Model Predictive Climate Control of a Swiss Office Building: Implementation, Results, and Cost–Benefit Analysis

Sturzenegger, David; Gyalistras, Dimitrios; Morari, Manfred; Smith, Roy S.

doi:10.1109/tcst.2015.2415411

Cited by 305 publications

(176 citation statements)

References 15 publications

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“…Several simulation studies [10,31], as well as the first prototypes of MPC implementations for large non-residential buildings [32] show the performance and the readiness level for industrial solutions of this intelligent control scheme. Nevertheless, some obstacles might complicate successful implementations and future market penetration.…”

Section: Possible Obstacles and Necessary Measuresmentioning

confidence: 99%

Hierarchical Model Predictive Control for Sustainable Building Automation

2017

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A hierarchical model predictive controller (HMPC) is proposed for flexible and sustainable building automation. The implications of a building automation system for sustainability are defined, and model predictive control is introduced as an ideal tool to cover all requirements. The HMPC is presented as a development suitable for the optimization of modern buildings, as well as retrofitting. The performance and flexibility of the HMPC is demonstrated by simulation studies of a modern office building, and the perfect interaction with future smart grids is shown.

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Section: Possible Obstacles and Necessary Measuresmentioning

confidence: 99%

Hierarchical Model Predictive Control for Sustainable Building Automation

2017

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“…In addition to the energy savings, some studies start to consider the expenses associated to the control hardware as well as other cost factors [32]. In the proposed novel strategy, there are no special hardware requirements aside from the already supposed On-Off automatization for the BS and the implementation of the proposed software is not expected to cause a significant increase of the computational effort compared to the MPC.…”

Section: Energy Usementioning

confidence: 99%

“…Previous studies have considered a hysteresis control for the blind system (BS) as the one of Sturzenegger et al [32], presenting the results of the OptiControl project or Sourbron's PhD [33] where the office model used for this study is described. The study of Le et al [34] about the influence of the BS in the energetic expenses of a building proposes at first stance a hybrid MPC in which the BSC, integrated in the MPC, operates in four discrete positions.…”

Section: Introductionmentioning

confidence: 99%

Energy Conservation in an Office Building Using an Enhanced Blind System Control

et al. 2017

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Abstract:The two spaces office module is usually considered as a representative case-study to analyse the energetic improvement in office buildings. In this kind of buildings, the use of a model predictive control (MPC) scheme for the climate system control provides energy savings over 15% in comparison to classic control policies. This paper focuses on the influence of solar radiation on the climate control of the office module under Belgian weather conditions. Considering MPC as main climate control, it proposes a novel distributed enhanced control for the blind system (BS) that takes into account part of the predictive information of the MPC. In addition to the savings that are usually achieved by MPC, it adds a potential 15% improvement in global energy use with respect to the usually proposed BS hysteresis control. Moreover, from the simulation results it can be concluded that the thermal comfort is also improved. The proposed BS scheme increases the energy use ratio between the thermally activated building system (TABS) and air-handling unit (AHU); therefore increasing the use of TABS and allowing economic savings, due to the use of more cost-effective thermal equipment.

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“…These usually assume that the model of the system is either perfectly known or found in literature, whereas the task is much more complicated and time consuming in case of a real building and sometimes, it can be even more complex and involved than the controller design itself. After several years of work on using first principles based models for demand response, multiple authors [26,27] have concluded that the biggest hurdle to mass adoption of intelligent building control is the cost and effort required to capture accurate dynamical models of the buildings.…”

Section: Related Workmentioning

confidence: 99%

“…Learning predictive models of building's dynamics using first principles based approaches (e.g. with EnergyPlus [8]) is very cost and time prohibitive and requires retrofitting the building with several sensors [26]. The user expertise, time, and associated sensor costs required to develop a model of a single building is very high.…”

Section: Introductionmentioning

confidence: 99%

Data Predictive Control for Peak Power Reduction

Jain

Mangharam

Behl

2016

Proceedings of the 3rd ACM International Conference on Systems for Energy-Efficient Built Environments

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Decisions on how best to optimize today's energy systems operations are becoming ever so complex and conflicting such that model-based predictive control algorithms must play a key role. However, learning dynamical models of energy consuming systems such as buildings, using grey/white box approaches is very cost and time prohibitive due to its complexity. This paper presents data-driven methods for making controloriented model for peak power reduction in buildings. Specifically, a data predictive control with regression trees (DPCRT) algorithm, is presented. DPCRT is a finite receding horizon method, using which the building operator can optimally trade off peak power reduction against thermal comfort without having to learn white/ grey box models of the systems dynamics. We evaluate the performance of our method using a DoE commercial reference virtual test-bed and show how it can be used for learning predictive models with 90% accuracy, and for achieving 8.6% reduction in peak power and costs. mb2kg@virginia.edu ABSTRACTDecisions on how best to optimize today's energy systems operations are becoming ever so complex and conflicting such that model-based predictive control algorithms must play a key role. However, learning dynamical models of energy consuming systems such as buildings, using grey/white box approaches is very cost and time prohibitive due to its complexity. This paper presents data-driven methods for making control-oriented model for peak power reduction in buildings. Specifically, a data predictive control with regression trees (DPCRT) algorithm, is presented. DPCRT is a finite receding horizon method, using which the building operator can optimally trade off peak power reduction against thermal comfort without having to learn white/grey box models of the systems dynamics. We evaluate the performance of our method using a DoE commercial reference virtual test-bed and show how it can be used for learning predictive models with 90% accuracy, and for achieving 8.6% reduction in peak power and costs.

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Model Predictive Climate Control of a Swiss Office Building: Implementation, Results, and Cost–Benefit Analysis

Cited by 305 publications

References 15 publications

Hierarchical Model Predictive Control for Sustainable Building Automation

Hierarchical Model Predictive Control for Sustainable Building Automation

Energy Conservation in an Office Building Using an Enhanced Blind System Control

Data Predictive Control for Peak Power Reduction

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