Building modeling as a crucial part for building predictive control

Prívara, Samuel; Cigler, Jiří; Váňa, Zdeněk; Oldewurtel, Frauke; Sagerschnig, Carina; Žáčeková, Eva

doi:10.1016/j.enbuild.2012.10.024

Cited by 304 publications

(138 citation statements)

References 40 publications

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“…Such an approach by analogy is often termed a grey-box model and is restricted to linear models. Prívara et al (2013) remark that this approach performed better than the more principled 4SID subspace method (Cigler & Prívara, 2010). Since it requires detailed knowledge of the building structure, the grey-box approach can be expected to suffer from similar shortcomings as white-box modelling.…”

Section: Creation Of the Predictive Modelmentioning

confidence: 99%

“…In fact, this figure of 70% does not appear unique to MPC in buildings -in the wider process-control community, the figure of 75% of project costs being attributable to modelling is widely quoted (Gevers, 2005;Hussain, 1999). The important topic of model generation for building MPC has recently been reviewed by Prívara et al (2013) from which it is clear that much work remains to be done.…”

Section: Creation Of the Predictive Modelmentioning

confidence: 99%

“…Further, the notorious problem of quality assurance in the construction industry means that what is actually built sometimes differs from what the designers have specified. Prívara et al (2013) suggest that methods which rely on a physical description of the building are suitable only for simpler structures.…”

Section: Creation Of the Predictive Modelmentioning

confidence: 99%

“…A further, hybrid approach to constructing predictive models uses an electrical circuit (resistor-capacitor, or RC) analogy to associate every first-order linear process with resistors and capacitors (Levermore, 2000;Coley & Penman, 1992;Prívara et al, 2013). Such an approach by analogy is often termed a grey-box model and is restricted to linear models.…”

Section: Creation Of the Predictive Modelmentioning

confidence: 99%

“…Currently, almost all non-domestic buildings employ rule-based controllers, also known as Building Management Systems (BMSs) or Building Energy Management Systems (BEMs) (Levermore, 2000), the fundamental technical basis of which has been criticised by numerous authors, for example, see (Prívara et al, 2013). In essence, the BMS approximates the closed-loop response of the building with a set of handcrafted if-then-else rules.…”

Section: Introductionmentioning

confidence: 99%

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Model-predictive control for non-domestic buildings: a critical review and prospects

Rockett

Hathway

2016

Building Research & Information

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Model-predictive control (MPC) has recently excited a great deal of interest as a new control paradigm for non-domestic buildings. Since it is based on the notion of optimisation, MPC is, in principle, well-placed to deliver significant energy savings and reduction in carbon emissions compared to existing rule-based control systems. In this paper, we critically review the prospects for buildings MPC and, in particular, the central role of the predictive mathematical model that lies at its heart; our clear emphasis is on practical implementation rather than control-theoretic aspects, and covers the role of occupants as well as the form of the predictive model. The most appropriate structure for such a model is still an open question, which we discuss alongside the development of the initial model, and the process of updating the model during the building's operational life. The importance of sensor placement is highlighted alongside the possibility of updating the model with occupants' comfort perception. We conclude that there is an urgent need for research on the automated creation and updating of predictive models if MPC is to become an economically-viable control methodology for non-domestic buildings. Finally, more evidence through operating full scale buildings with MPC is required to demonstrate the viability of this method.

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Section: Creation Of the Predictive Modelmentioning

confidence: 99%

Section: Creation Of the Predictive Modelmentioning

confidence: 99%

Section: Creation Of the Predictive Modelmentioning

confidence: 99%

Section: Creation Of the Predictive Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Model-predictive control for non-domestic buildings: a critical review and prospects

Rockett

Hathway

2016

Building Research & Information

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Building Heat Load Estimation Method Including Parameter Estimation from Actual Data

Nakamura

Kawamura

2021

IEEJ Transactions Elec Engng

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In recent years, various method of air conditioning control and making operation plans have been developed. They require a building thermal model, but for making the model detailed design data is needed. Several model construction methods have been proposed up to now, but they are for buildings surrounded by walls made with one material. In this situation for deriving the room temperature and air conditioner power consumption, a parameter estimation method for estimating heat capacities and resistances of walls with different physical properties was developed. This method consists of two steps. At the first step, in steady temperature state, the least-squares method is used to derive thermal resistances. In the second step, in non-steady temperature state, an Unscented Kalman Filter (UKF) is used to derive heat capacities and solar-radiation shading coefficients. UKF is adopted for numerical stability and short operation time. With this method, it is possible to obtain the independent parameters of each wall by repeating the evaluation using a simple model. These steps were applied to data obtained by simulation. As a result, the physical parameter error of the inner and outer walls was sometimes 10-60%. On the other hand, regarding to the estimation target, the error of the indoor temperature estimation was 0.3 • C, and the error of power consumption estimation was less than 4%, so high estimation accuracy was obtained. This confirmed the effectiveness of the proposed method.

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Inverse Model Identification of the Thermal Dynamics of a Norwegian Zero Emission House

Vogler-Finck¹,

Clauß

Georges

et al. 2018

Springer Proceedings in Energy

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Project managers, Sweco Ulf and Håkan will be presenting experiences from relocation of heritage buildings due to the expanding iron ore mine in Kiruna. 10:30 Bus leaves to Scandic Ferrum Group 2 9:00 Bus leaves from Scandic Ferrum to the iron ore mine 9:30 Keynote speaker: Ulf Svennemyr and Håkan Ylvin-Project managers, Sweco Ulf and Håkan will be presenting experiences from relocation of heritage buildings due to the expanding iron ore mine in Kiruna.

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Building modeling as a crucial part for building predictive control

Cited by 304 publications

References 40 publications

Model-predictive control for non-domestic buildings: a critical review and prospects

Model-predictive control for non-domestic buildings: a critical review and prospects

Building Heat Load Estimation Method Including Parameter Estimation from Actual Data

Inverse Model Identification of the Thermal Dynamics of a Norwegian Zero Emission House

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