A preference-based multi-objective building performance optimization method for early design stage

A large number of cases show that the multi-objective optimization method can significantly improve building performance.The method for multi-objective building performance optimization(BPO)design has achieved rapid development in recent years.However,the BPO method still needs to be improved.Specifically,weak interaction between the optimization process and the decision-making process results in low optimization efficiency,which limits the widespread application of the optimization method in early design stage.In this paper,a new interactive BPO mode is explored to strengthen the interaction between the optimization process and decisionmaking process,and a preference-based multi-objective BPO method is proposed to account for designers'decision preferences during the optimization process,making the objective more controllable,improving the optimization efficiency and ensuring the diversity of solutions.Firstly,this paper illustrates the proposed method in detail,defines the concept of performance preference,expounds the flow of the preference-based multi-objective optimization algorithm,and proposes three indicators to evaluate the algorithm,which includes convergence speed,preference satisfaction rate,and diversity measurement.Secondly,through testing and comparison,it is found that the proposed preference-based algorithm has advantages over the non-preference optimization algorithm(represented by the NSGA-II algorithm).The proposed method leads to faster convergence and higher preference satisfaction,so it is more suitable for the BPO process in the early design stage.Specially,the proposed method can achieve 100%preference satisfaction rate with only 2400 simulations,while the non-preference method can only achieve 20%preference satisfaction rate after 5800 simulations.In this paper,a preference-based multi-objective BPO method is proposed to make the optimization process closely interact with the decision-making process and make the design preferences be accounted during the BPO process,thereby improving the optimization efficiency.In addition,this study first proposes two indicators to measure the quality of optimization results:preference satisfaction rate and diversity measurement.This study aims to guide the development of BPO methods towards providing high satisfaction rate and high quality optimization results.

Development and Application of Decision Support Model for the Performance Optimization of Office Buildings Based on Grasshopper

With the expansion of the office building area,the energy consumption of office buildings is growing.High⁃performance building design contributes to energy saving and the development of green buildings.However,there is a lack of high⁃performance building tools and the workflow is often time⁃consuming.The building performance simulation,multiple objective optimizations,and the decision support model are the new approaches of high⁃performance building design.This paper proposes a newly developed decision support model,a high⁃performance building decision model named HPBuildingDSM,which integrates the building performance simulation,building performance multiple objective optimizations,building performance sampling,and parameter sensitivity analysis to design high⁃performance office buildings.In this research,the HPBuildingDSM was operated to search for the desirable office building design results with low⁃energy and high⁃quality daylighting performances.The simulated results had better daylighting performance and lower energy consumption,whose UDI100-2000 was 37.94%and annual energy consumption performance was 76.28 kWh/(m2·a),indicating a better building performance than the optimized results in the previous case study.