Practical implementation and evaluation of model predictive control for an office building in Brussels

Coninck, Roel De; Helsen, Lieve

doi:10.1016/j.enbuild.2015.11.014

Cited by 164 publications

(82 citation statements)

References 13 publications

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“…As reported by others [17,18] cost reductions in energy usage vary between 34-80 %. Nevertheless, the authors in [15] point out that "...the necessary engineering effort for constructing a model still remains the largest unknown factor on the cost side...".…”

Section: What Are the Main Challenges Of An Mpc Implementation?supporting

confidence: 55%

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Ten questions concerning model predictive control for energy efficient buildings

Killian

Kozek

2016

Building and Environment

298

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Section: What Are the Main Challenges Of An Mpc Implementation?supporting

confidence: 55%

“…[4,5,9,13,14]), the number of applications of MPC in real buildings is still limited , e.g. [15,16,17,18,19,20]. The most crucial drawback is the fact that MPC needs a mathematical model of the controlled system which should be able to predict the behavior of the building.…”

Section: What Is the Price Of These Advantages?mentioning

confidence: 98%

Ten questions concerning model predictive control for energy efficient buildings

Killian

Kozek

2016

Building and Environment

298

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“…A model predictive control (MPC) might be a solution as an MPC takes into account the current situation and the future demand. MPC has already shown savings for hydronic systems in operating buildings as indicated in recent studies [3], [4]. In current literature already some studies focused on MPC for ventilation systems.…”

Section: Introductionmentioning

confidence: 93%

Comparison of model identification techniques for MPC in all-air HVAC systems in an educational building

2019

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In school and office buildings the ventilation system has a large contribution to the total energy use. A control strategy that adjusts the operation to the actual demand can significantly reduce the energy use. This is important in rooms with a highly fluctuating occupancy profile. However, a standard rule-based control is reactive, making the installation ‘lag behind’ in relation to the demand. A model predictive control (MPC) might be a solution. To implement an MPC control first a suitable model must be identified for reliable predictions of room temperature and CO2 concentration. For CO2 predictions three scenarios are proposed respectively counting camera, lecture schedule and motion sensor. Two model identification techniques are evaluated: ARX and RC models. For identifying the heating dynamics of the case study building the 3 state RC model showed a good performance, a 5 step ahead prediction on a 15 minute time interval indicated a RMSE of approximately 0.60 °C. The 3rd order ARX model indicated similar results, however the cross validation demonstrated that the RC model outperforms the ARX model. For CO2 predictions the counting scenario resulted in the most accurate n-step ahead predictions. The RMSE found for the RC model is at maximum 90 ppm while 140 ppm for the ARX model. RC models are recommended for modelling all-air HVAC systems attributed by the higher prediction accuracy over ARX models. In addition, these models still contain physical parameters compared to ARX models.

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“…Xiwang et al [29] reviewed building energy modeling for control and operation. De Coninck et al [30] developed a toolbox making validated grey-box building models and did practical implementation of MPC. Ruparathna et al [31] summarized different energy efficiency improvement initiatives and defined a road map for improving the energy performance of operating commercial and institutional buildings.…”

Section: Overview Of the Literature About Mpcmentioning

confidence: 99%

Analysis of New Strategies to Reach Nearly Zero Energy Buildings (nZEBs)

Santos-Herrero

López-Guede

Flores

2018

The 2nd International Research Conference on Sustainable Energy, Engineering, Materials and Environment

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The need to reduce CO2 emissions makes it necessary to review most areas of human activity because we are the main source of these emissions. In particular, the building sector is one of its main responsible. Through a State of the Art, several papers published in various scientific journals about nearly Zero Energy Building (nZEB) studies are reviewed. After carrying out an analysis of said documentation, it is considered that a new way of study can be developed with a multidisciplinary approach. For this, another series of articles are analyzed, where different advances in the field of building construction have been developed and are summarized. It is concluded that the Model Predictive Control (MPC) can also be a useful tool to optimize energy consumption in buildings, especially for public use, in order to achieve the goal of achieving nZEB, through the use of Renewable Energy Sources (RES).

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Practical implementation and evaluation of model predictive control for an office building in Brussels

Cited by 164 publications

References 13 publications

Ten questions concerning model predictive control for energy efficient buildings

Ten questions concerning model predictive control for energy efficient buildings

Comparison of model identification techniques for MPC in all-air HVAC systems in an educational building

Analysis of New Strategies to Reach Nearly Zero Energy Buildings (nZEBs)

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