國立虎尾科技大學 |

Model Predictive Control for Retrofit Rooftop Unit Control.

紀錄類型:	書目-語言資料,手稿 : Monograph/item
正題名/作者:	Model Predictive Control for Retrofit Rooftop Unit Control./
作者:	Weskalnies, Paul.
面頁冊數:	1 online resource (235 pages)
附註:	Source: Masters Abstracts International, Volume: 57-04.
標題:	Architecture. -
電子資源:	click for full text (PQDT)
ISBN:	9780355647815

Model Predictive Control for Retrofit Rooftop Unit Control.
Weskalnies, Paul.

Model Predictive Control for Retrofit Rooftop Unit Control. - 1 online resource (235 pages)

Source: Masters Abstracts International, Volume: 57-04.

Thesis (M.S.)--University of Colorado at Boulder, 2017.

Includes bibliographical references

This thesis explores the viability of model predictive control strategies for a retrofit rooftop unit control solution developed by start-up technology provider Transformative Wave, called the CATALYST. This research has been conducted in conjunction with a research project funded by the Wells Fargo Innovation Incubator program, in collaboration with the National Renewable Energy Laboratory (NREL) to enhance the commercial retrofit rooftop unit control solution with optimum control development. The goal is to develop a simple, easily implementable model predictive controller to further reduce energy costs in commercial retail buildings. This thesis extends previously developed building performance simulation models and model predictive control tools to provide insight into the demands of model development and baseline optimum control results. Model development is approached by taking data provided only from the CATALYST controllers to estimate model parameters of a building and its HVAC systems sight unseen. Additional optimization tasks are evaluated to test the effectiveness of the building to improve electric grid integration and achieve carbon reductions. It was found that the MPC, within the simulation environment, was best able to reduce peak demand utility costs and to improve the building in terms of grid relationship but at the cost of increased energy consumption and carbon emissions. In terms of utility cost savings, the addition of model predictive control was able to save approximately $243 a month in utility demand charges at an increased cost of $8.42 a week resulting in total utility bill cost savings of $210 a month. This suggests an annual demand cost savings of 5% at a 2% energy cost increase.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2018

Mode of access: World Wide Web

ISBN: 9780355647815Subjects--Topical Terms:

555123
Architecture.
Index Terms--Genre/Form:

554714
Electronic books.

Model Predictive Control for Retrofit Rooftop Unit Control.
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245 1 0 $a Model Predictive Control for Retrofit Rooftop Unit Control.
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500 $a Source: Masters Abstracts International, Volume: 57-04.
500 $a Adviser: Gregor P. Henze.
502 $a Thesis (M.S.)--University of Colorado at Boulder, 2017.
504 $a Includes bibliographical references
520 $a This thesis explores the viability of model predictive control strategies for a retrofit rooftop unit control solution developed by start-up technology provider Transformative Wave, called the CATALYST. This research has been conducted in conjunction with a research project funded by the Wells Fargo Innovation Incubator program, in collaboration with the National Renewable Energy Laboratory (NREL) to enhance the commercial retrofit rooftop unit control solution with optimum control development. The goal is to develop a simple, easily implementable model predictive controller to further reduce energy costs in commercial retail buildings. This thesis extends previously developed building performance simulation models and model predictive control tools to provide insight into the demands of model development and baseline optimum control results. Model development is approached by taking data provided only from the CATALYST controllers to estimate model parameters of a building and its HVAC systems sight unseen. Additional optimization tasks are evaluated to test the effectiveness of the building to improve electric grid integration and achieve carbon reductions. It was found that the MPC, within the simulation environment, was best able to reduce peak demand utility costs and to improve the building in terms of grid relationship but at the cost of increased energy consumption and carbon emissions. In terms of utility cost savings, the addition of model predictive control was able to save approximately $243 a month in utility demand charges at an increased cost of $8.42 a week resulting in total utility bill cost savings of $210 a month. This suggests an annual demand cost savings of 5% at a 2% energy cost increase.
533 $a Electronic reproduction. $b Ann Arbor, Mich. : $c ProQuest, $d 2018
538 $a Mode of access: World Wide Web
650 4 $a Architecture. $3 555123
655 7 $a Electronic books. $2 local $3 554714
690 $a 0729
710 2 $a ProQuest Information and Learning Co. $3 1178819
710 2 $a University of Colorado at Boulder. $b Civil Engineering. $3 1178930
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10682965 $z click for full text (PQDT)