{"title":"采用数据驱动方法发现能源变量之间隐藏的复杂关系,并估算美国家庭的能源消耗量","authors":"","doi":"10.1016/j.buildenv.2024.112175","DOIUrl":null,"url":null,"abstract":"<div><div>The U.S. government has committed to improving building energy efficiency. In many buildings, residential homes are one of the largest end-users of energy consumption. Today, many U.S. homes have been in use for decades and they are now outdated, poorly insulated and equipped. Retrofitting existing homes is therefore urgent to improve the quality of Americans’ life and reduce environmental impact from energy waste. To support successful retrofits, this study proposes a decision tree-based analytical model to identify the complex relationships between residential energy variables of physical and socio-economic characteristics using the Residential Energy Consumption Survey (RECS). For this, a model-based recursive partitioning (MOB) algorithm was applied in the decision tree models for understanding energy consumption in residential buildings. The results discovered the most influential energy variables for retrofits and identified heterogeneous relationships on energy consumption for different climatic regions. Also, the findings from decision tree models offer estimations for residential energy consumption in different U.S. climate zones, depending on the combinations of design and operating energy variables. The proposed equations for the <span>EUI</span> estimations can be used to predict the impact of energy variables on primary residential load components (i.e., cooling, heating, domestic hot water loads) to support effective retrofits for architects and homeowners in the future.</div></div>","PeriodicalId":9273,"journal":{"name":"Building and Environment","volume":null,"pages":null},"PeriodicalIF":7.1000,"publicationDate":"2024-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"A data-driven approach to discover hidden complicated relationships of energy variables and estimate energy consumption in U.S. homes\",\"authors\":\"\",\"doi\":\"10.1016/j.buildenv.2024.112175\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>The U.S. government has committed to improving building energy efficiency. In many buildings, residential homes are one of the largest end-users of energy consumption. Today, many U.S. homes have been in use for decades and they are now outdated, poorly insulated and equipped. Retrofitting existing homes is therefore urgent to improve the quality of Americans’ life and reduce environmental impact from energy waste. To support successful retrofits, this study proposes a decision tree-based analytical model to identify the complex relationships between residential energy variables of physical and socio-economic characteristics using the Residential Energy Consumption Survey (RECS). For this, a model-based recursive partitioning (MOB) algorithm was applied in the decision tree models for understanding energy consumption in residential buildings. The results discovered the most influential energy variables for retrofits and identified heterogeneous relationships on energy consumption for different climatic regions. Also, the findings from decision tree models offer estimations for residential energy consumption in different U.S. climate zones, depending on the combinations of design and operating energy variables. The proposed equations for the <span>EUI</span> estimations can be used to predict the impact of energy variables on primary residential load components (i.e., cooling, heating, domestic hot water loads) to support effective retrofits for architects and homeowners in the future.</div></div>\",\"PeriodicalId\":9273,\"journal\":{\"name\":\"Building and Environment\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":7.1000,\"publicationDate\":\"2024-10-10\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Building and Environment\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0360132324010175\",\"RegionNum\":1,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"CONSTRUCTION & BUILDING TECHNOLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Building and Environment","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0360132324010175","RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"CONSTRUCTION & BUILDING TECHNOLOGY","Score":null,"Total":0}
A data-driven approach to discover hidden complicated relationships of energy variables and estimate energy consumption in U.S. homes
The U.S. government has committed to improving building energy efficiency. In many buildings, residential homes are one of the largest end-users of energy consumption. Today, many U.S. homes have been in use for decades and they are now outdated, poorly insulated and equipped. Retrofitting existing homes is therefore urgent to improve the quality of Americans’ life and reduce environmental impact from energy waste. To support successful retrofits, this study proposes a decision tree-based analytical model to identify the complex relationships between residential energy variables of physical and socio-economic characteristics using the Residential Energy Consumption Survey (RECS). For this, a model-based recursive partitioning (MOB) algorithm was applied in the decision tree models for understanding energy consumption in residential buildings. The results discovered the most influential energy variables for retrofits and identified heterogeneous relationships on energy consumption for different climatic regions. Also, the findings from decision tree models offer estimations for residential energy consumption in different U.S. climate zones, depending on the combinations of design and operating energy variables. The proposed equations for the EUI estimations can be used to predict the impact of energy variables on primary residential load components (i.e., cooling, heating, domestic hot water loads) to support effective retrofits for architects and homeowners in the future.
期刊介绍:
Building and Environment, an international journal, is dedicated to publishing original research papers, comprehensive review articles, editorials, and short communications in the fields of building science, urban physics, and human interaction with the indoor and outdoor built environment. The journal emphasizes innovative technologies and knowledge verified through measurement and analysis. It covers environmental performance across various spatial scales, from cities and communities to buildings and systems, fostering collaborative, multi-disciplinary research with broader significance.