CAREER: Intelligent Energy Retrofit Decisions for Large-scale Residential Buildings
CAREER: Intelligent Energy Retrofit Decisions for Large-scale Residential Buildings
批准号:
2046374
负责人:
Dong Zhao
金额:
$50.56万
依托单位国家:
美国
项目类别:
Continuing Grant
财政年份:
2021
资助国家:
美国
项目状态:
未结题
起止时间:
2021-07-01 至 2026-06-30
中文摘要
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英文摘要
Large-scale retrofits of existing buildings can potentially reduce energy use by 60%-80% and carbon emissions by 30%. Large-scale retrofits are critical in America where more than 44% of existing buildings are expected to be replaced or renovated by 2050. Retrofits of residential buildings have potential to save more energy than commercial buildings. Although the technologies for residential buildings are not complicated, large-scale retrofits are held back due to a lack of understanding human-building interaction and the high complexity of community systems. This knowledge gap results in inaccurate and unnecessary retrofit decisions and causes wasted energy and investment. For example, an energy-efficient stove is not useful if occupants rarely cook. To address this gap, the investigator has created a new concept of routine occupant behavior (ROB) to indicate the stable occupant behaviors that are essential for building retrofit decisions. The research program aims to better understand human-building integration and produce intelligent, cost-effective, and transformative energy retrofit decisions for large-scale residential buildings. Built upon the ROB concept, the PI proposes a highly integrated research and education/outreach plan.The research outcomes targeted are: (1) definition of ROB via a theoretical framework and a detection tool; (2) optimization of whole-community retrofit decisions via dynamic human-building networks; and (3) enablement of across-community retrofit knowledge transfer via an intelligent lifelong learning model. Research outcomes are to be validated using data from four testbeds with up to 3,000 single-family and multifamily units across multiple climate zones and geographic landscapes. The holistic education/outreach program aims to prepare the next generation of convergent researchers and engineers with knowledge, skills, and abilities in green technology and philosophy.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
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Application of occupant behavior prediction model on residential big data analysis
居住者行为预测模型在住宅大数据分析中的应用
DOI:
10.1145/3486611.3491121
发表时间:
2021
期刊:
and Transportation
影响因子:
--
作者:
[Mo, Yunjeong, Zhao, Dong]
通讯作者:
Zhao, Dong
DOI:
10.3390/buildings13061363
发表时间:
2023-06-01
期刊:
BUILDINGS
影响因子:
3.8
作者:
[Zhao,Dong, Mo,Yunjeong]
通讯作者:
Mo,Yunjeong
Spatial Analysis on Routine Occupant Behavior Patterns and Associated Factors in Residential Buildings
住宅建筑中居住者日常行为模式及相关因素的空间分析
DOI:
10.1061/9780784483961.035
发表时间:
2022
期刊:
ASCE's construction research congress
影响因子:
--
作者:
[Mo, Yunjeong, Zhao, Dong]
通讯作者:
Zhao, Dong
DOI:
10.3390/buildings13061425
发表时间:
2023-05
期刊:
Buildings
影响因子:
3.8
作者:
[Lei Shu;Dong Zhao]
通讯作者:
Lei Shu;Dong Zhao
DOI:
10.1016/j.jobe.2021.102891
发表时间:
2021-06
期刊:
Journal of building engineering
影响因子:
6.4
作者:
[Yunjeong Mo;Dong Zhao]
通讯作者:
Yunjeong Mo;Dong Zhao
CAS- Climate. SRS: U.S.-China: Infrastructure-Driven Decision System for Sustainable and Equitable Urban-Rural Development
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批准号:2214872
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项目类别:Continuing Grant
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资助金额:$50.0万
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财政年份:2022
-
负责人:Dong Zhao
-
依托单位:
Research Initiation: Explore student virtual collaborations to prepare for future digital education
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批准号:2204959
-
项目类别:Standard Grant
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资助金额:$20.0万
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财政年份:2022
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负责人:Dong Zhao
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依托单位:
国内基金
海外基金
Intelligent Patent Analysis for Optimized Technology Stack Selection:Blockchain BusinessRegistry Case Demonstration
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批准号:--
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项目类别:外国学者研究基金项目
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资助金额:--
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批准年份:2024
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负责人:USHARANI HAREESH GOVINDARA JAN
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依托单位: