東華大學圖書館 |

語系: 繁體中文

說明(常見問題)

回圖書館首頁

手機版館藏查詢

登入

回首頁

切換: 標籤 | MARC模式 | ISBD

Prediction of Urban-Scale Building E...

Lim, Hyunwoo.

FindBook

Google Book

Amazon

博客來

Prediction of Urban-Scale Building Energy Performance with a Stochastic-Deterministic-Coupled Approach.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Prediction of Urban-Scale Building Energy Performance with a Stochastic-Deterministic-Coupled Approach./
作者:	Lim, Hyunwoo.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, : 2017,
面頁冊數:	268 p.
附註:	Source: Dissertation Abstracts International, Volume: 78-10(E), Section: B.
Contained By:	Dissertation Abstracts International78-10B(E).
標題:	Architectural engineering. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10271377
ISBN:	9781369785791

Prediction of Urban-Scale Building Energy Performance with a Stochastic-Deterministic-Coupled Approach.
Lim, Hyunwoo.

Prediction of Urban-Scale Building Energy Performance with a Stochastic-Deterministic-Coupled Approach. - Ann Arbor : ProQuest Dissertations & Theses, 2017 - 268 p.

Source: Dissertation Abstracts International, Volume: 78-10(E), Section: B.

Thesis (Ph.D.)--University of Colorado at Boulder, 2017.

Urban areas consume two-thirds of the world's energy and account for 71% of global greenhouse gas emissions. In the U.S., residential and commercial buildings consume 22% and 19% of the total energy use, respectively. In response to current energy and environmental issues, policymakers have been actively engaged in the establishment of regulations and incentives to promote strategies for energy and greenhouse gas reduction in urban areas. To assist such decision makings requires an accurate and dynamic prediction and analysis of urban energy needs and developing trends, especially for building stocks.

ISBN: 9781369785791Subjects--Topical Terms:

3174102
Architectural engineering.

Prediction of Urban-Scale Building Energy Performance with a Stochastic-Deterministic-Coupled Approach.
LDR:03101nmm a2200313 4500 001 2164269
005 20181106104111.5
008 190424s2017 ||||||||||||||||| ||eng d
020 $a 9781369785791
035 $a (MiAaPQ)AAI10271377
035 $a (MiAaPQ)colorado:14848
035 $a AAI10271377
040 $a MiAaPQ $c MiAaPQ
100 1 $a Lim, Hyunwoo. $3 3352315
245 1 0 $a Prediction of Urban-Scale Building Energy Performance with a Stochastic-Deterministic-Coupled Approach.
260 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2017
300 $a 268 p.
500 $a Source: Dissertation Abstracts International, Volume: 78-10(E), Section: B.
500 $a Adviser: John Zhai.
502 $a Thesis (Ph.D.)--University of Colorado at Boulder, 2017.
520 $a Urban areas consume two-thirds of the world's energy and account for 71% of global greenhouse gas emissions. In the U.S., residential and commercial buildings consume 22% and 19% of the total energy use, respectively. In response to current energy and environmental issues, policymakers have been actively engaged in the establishment of regulations and incentives to promote strategies for energy and greenhouse gas reduction in urban areas. To assist such decision makings requires an accurate and dynamic prediction and analysis of urban energy needs and developing trends, especially for building stocks.
520 $a Five primary challenges exist in modeling urban level building energy uses: (a) lack of building details for massive infrastructures (e.g., building envelope, floor area, age); (b) lack of knowledge of occupant related parameters (e.g., human behaviors, equipment power density, heating and cooling temperature set points); (c) uncertainties in building energy models; (d) unavailability of energy use data for validation; (e) computational effort. To address such challenges, a stochastic-deterministic-coupled modeling approach was developed. In this method, the energy uses of probability-based representative buildings were calculated with a deterministic engineering-based tool (e.g., EnergyPlus) with probabilistic inputs (e.g., building materials, human behaviors).
520 $a Detailed analyses were performed considering the accuracy of estimation and computational time for each step of the process. The analysis of building stock information and the impact of its uncertainty were also examined. The proposed stochastic-deterministic-coupled approach was demonstrated on the campus scale. The proposed model has the following advantages over the existing building stock models: (a) Applicable to various building types; (b) Fast computational time; (c) predictability by energy end-use type; (d) Availability of various temporal and spatial; (e) Availability for retrofit analysis of building stock. The proposed model enables cost-effective energy estimation at large scale considering uncertainties.
590 $a School code: 0051.
650 4 $a Architectural engineering. $3 3174102
690 $a 0462
710 2 $a University of Colorado at Boulder. $b Civil Engineering. $3 1021890
773 0 $t Dissertation Abstracts International $g 78-10B(E).
790 $a 0051
791 $a Ph.D.
792 $a 2017
793 $a English
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10271377