Ten questions concerning model predictive control for energy efficient buildings

Killian, Michaela; Kozek, Martin

doi:10.1016/j.buildenv.2016.05.034

Cited by 298 publications

(122 citation statements)

References 75 publications

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“…However they have high cooling output and faster response time as compared to chilled concrete core slabs [5]. They are perfectly suited for buildings with varying cooling loads and can be easily integrated with ventilation supply from the ceiling [4].…”

Section: Radiant Ceiling Panels / Chilled Beamsmentioning

confidence: 99%

Economic Assessment of Radiant Cooling Technique in Air Conditioning Building

M*,

harsha,

et al. 2020

IJRTE

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- Energy supervision in buildings is essential which would manage the energy utilize as well as the cost concerned while maintaining comfort conditions and necessities in indoor environments. In recent years, radiant cooling has acquired a lot of popularity as it can be attributed to the fact that it offers potential to reduce cooling and heating energy consumption by lowering peak loads when coupled with building thermal mass. This can be further enhanced by the application of low energy ventilation systems. Displacement ventilation is one of the techniques to maintain adequate level of air purity and thermal comfort in indoor. The sustainable aspect of these radiant cooling systems to expand the control in Energy and Environmental system design recommendation for low carbon emission in the environment.

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Section: Radiant Ceiling Panels / Chilled Beamsmentioning

confidence: 99%

Economic Assessment of Radiant Cooling Technique in Air Conditioning Building

M*,

harsha,

et al. 2020

IJRTE

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“…In building control level, model predictive based controllers (MPC) have been recently proposed as powerful tools to optimize the operation of smart buildings, to improve human comfort and improve grid stability [28,29]. Recently, a "Ten Questions" paper was published that covered in detail how MPC can improve the energy efficiency of buildings [30]. The accuracy of MPC-based smart building controllers is very much dependent on accurate prediction of factors affecting energy consumption (such as weather, occupancy patterns, or specific characteristics of the building and system equipment), energy prices and other related factors such as the impact on building occupants.…”

Section: Can Integration Of a Smart Grid And Smart Buildings Improve mentioning

confidence: 99%

Ten questions concerning integrating smart buildings into the smart grid

Lawrence

Boudreau

Helsen

et al. 2016

Building and Environment

121

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Recent advances in information and communications technology (ICT) have initiated development of a smart electrical grid and smart buildings. Buildings consume a large portion of the total electricity production worldwide, and to fully develop a smart grid they must be integrated with that grid. Buildings can now be 'prosumers' on the grid (both producers and consumers), and the continued growth of distributed renewable energy generation is raising new challenges in terms of grid stability over various time scales. Buildings can contribute to grid stability by managing their overall electrical demand in response to current conditions. Facility managers must balance demand response requests by grid operators with energy needed to maintain smooth building operations. For example, maintaining thermal comfort within an occupied building requires energy and, thus an optimized solution balancing energy use with indoor environmental quality (adequate thermal comfort, lighting, etc.) is needed. Successful integration of buildings and their systems with the grid also requires interoperable data exchange. However, the adoption and integration of newer control and communication technologies into buildings can be problematic with older legacy HVAC and building control systems. Public policy and economic structures have not kept up with the technical developments that have given rise to the budding smart grid, and further developments are needed in both technical and nontechnical areas.

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“…Building Energy Management Systems (BEMS) is therefore an important part of the ongoing effort to reduce anthropogenic CO 2 emissions. In particular, Model Predictive Control (MPC) has been shown to reduce energy consumption in buildings by utilizing models to predict the thermal behaviour of a building (Fux et al, 2014;Killian and Kozek, 2016). Hence, the development of models of building thermal behaviour has received considerable interest by the scientific community in recent years.…”

Section: Introductions 1backgroundmentioning

confidence: 99%

Sensor placement and parameter identi?ability in grey-box models of building thermal behaviour

Brastein¹,

Sharma²,

Skeie³

2019

Linköping Electronic Conference Proceedings

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Building Energy Management systems can reduce energy consumption for space heating in existing buildings, by utilising Model Predictive Control. In such applications, good models of building thermal behaviour is important. A popular method for creating such models is creating Thermal networks, based cognitively on naive physical information about the building thermal behaviour. Such models have lumped parameters which must be calibrated from measured temperatures and weather conditions. Since the parameters are calibrated, it is important to study the identifiability of the parameters, prior to analysing them as physical constants derived from the building structure. By utilising a statistically founded parameter estimation method based on maximising the likelihood function, identifiability analysis can be performed using the Profile Likelihood method. In this paper, the effect of different sensor locations with respect to the buildings physical properties is studied by utilising likelihood profiles for identifiability analysis. The extended 2D profile likelihood method is used to compute two-dimensional profiles which allows diagnosing parameter inter-dependence, in addition to analysing the identifiability. The 2D profiles are compared with confidence regions computed based on the Hessian.

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Ten questions concerning model predictive control for energy efficient buildings

Cited by 298 publications

References 75 publications

Economic Assessment of Radiant Cooling Technique in Air Conditioning Building

Economic Assessment of Radiant Cooling Technique in Air Conditioning Building

Ten questions concerning integrating smart buildings into the smart grid

Sensor placement and parameter identi?ability in grey-box models of building thermal behaviour

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