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

FindBook

Google Book

Amazon

博客來

An Equest Based Building Energy Modeling Analysis for Energy Efficiency of Buildings.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	An Equest Based Building Energy Modeling Analysis for Energy Efficiency of Buildings./
作者:	Lamichhane, Saroj.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, : 2021,
面頁冊數:	90 p.
附註:	Source: Masters Abstracts International, Volume: 83-05.
Contained By:	Masters Abstracts International83-05.
標題:	Software. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=28811793
ISBN:	9798494446916

An Equest Based Building Energy Modeling Analysis for Energy Efficiency of Buildings.
Lamichhane, Saroj.

An Equest Based Building Energy Modeling Analysis for Energy Efficiency of Buildings. - Ann Arbor : ProQuest Dissertations & Theses, 2021 - 90 p.

Source: Masters Abstracts International, Volume: 83-05.

Thesis (M.Sc.)--West Virginia University, 2021.

This item must not be sold to any third party vendors.

Building energy performance is a function of numerous building parameters. In this study, sensitivity analysis on twenty parameters is performed to determine the top three parameters which have the most significant impact on the energy performance of buildings. Actual data from two fully operational commercial buildings were collected and used to develop a building energy model in eQUEST. The model is calibrated using Normalized Mean Bias Error (NMBE) and Coefficient of Variation of Root Mean Square Error (CV(RMSE)) method. The model satisfies the NMBE and CV(RMSE) criteria set by the American Society of Heating, Refrigeration, and Air-Conditioning (ASHRAE) Guideline 14, Federal Energy Management Program (FEMP), and International Performance Measurement and Verification Protocol (IPMVP) for building energy model calibration. The values of the parameters are varied in two levels, and then the percentage change in output is calculated. Fractional factorial analysis on eight parameters with the highest percentage change in energy performance is performed at two levels in a statistical software JMP.For Building A, top 3 parameters from percentage change method are: Heating setpoint, cooling setpoint and server room. From fractional factorial design, top 3 parameters are: heating setpoint (p-value= 0.00129), cooling setpoint (p-value= 0.00133), and setback control (p-value= 0.00317). For Building B, top 3 parameters from both methods are: Server room (p-value= 0.0000), heating setpoint (p-value= 0.00014), and cooling setpoint (p-value= 0.00035). If the best values for all top three parameters are taken simultaneously, energy efficiency improves by 29% for Building A and 35 % for Building B.

ISBN: 9798494446916Subjects--Topical Terms:

619355
Software.
Subjects--Index Terms:

Building energy performance

An Equest Based Building Energy Modeling Analysis for Energy Efficiency of Buildings.
LDR:02882nmm a2200361 4500 001 2345042
005 20220531062342.5
008 241004s2021 ||||||||||||||||| ||eng d
020 $a 9798494446916
035 $a (MiAaPQ)AAI28811793
035 $a (MiAaPQ)WVirginia9279
035 $a AAI28811793
040 $a MiAaPQ $c MiAaPQ
100 1 $a Lamichhane, Saroj. $3 3683900
245 1 3 $a An Equest Based Building Energy Modeling Analysis for Energy Efficiency of Buildings.
260 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2021
300 $a 90 p.
500 $a Source: Masters Abstracts International, Volume: 83-05.
500 $a Advisor: Gopalakrishnan, Bhaskaran.
502 $a Thesis (M.Sc.)--West Virginia University, 2021.
506 $a This item must not be sold to any third party vendors.
520 $a Building energy performance is a function of numerous building parameters. In this study, sensitivity analysis on twenty parameters is performed to determine the top three parameters which have the most significant impact on the energy performance of buildings. Actual data from two fully operational commercial buildings were collected and used to develop a building energy model in eQUEST. The model is calibrated using Normalized Mean Bias Error (NMBE) and Coefficient of Variation of Root Mean Square Error (CV(RMSE)) method. The model satisfies the NMBE and CV(RMSE) criteria set by the American Society of Heating, Refrigeration, and Air-Conditioning (ASHRAE) Guideline 14, Federal Energy Management Program (FEMP), and International Performance Measurement and Verification Protocol (IPMVP) for building energy model calibration. The values of the parameters are varied in two levels, and then the percentage change in output is calculated. Fractional factorial analysis on eight parameters with the highest percentage change in energy performance is performed at two levels in a statistical software JMP.For Building A, top 3 parameters from percentage change method are: Heating setpoint, cooling setpoint and server room. From fractional factorial design, top 3 parameters are: heating setpoint (p-value= 0.00129), cooling setpoint (p-value= 0.00133), and setback control (p-value= 0.00317). For Building B, top 3 parameters from both methods are: Server room (p-value= 0.0000), heating setpoint (p-value= 0.00014), and cooling setpoint (p-value= 0.00035). If the best values for all top three parameters are taken simultaneously, energy efficiency improves by 29% for Building A and 35 % for Building B.
590 $a School code: 0256.
650 4 $a Software. $2 gtt. $3 619355
650 4 $a Energy efficiency. $3 3555643
650 4 $a Cooling. $3 1457878
650 4 $a Green buildings. $3 3559500
650 4 $a Energy modeling. $3 3683859
650 4 $a Computer science. $3 523869
650 4 $a Energy. $3 876794
653 $a Building energy performance
653 $a Normalized mean bias error
653 $a Federal Energy Management Program
690 $a 0729
690 $a 0984
690 $a 0791
710 2 $a West Virginia University. $3 1017532
773 0 $t Masters Abstracts International $g 83-05.
790 $a 0256
791 $a M.Sc.
792 $a 2021
793 $a English
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=28811793