Real-life implementation of a linear model predictive control in a building energy system

Sangi, Roozbeh; Kümpel, Alexander; Müller, Dirk

doi:10.1016/j.jobe.2019.01.002

Cited by 24 publications

(8 citation statements)

References 46 publications

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“…Besides, the ON-OFF operation may generate oscillations of the controlled temperature, which leads to wastage of energy in residential buildings. Sometimes, ON-OFF-based controllers cannot be effective in complex energy systems and hence, the appropriate control of variables and objectives cannot be fulfilled with only discrete ON or OFF values [75]. To tackle these issues, PID controllers are introduced in the subsequent subsections.…”

Section: ) Thermostat Controlmentioning

confidence: 99%

“…The control methodology of model predictive control (MPC) is based on the optimal control actions of a dynamical system and its predictions in future evolution [136], thus providing an advanced control strategy for complex building energy systems [75]. Most of the MPC is designed using the discrete linear models achieved by either developing linear autoregressive models with exogenous variables (ARX) models from empirical data or linearizing the state-space models around a certain steady-state point.…”

Section: ) Model-based Predictive Controlmentioning

confidence: 99%

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Intelligent Controllers and Optimization Algorithms for Building Energy Management Towards Achieving Sustainable Development: Challenges and Prospects

et al. 2021

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Buildings account for a significant amount of energy consumption leading to the issues of global emissions and climate change. Thus, energy management in a building is increasingly explored due to its significant potential in reducing the overall electricity expenses for the consumers and mitigating carbon emissions. In line with that, the greater control and optimization of energy management integrated with renewable energy resources is required to improve building energy efficiency while satisfying indoor environment comfort. Even though actions are being taken to reduce the energy consumption in buildings with several optimization and controller techniques, yet some issues remain unsolved. Therefore, this work provides a comprehensive review of the conventional and intelligent control methods with emphasis on their classification, features, configuration, benefits, and drawbacks. This review critically investigates the different optimization objectives and constraints with respect to comfort management, energy consumption, and scheduling. Furthermore, the review outlines the different methodological approaches to optimization algorithms used in building energy management. The contributions of controller and optimization in building energy management with the relation of sustainable development goals (SDGs) are explained rigorously. Discussions on the key challenges of the existing methods are presented to identify the gaps for future research. The review delivers some effective future directions that would be beneficial to the researchers and industrialists to design an efficiently optimized controller for building energy management toward targeting SDGs.INDEX TERMS Building energy management, controller, optimization, scheduling, sustainable development goals I. INTRODUCTIONPresently, buildings take the lead in consuming a substantial amount of energy, indicating about 40% of global energy consumption, which is responsible to release one-third of greenhouse gas (GHG) emissions [1], [2]. Another report demonstrates that buildings hold 49% of the total energy worldwide in which 60% of the energy is consumed for heating and cooling purposes [3], [4]. The poor management and ineffective control approach of appliances used in the building may result in a significant loss of energy in a

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Section: ) Thermostat Controlmentioning

confidence: 99%

Section: ) Model-based Predictive Controlmentioning

confidence: 99%

Intelligent Controllers and Optimization Algorithms for Building Energy Management Towards Achieving Sustainable Development: Challenges and Prospects

et al. 2021

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“…However, complexity is the enemy of dependability [25]. This is especially true in unsupervised embedded implementations such as the presented one, in contrast to supervised MILP implementations on workstation PCs and control rooms that are common in the recent literature [26,27]. MILP formulation of the energy system model can be avoided, as explained in [13].…”

Section: Extension On Mixed Integer and Nonlinear Problem Formulationmentioning

confidence: 99%

Modular Model Composition for Rapid Implementations of Embedded Economic Model Predictive Control in Microgrids

2021

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Economic model predictive control in microgrids combined with dynamic pricing of grid electricity is a promising technique to make the power system more flexible. However, to date, each individual microgrid requires major efforts for the mathematical modelling, the implementation on embedded devices, and the qualification of the control. In this work, a field-suitable generalised linear microgrid model is presented. This scalable model is instantiated on field-typical hardware and in a modular way, so that a class of various microgrids can be easily controlled. This significantly reduces the modelling effort during commissioning, decreases the necessary qualification of commissioning staff, and allows for the easy integration of additional microgrid devices during operation. An exemplary model, derived from an existing production facility microgrid, is instantiated, and the characteristics of the results are analysed.

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“…Examples of simulation studies can be found for multicriteria optimal operation of a microgrid 16 and mixed-integer MPC of variable-speed heat pumps. 17 Examples of experimental studies are the exergy-based control of a heat exchanger within an energy supply system, 18 experimental operation of a microgrid, 19 and control of heating, ventilation, and air conditioning (HVAC) systems 20 and heating systems 21 of buildings.…”

Section: Introductionmentioning

confidence: 99%

Experimental operation of a solar‐driven climate system with thermal energy storages using mixed‐integer nonlinear model predictive control

Bürger

Bull

Sawant

et al. 2021

Optim Control Appl Methods

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This work presents the results of experimental operation of a solar-driven climate system using mixed-integer nonlinear model predictive control (MPC).The system is installed in a university building and consists of two solar thermal collector fields, an adsorption cooling machine with different operation modes, a stratified hot water storage with multiple inlets and outlets as well as a cold water storage. The system and the applied modeling approach is described and a parallelized algorithm for mixed-integer nonlinear MPC and a corresponding implementation for the system are presented. Finally, we show and discuss the results of experimental operation of the system and highlight the advantages of the mixed-integer nonlinear MPC application. K E Y W O R D Sexperimental study, mixed-integer nonlinear programming, model predictive control, renewable energy systems, solar thermal climate systemsThis is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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Real-life implementation of a linear model predictive control in a building energy system

Cited by 24 publications

References 46 publications

Intelligent Controllers and Optimization Algorithms for Building Energy Management Towards Achieving Sustainable Development: Challenges and Prospects

Intelligent Controllers and Optimization Algorithms for Building Energy Management Towards Achieving Sustainable Development: Challenges and Prospects

Modular Model Composition for Rapid Implementations of Embedded Economic Model Predictive Control in Microgrids

Experimental operation of a solar‐driven climate system with thermal energy storages using mixed‐integer nonlinear model predictive control

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