Model predictive control of a building heating system: The first experience

Prívara, Samuel; Široký, Ján; Ferkl, Lukas; Cigler, Jiří

doi:10.1016/j.enbuild.2010.10.022

Cited by 403 publications

(175 citation statements)

References 18 publications

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“…[16][17][18]. While this control aspect is beyond the scope of the paper, readers interested in (optimal) control of GEOTABS buildings are referred to [12,[19][20][21][22][23] for rule-based controllers and to [24][25][26][27][28][29][30][31]) for optimal control.…”

Section: Existing Methodsmentioning

confidence: 99%

Economic Optimal HVAC Design for Hybrid GEOTABS Buildings and CO2 Emissions Analysis

Picard

Helsen

2018

Energies

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Abstract:In the early design phase of a building, the task of the Heating, Ventilation and Air Conditioning (HVAC) engineer is to propose an appropriate HVAC system for a given building. This system should provide thermal comfort to the building occupants at all time, meet the building owner's specific requirements, and have minimal investment, running, maintenance and replacement costs (i.e., the total cost) and energy use or environmental impact. Calculating these different aspects is highly time-consuming and the HVAC engineer will therefore only be able to compare a (very) limited number of alternatives leading to suboptimal designs. This study presents therefore a Python tool that automates the generation of all possible scenarios for given thermal power profiles and energy load and a given database of HVAC components. The tool sizes each scenario properly, computes its present total cost (PC) and the total CO 2 emissions associated with the building energy use. Finally, the different scenarios can be searched and classified to pick the most appropriate scenario. The tool uses static calculations based on standards, manufacturer data and basic assumptions similar to those made by engineers in the early design phase. The current version of the tool is further focused on hybrid GEOTABS building, which combines a GEOthermal heat pump with a Thermally Activated System (TABS). It should further be noted that the tool optimizes the HVAC system but not the building envelope, while, ideally, both should be simultaneously optimized.

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Section: Existing Methodsmentioning

confidence: 99%

Economic Optimal HVAC Design for Hybrid GEOTABS Buildings and CO2 Emissions Analysis

Picard

Helsen

2018

Energies

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“…• Prívara, Široký, Ferkl, and Cigler (2011) reported operation of the whole of a building at the Czech Technical University, Prague comprising seven "blocks" for "February 2010". Experimental details are sparse but energy savings of "17-24%, depending on the particular building block" (p.569) and "29%" were claimed.…”

Section: Evaluation Of Mpc: the Prospects For Energy Savingmentioning

confidence: 99%

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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“…We used a set of two-day (Nov. [11][12]2016) measured data of indoor air temperature, surface temperature of all the walls, cooling supplied by HVAC, solar radiation, and outdoor temperature to identify the optimal parameters of the room model by minimizing the error between the measured temperature and the simulated temperature obtained by the room model based on (8)- (9). The typical values of the parameters in (8)-(9), which are obtained from ASHRAE handbook [31], are used as the initial guesses in this optimization program.…”

Section: Set-up Of the Case Studiesmentioning

confidence: 99%

“…Model predictive control (MPC) is another method usually used to address the problem under uncertainties. Existing results obtained in [10], [12], and [13] showed that MPC features high energy and cost saving since the future evolution of system dynamics is involved in the decision-making process and the optimal solution is obtained based on a rolling horizon basis. Kwadzogah et al [13] provided a review of MPC for HVAC systems.…”

Section: Introductionmentioning

confidence: 99%

PMV-based event-triggered mechanism for building energy management under uncertainties

Spanos

et al. 2017

Energy and Buildings

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This paper provides a study of the optimal scheduling of building operation to minimize its energy cost under building operation uncertainties. Opposed to the usual way that describes thermal comfort using a static range of air temperature, the optimization of a tradeoff between energy cost and thermal comfort predicted mean vote (PMV) index is addressed in this paper. In order to integrate the calculation of the PMV index with the optimization procedure, we develop a sufficiently accurate approximation of the original PMV model which is computationally efficient. We develop a model-based periodic eventtriggered mechanism (ETM) to handle the uncertainties in the building operation. Upon the triggering of predefined events, the ETM determines whether the optimal strategy should be recalculated. In this way, the communication and computational resources required can be significantly reduced. Numerical results show that the ETM method is robust with respect to the uncertainties in prediction errors and results in a reduction of more than 60% in computation without perceivable degradation in system performance as compared to a typical closed-loop model predictive control.

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Model predictive control of a building heating system: The first experience

Cited by 403 publications

References 18 publications

Economic Optimal HVAC Design for Hybrid GEOTABS Buildings and CO2 Emissions Analysis

Economic Optimal HVAC Design for Hybrid GEOTABS Buildings and CO2 Emissions Analysis

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

PMV-based event-triggered mechanism for building energy management under uncertainties

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