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

Merema, Bart; Breesch, Hilde; Sælens, Dirk

doi:10.1051/e3sconf/201911101053

Cited by 5 publications

(4 citation statements)

References 14 publications

(16 reference statements)

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“…A prediction horizon of 15 minutes is acceptable if a prediction accuracy lower than the sensor accuracy (0.15°C) is pursued. The absolute prediction errors in other research [5], [10] range from 0.30°C -1°C. Therefore, a prediction horizon of 1 day, obtaining a maximum error of 0.4°C, is acceptable considering the higher prediction errors in comparable literature.…”

Section: Prediction Horizonmentioning

confidence: 84%

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Recommendations for model identification for MPC of an all-Air HVAC system

2021

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Rule-based control (RBC) strategies are often unable to execute the optimal control action, which leads to unnecessary energy consumption and suboptimal comfort. Model predictive control (MPC) is a dynamic control strategy for heating, ventilation and air-conditioning (HVAC) systems that is mostly more capable of performing optimal control actions. The identification process of predictive models is an essential aspect of MPC. However, this model identification process remains time consuming due to the large variation in buildings and systems. The aim of this paper is to determine guidelines to identify predictive grey-box models more time efficient, thus enhancing the applicability of MPC. This paper focusses on a case study building equipped with an all-air HVAC system, which combines ventilation, heating and cooling. Making both temperature and CO2-concentration key parameters to predict. The grey-box model represents an open zone in a landscaped office, making the influence of neighbouring zones an additional challenge. Different models for predicting the zone temperature and CO2-concentration are identified, evaluated and validated using CTSM-R. The following aspects are studied: the dataset size, the influence of neighbouring zones, the difference between winter and summer conditions, number of states and the prediction horizon. A three state RC-model with the implementation of the zone temperature of one neighbouring zone is preferred for predicting the indoor temperature with an acceptable prediction horizon of one day. However, this temperature model is not suitable during sunny periods. A simple model representing a mass balance obtains accurate predictions of the zone CO2-concentration for a timestep of 15 minutes. For both model types the utilization of 5-day datasets is favoured over 12-day datasets due to a shorter monitoring period, lower prediction error and an easier parameter convergence. The usage of 12-day datasets is only preferred when an accurate estimation of the thermal inertia is pursued.

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Section: Prediction Horizonmentioning

confidence: 84%

“…Most research regarding MPC focusses on hydronic systems and less on air-based systems [5]. Therefore, this paper will identify predictive models for an all-air HVAC system.…”

Section: Introductionmentioning

confidence: 99%

Recommendations for model identification for MPC of an all-Air HVAC system

2021

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“…A previous simulation model of the building was created in Modelica (Dymola, 2018) and calibrated according to the ASHRAE Guideline 14 (2002) [34]. A detailed description of this model and related calibration against real measurements is inferred in a previous work [35]. In additions, according to the purposes of the present study, the model has been updated, by using a new version of the same tool (i.e., Dymola 2020) in order to implement the hot water generation section in detail (i.e., the pellet boiler, the tank and the water pumps with related controls).…”

Section: Building Simulation Modelmentioning

confidence: 99%

Evaluation and optimization of the performance of the heating system in a nZEB educational building by monitoring and simulation

Borrelli

Merema

Ascione

et al. 2021

Energy and Buildings

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The optimal control of the HVAC system in the nearly zero energy buildings (nZEBs) can be considered as a challenge.Indeed, the lack of data given by monitored data does not allow understanding what are the implications of different strategies on thermal comfort and energy consumptions. This paper determines an approach for the evaluation of the management of the heating system in an existing educational nZEB. The aim is to understand how the energy efficiency of the heating system is sensitive to controls strategies and the important role played by the control systems. The test lecture rooms on KU Leuven Ghent Technology Campus (Belgium) have been chosen as case study.A deep analysis, starting from an in-field monitoring, was carried out during the winter period of 2019, in order to assess the actual operation and the energy use of building systems under real conditions. Then, a building energy simulation (BES) model of the building and its systems was developed and calibrated against the real monitoring data. Control strategies of the heating system are simulated and compared to the current configuration. The simulation results show that strategies based on a definition of a heating curve for the boiler can reduce the comfortable time (-23%). Controlling the temperature inside the storage tank as a function of the outdoor temperature together with the control on the activation of the air handling unit result in an energy savings from 32% to 46%, with a correspondent increase of comfort time in the period from 0.6% to 3.4% compared to the baseline scenario, depending to the relationship between the external temperature and water temperature inside the water storage tank.

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“…The comfort criteria include the heating set point and minimum airflow rate set point that changes over time. In step 2, a previously identified grey-box model [11] is used as prediction model in the predictive controller. This grey-box model of the room consist of four states: thermal mass temperature, indoor air temperature, supply air temperature and indoor CO2 concentration.…”

Section: Qvent = Mair*ρ*(tsupply-troom)mentioning

confidence: 99%

Implementation of MPC for an all-air system in an educational building

et al. 2021

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The building sector has to significantly reduce the total energy use. A predictive control could be a solution to control an HVAC system more energy efficiently since it takes into account the current measurements and the future demand. In this study a predictive control framework is implemented in an educational building with two lecture rooms. The airflow rate is controlled by VAV boxes based on measurements of CO2 concentration and operative temperature. The dynamic model used for optimization of the control input is a grey-box model, previously identified using measurement data. Weather forecasts and weekly lecture schedules are used as forecasts for the optimization of future control actions. The control actions resulting from the optimization are written to the set points for supply air temperature and VAV damper position using the BACnet interface. Results of the first trial indicate that the predictive control is able to control the room temperature and CO2 concentration, even with uncertainty introduced by the forecasts. Prediction errors observed were 0.17 ˚C for room temperature and 87 ppm for indoor CO2 concentration.

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

Cited by 5 publications

References 14 publications

Recommendations for model identification for MPC of an all-Air HVAC system

Recommendations for model identification for MPC of an all-Air HVAC system

Evaluation and optimization of the performance of the heating system in a nZEB educational building by monitoring and simulation

Implementation of MPC for an all-air system in an educational building

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