Nonlinear model predictive control for a heating and cooling system of a low-energy office building

Schirrer, Alexander; Brandstetter, Markus; Leobner, Ines; Hauer, Stefan; Kozek, Martin

doi:10.1016/j.enbuild.2016.04.029

Cited by 38 publications

(14 citation statements)

References 23 publications

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“…The development of powerful control strategies for renewable energy systems is an important task, as these can increase the efficiency of such systems, support their integration into the current supply systems, and with this, support the transition toward a sustainable energy supply. In particular, model predictive control (MPC) has been shown to be suitable for control of such systems and demonstrated on various applications, for example, for microgrids, 1,2 heat pump systems, 3 energy systems in low energy buildings, 4,5 and solar power plants. 6,7 Within MPC-based control strategies, a model of the controlled system is used to determine a sequence of control signals which are optimal for system operation regarding a defined control objective for a given time horizon, while explicitly considering system dynamics, state and control boundaries, and the current system state.…”

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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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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show abstract

“…Its performance was tested, and the obtained results showed an improvement in indoor thermal comfort, as well a significant energy consumption reduction. Schirrer et al [36] presents a nonlinear modular model predictive optimal control that simultaneously controls the heating and cooling of the spaces in a low-energy office building. The results showed that thermal comfort for the user and building energy efficiency are achieved with excellent control performance and robustness.…”

Section: Introductionmentioning

confidence: 99%

HVAC Systems Applied in University Buildings with Control Based on PMV and aPMV Indexes

et al. 2019

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In this work, HVAC (Heating, Ventilation and Air Conditioning) systems applied in university buildings with control based on PMV (Predicted Mean Vote) and aPMV (adaptive Predicted Mean Vote) indexes are discussed. The building’s thermal behavior with complex topology, in transient thermal conditions, for summer and winter conditions is simulated by software. The university building is divided into 124 spaces, on two levels with an area of 5931 m2, and is composed of 201 transparent surfaces and 1740 opaque surfaces. There are 86 compartments equipped with HVAC systems. The simulation considers the actual occupation and ventilation cycles, the external environmental variables, the internal HVAC system and the occupants’ and building’s characteristics. In this work, a new HVAC control system, designed to simultaneously obtain better occupants’ thermal comfort levels according to category C of ISO 7730 with less energy consumption, is presented. This new HVAC system with aPMV index control is numerically implemented, and its performance is compared with the performance of the same HVAC system with the usual PMV index control. Both HVAC control systems turn on only when the PMV index or the aPMV index reaches values below −0.7, in winter conditions, and when the PMV index or the aPMV index reaches values above +0.7, in summer conditions. In accordance with the results obtained, the HVAC system guarantees negative PMV and aPMV indexes in winter conditions and positive PMV and aPMV indexes in summer conditions. The energy consumption level is higher in winter conditions than in summer conditions for compartments with shading, and it is lower in winter conditions than in summer conditions for compartments exposed to direct solar radiation. The consumption level is higher using the PMV control than with the aPMV control. Air temperature, in accordance with Portuguese standards, is higher than 20 °C in winter conditions and lower than 27 °C in summer conditions. In Mediterranean climates, the HVAC systems with aPMV control provide better occupants’ thermal comfort levels and less energy consumption than the HVAC system with PMV control.

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“…Besides, the principle extent of this paper was to characterize the MPC, detailing the structure and basically discussing the results of various existing MPC calculations for building and HVAC framework administration. The authors in [23] discussed an MPC-based nonlinear control of an air conditioning system for an office in building. Heating and cooling were simultaneously controlled in the office building.…”

Section: Introductionmentioning

confidence: 99%

Optimization of BuildingsEnergy Consumption by Designing Sliding Mode Control for Multizone VAV Air Conditioning Systems

Shah

Huang

et al. 2018

Energies

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Variable air volume (VAV) is the most common installation among heating ventilating and air conditioning (HVAC) systems. To maintain the comfort level and lessen energy utilization, there is a pressing need for its effective control. In this study, a lumped parameter model composed of multizone VAV is considered, and sliding mode control (SMC) is designed to guarantee robust operation in the presence of uncertainties. For comparison of the proposed controller performance, a proportional integral derivative (PID) controller is additionally designed. The indoor temperature of zones is controlled by positioning the supply air dampers. Tracking objectives of controllers are inspected via two practical cases of desired temperature setpoints including (a) sinusoidal waveform and (b) the combination of steps. Results obtained using SMC ensure the robust operation of the VAV system against parametric uncertainties. In addition, SMC is more energy efficient than PID in terms of overshoot and settling time.

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Nonlinear model predictive control for a heating and cooling system of a low-energy office building

Cited by 38 publications

References 23 publications

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

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

HVAC Systems Applied in University Buildings with Control Based on PMV and aPMV Indexes

Optimization of BuildingsEnergy Consumption by Designing Sliding Mode Control for Multizone VAV Air Conditioning Systems

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