Optimizing building retrofit through data analytics:A study of multi-objective optimization and surrogate models derived from energy performance certificates  

作　　者：G.R.Araújo Ricardo Gomes Paulo Ferrão M.Glória Gomes 

机构地区：[1]IN+,DEM,Técnico Lisboa,Universidade de lisboa,Av Rovisco Pais,Lisboa,1049-001,Portugal [2]CERIS,DECivil,Técnico Lisboa,Universidade de Lisboa,Av Rovisco Pais,Lisboa,1049-001,Portugal

出　　处：《Energy and Built Environment》2024年第6期889-899,共11页能源与人工环境（英文）

基　　金：supported by Fundação para a Ciência e Tecnologia(FCT)through IN+UIDP/EEA/50009/2020-IST-ID,through CERIS UIDB/04625/2020;Ph.D.grant under the contract of FCT 2021.04849.BD.;Project C-TECH-Climate Driven Technologies for Low Carbon Cities,grant number POCI-01-0247-FEDER-045919,LISBOA-01-0247-FEDER-045919,co-financed by the ERDF-European Regional Development Fund through the Operational Program for Competitiveness and Internationalization-COMPETE 2020,the Lisbon Portugal Regional Operational Program-LISBOA 2020 and by the FCT under MIT Portugal Program.

摘　　要：The building stock is responsible for a large share of global energy consumption and greenhouse gas emissions,therefore,it is critical to promote building retrofit to achieve the proposed carbon and energy neutrality goals.One of the policies implemented in recent years was the Energy Performance Certificate(EPC)policy,which proposes building stock benchmarking to identify buildings that require rehabilitation.However,research shows that these mechanisms fail to engage stakeholders in the retrofit process because it is widely seen as a mandatory and complex bureaucracy.This study makes use of an EPC database to integrate machine learning techniques with multi-objective optimization and develop an interface capable of(1)predicting a building’s,or household’s,energy needs;and(2)providing the user with optimum retrofit solutions,costs,and return on investment.The goal is to provide an open-source,easy-to-use interface that guides the user in the building retrofit process.The energy and EPC prediction models show a coefficient of determination(R2)of 0.84 and 0.79,and the optimization results for one case study EPC with a 2000€budget limit inÉvora,Portugal,show decreases of up to 60%in energy needs and return on investments of up to 7 in 3 years.

关 键 词：Building energy performance Building optimization Multi-Objective surrogate models Building retrofitting 

分 类 号：TK11[动力工程及工程热物理—热能工程]