New Paths Toward Energy-Efficient Buildings: A Multiaspect Discussion of Advanced Model-Based Control

Atam, Ercan

doi:10.1109/mie.2016.2615127

Cited by 18 publications

(10 citation statements)

References 58 publications

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“…The objective was always to reduce the energy consumption while maintaining a certain (thermal) comfort. The success of MPC in this field was due to that it allows to incorporate statistical uncertainties and even weather forecasts [5], e.g. as in [90].…”

Section: Building Climate and Energymentioning

confidence: 99%

Review on model predictive control: an engineering perspective

Schwenzer

Bergs

et al. 2021

Int J Adv Manuf Technol

301

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Model-based predictive control (MPC) describes a set of advanced control methods, which make use of a process model to predict the future behavior of the controlled system. By solving a—potentially constrained—optimization problem, MPC determines the control law implicitly. This shifts the effort for the design of a controller towards modeling of the to-be-controlled process. Since such models are available in many fields of engineering, the initial hurdle for applying control is deceased with MPC. Its implicit formulation maintains the physical understanding of the system parameters facilitating the tuning of the controller. Model-based predictive control (MPC) can even control systems, which cannot be controlled by conventional feedback controllers. With most of the theory laid out, it is time for a concise summary of it and an application-driven survey. This review article should serve as such. While in the beginnings of MPC, several widely noticed review paper have been published, a comprehensive overview on the latest developments, and on applications, is missing today. This article reviews the current state of the art including theory, historic evolution, and practical considerations to create intuitive understanding. We lay special attention on applications in order to demonstrate what is already possible today. Furthermore, we provide detailed discussion on implantation details in general and strategies to cope with the computational burden—still a major factor in the design of MPC. Besides key methods in the development of MPC, this review points to the future trends emphasizing why they are the next logical steps in MPC.

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Section: Building Climate and Energymentioning

confidence: 99%

Review on model predictive control: an engineering perspective

Schwenzer

Bergs

et al. 2021

Int J Adv Manuf Technol

301

View full text Add to dashboard Cite

show abstract

“…From a system optimization perspective, fuel cell-based studies are conducted mainly using performance objectives such as system efficiency, life-cycle costs, and carbon emissions [9]. While on the other end, optimizing building performance has been widely explored using advanced model-based control strategies, such as model predictive control (MPC) [17][18][19][20][21] and the directions of datadriven methods [22]. The possibility of incorporating fuel cell systems into buildings for enhancing demand response potential is considered in Refs.…”

Section: Introductionmentioning

confidence: 99%

Optimal Energy Dispatch Controller for Fuel Cell-Integrated Multi-Zone Building

Lin

Chinde

Huang

2020

ASME Journal of Engineering for Sustainable Buildings and Cities

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Stationary fuel cells provide potential opportunities for energy savings when integrated with buildings. Through smart dispatch of both electrical power and heat generated by the fuel cells and managing the building loads, the buildings can achieve more efficient operation. In this paper, we develop an optimal energy dispatch controller to operate a fuel cell-integrated building. The controller leverages the inherent thermal storage and the dispatchable fuel cell to reduce its operating cost and to allow the building to participate in grid services. The proposed controller is implemented on two types of commercial buildings, a large office building and a large hotel, and the effectiveness of the controller is demonstrated through simulations.

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Section: Introductionmentioning

confidence: 99%

“…This de facto situation and the importance of the building sector for energy use reduction caused a surge in interest both from control companies (for example, Siemens, Honeywell, Johnson Controls) and the academic community to develop advanced control methods for energy-efficient thermal control of buildings. For example, it was specified in [5,6] that the number of publications on predictive control of buildings (a strain of advanced model-based control methods) in 2015 was 31 times that in 2000. MPC is the most appropriate and popular control method for thermal control of buildings in terms of energy efficiency [7][8][9][10][11] since (i) they have many measurable or predictable disturbances (ambient temperature, solar, and internal gains), (ii) their thermal response time is not short due to their large thermal mass, which can be used to store thermal energy and then use this stored energy effectively later on.…”

Section: Introductionmentioning

confidence: 99%

Investigation of the computational speed of Laguerre network-based MPC in the thermal control of energy-efficient buildings

Atam¹

2017

Turk J Elec Eng & Comp Sci

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Abstract:The design of computationally efficient model predictive control (MPC) systems for the thermal control of buildings is a challenging task since long prediction horizons may be needed, which can take a significant computational time, especially when multizone buildings are considered. In this paper, we investigate the computational performance of a potential approach for this purpose, Laguerre network-based MPC (LN-MPC), for thermal control of buildings, where parameterization of control input over the prediction horizon is used to reduce the number of decision variables. The computational performance of the suggested framework with comparison to the classical MPC framework is investigated through a detailed case study. It was observed that although LN-MPC can produce almost the same results as the classical MPC with a considerably smaller number of decision variables, it has no computational advantage. The potential reasons behind the lack of improvement in the computational performance of LN-MPC are also discussed.

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New Paths Toward Energy-Efficient Buildings: A Multiaspect Discussion of Advanced Model-Based Control

Cited by 18 publications

References 58 publications

Review on model predictive control: an engineering perspective

Review on model predictive control: an engineering perspective

Optimal Energy Dispatch Controller for Fuel Cell-Integrated Multi-Zone Building

Investigation of the computational speed of Laguerre network-based MPC in the thermal control of energy-efficient buildings

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