Decarbonization in operational residential buildings worldwide has become critical in achieving the carbon neutral target due to the growing household energy demand.To accelerate the pace of global carbon neutrality,t...Decarbonization in operational residential buildings worldwide has become critical in achieving the carbon neutral target due to the growing household energy demand.To accelerate the pace of global carbon neutrality,this study explores the operational carbon change in global residential buildings through the generalized Divisia index method and decoupling analysis,considering the decarbonization levels of residential buildings at different scales.The results show that(1)most of the samples showed a decrease in the total emissions from 2000 to 2019.Except for China and the United States(US),the carbon emissions in global residential building operations decreased by 7.95 million tons of carbon dioxide(MtCO_(2))per year over the study period.Emissions per gross domestic product(GDP)was the most positive driver causing the decarbonization of residential buildings,while GDP was the most negative driver.(2)Carbon intensity was essential to achieving a strong decoupling of economic development and carbon emissions.The US almost consistently presented strong decoupling,while China showed weak decoupling over the last two decades.(3)The pace of decarbonization in global residential building operations is gradually slowing down.From 2000 to 2019,decarbonization from residential buildings across 30 countries was 2094.3 MtCO_(2),with a decarbonization efficiency of 3.4%.Overall,this study addresses gaps in evaluating global decarbonization from operational residential buildings and provides a reference for evaluating building decarbonization by other emitters.展开更多
Building-Integrated photovoltaics(BIPV)have emerged as a promising sustainable energy solution,relying on accurate energy production predictions and effective decarbonization strategies for efficient deployment.This p...Building-Integrated photovoltaics(BIPV)have emerged as a promising sustainable energy solution,relying on accurate energy production predictions and effective decarbonization strategies for efficient deployment.This paper presents a novel approach that combines photogrammetry and deep learning techniques to address the problem of BIPV decarbonization.The method is called BIM-AITIZATION referring to the integration of BIM data,AI techniques,and automation principles.It integrates photogrammetric data into practical BIM parameters.In addition,it enhances the precision and reliability of PV energy prediction by using artificial intelligence strategies.The primary aim of this approach is to offer advanced,data-driven energy forecasts and BIPV decarbonization while fully automating the underlying process.To achieve this,the first step is to capture point cloud data of the building through photogrammetric acquisition.This data undergoes preprocessing to identify and remove unwanted points,followed by plan segmentation to extract the plan facade.After that,a meteorological dataset is assembled,incorporating various attributes that influence energy production,including solar irradiance parameters as well as BIM parameters.Finally,machine and deep learning techniques are used for accurate photovoltaic energy predictions and the automation of the entire process.Extensive experiments are conducted,including multiple tests aimed at assessing the performance of diverse machine learning models.The objective is to identify the most suitable model for our specific application.Furthermore,a comparative analysis is undertaken,comparing the performance of the proposed model against that of various established BIPV software tools.The outcomes reveal that the proposed approach surpasses existing software solutions in both accuracy and precision.To extend its applicability,the approach is evaluated using a building case study,demonstrating its ability to generalize effectively to new building data.展开更多
The impetus for buildings to decarbonize and move towards radical energy and water efficiency is increasingly strong and identified as a priority within the green building sector.The tiny house movement offers an oppo...The impetus for buildings to decarbonize and move towards radical energy and water efficiency is increasingly strong and identified as a priority within the green building sector.The tiny house movement offers an opportunity to both address the challenges of affordable housing and contribute to residential building decarbonization.Tiny houses de-emphasize mass consumption and excessive belongings and have potential to address equity issues such as gentrification by providing living spaces to low-income residents in desirable housing locations.This paper analyzes the Tiny House in My Backyard(THIMBY)project,investigating building sustainability concepts through the design-build-occupy process in a three-year-old structure.THIMBY demonstrates energy and water efficiency technologies inside an award-winning small living space(18.5 m^(2)).THIMBY was designed to reduce energy and water use by 87 and 82%compared to California residential averages.In practice,it has reduced site energy by 88%and has emitted 96%fewer carbon emissions than a 2100 square foot California Energy Commission 2016 Title 24 minimally compliant home.We discuss the differences between design and performance of energy and water systems,which we find offer important lessons for the further expansion of the tiny house movement and other alternative and micro green housing types.We find that optimizing such houses through integration of energy and water saving technologies,home energy management systems,and strong communication between modelers,builders and occupants will be essential to achieving dramatic energy(87%),water(82%),and carbon(96%)savings.展开更多
基金This manuscript has been authored by an author at Lawrence Berkeley National Laboratory under Contract No.DE-AC02-05CH11231 with the U.S.Department of Energy
文摘Decarbonization in operational residential buildings worldwide has become critical in achieving the carbon neutral target due to the growing household energy demand.To accelerate the pace of global carbon neutrality,this study explores the operational carbon change in global residential buildings through the generalized Divisia index method and decoupling analysis,considering the decarbonization levels of residential buildings at different scales.The results show that(1)most of the samples showed a decrease in the total emissions from 2000 to 2019.Except for China and the United States(US),the carbon emissions in global residential building operations decreased by 7.95 million tons of carbon dioxide(MtCO_(2))per year over the study period.Emissions per gross domestic product(GDP)was the most positive driver causing the decarbonization of residential buildings,while GDP was the most negative driver.(2)Carbon intensity was essential to achieving a strong decoupling of economic development and carbon emissions.The US almost consistently presented strong decoupling,while China showed weak decoupling over the last two decades.(3)The pace of decarbonization in global residential building operations is gradually slowing down.From 2000 to 2019,decarbonization from residential buildings across 30 countries was 2094.3 MtCO_(2),with a decarbonization efficiency of 3.4%.Overall,this study addresses gaps in evaluating global decarbonization from operational residential buildings and provides a reference for evaluating building decarbonization by other emitters.
基金This work was supported by CESI EST and the GRAND EST region.The authors are very grateful to Mourad ZGHAL for fruitful discussions and Benoit DESTENAY(Teacher&responsible in charge of education at CESI school of engineering),Pierre BALLESTER,Cemal OCAKTAN,Oussama OUSSOUS and SOW Mame-Cheikh for technical assistance.The authors are grateful to GBAGUIDI HAORE Sevi(Teacher&responsible in charge of education at CESI school of engineering)and energy expert for his excellent technical support on the subject of the energy decarbonization of buildings.We would like to thank Ophéa-Eurométropole Habitat Strasbourg for allowing us to have the energy production data for these buildings.
文摘Building-Integrated photovoltaics(BIPV)have emerged as a promising sustainable energy solution,relying on accurate energy production predictions and effective decarbonization strategies for efficient deployment.This paper presents a novel approach that combines photogrammetry and deep learning techniques to address the problem of BIPV decarbonization.The method is called BIM-AITIZATION referring to the integration of BIM data,AI techniques,and automation principles.It integrates photogrammetric data into practical BIM parameters.In addition,it enhances the precision and reliability of PV energy prediction by using artificial intelligence strategies.The primary aim of this approach is to offer advanced,data-driven energy forecasts and BIPV decarbonization while fully automating the underlying process.To achieve this,the first step is to capture point cloud data of the building through photogrammetric acquisition.This data undergoes preprocessing to identify and remove unwanted points,followed by plan segmentation to extract the plan facade.After that,a meteorological dataset is assembled,incorporating various attributes that influence energy production,including solar irradiance parameters as well as BIM parameters.Finally,machine and deep learning techniques are used for accurate photovoltaic energy predictions and the automation of the entire process.Extensive experiments are conducted,including multiple tests aimed at assessing the performance of diverse machine learning models.The objective is to identify the most suitable model for our specific application.Furthermore,a comparative analysis is undertaken,comparing the performance of the proposed model against that of various established BIPV software tools.The outcomes reveal that the proposed approach surpasses existing software solutions in both accuracy and precision.To extend its applicability,the approach is evaluated using a building case study,demonstrating its ability to generalize effectively to new building data.
文摘The impetus for buildings to decarbonize and move towards radical energy and water efficiency is increasingly strong and identified as a priority within the green building sector.The tiny house movement offers an opportunity to both address the challenges of affordable housing and contribute to residential building decarbonization.Tiny houses de-emphasize mass consumption and excessive belongings and have potential to address equity issues such as gentrification by providing living spaces to low-income residents in desirable housing locations.This paper analyzes the Tiny House in My Backyard(THIMBY)project,investigating building sustainability concepts through the design-build-occupy process in a three-year-old structure.THIMBY demonstrates energy and water efficiency technologies inside an award-winning small living space(18.5 m^(2)).THIMBY was designed to reduce energy and water use by 87 and 82%compared to California residential averages.In practice,it has reduced site energy by 88%and has emitted 96%fewer carbon emissions than a 2100 square foot California Energy Commission 2016 Title 24 minimally compliant home.We discuss the differences between design and performance of energy and water systems,which we find offer important lessons for the further expansion of the tiny house movement and other alternative and micro green housing types.We find that optimizing such houses through integration of energy and water saving technologies,home energy management systems,and strong communication between modelers,builders and occupants will be essential to achieving dramatic energy(87%),water(82%),and carbon(96%)savings.
