance indices,which indicates that the climate change factors should be seriously considered when conducting architectural design tasks and building performance optimization research.