Online Model Estimation for Predictive Thermal Control of Buildings

Radecki, Peter; Hencey, Brandon

doi:10.1109/tcst.2016.2587737

Cited by 26 publications

(18 citation statements)

References 22 publications

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“…Starting from a common standard model, the MPC controller would thus learn from the functioning of the systems, and adapt the model parameters given the response of the specific building to heating or cooling excitation found in its past data. Such methods are being investigated and represent promising research paths [170], [171]. It would be particularly relevant in the context of Mediterranean climate studied in the present thesis, where the model would need changes notably between the heating and cooling seasons: an online parameter estimation method would adapt the model little by little as time passes by, instead of an abrupt transition.…”

Section: Conclusion From the Co-simulation Casesmentioning

confidence: 99%

Heat Pump Controls to Exploit the Energy Flexibility of Building Thermal Loads

Péan

2021

Springer Theses

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Section: Conclusion From the Co-simulation Casesmentioning

confidence: 99%

Heat Pump Controls to Exploit the Energy Flexibility of Building Thermal Loads

Péan

2021

Springer Theses

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“…Adaptive MPC schemes for building control in which both the unknown parameters and unmeasured states are estimated during operation, using EKF and UKF were proposed in (Fux, 2013), (Maasoumy et al, 2014) and (Radecki and Hencey, 2015). Simulations using these schemes the estimates of the unknown building parameters typically converge after a couple of weeks of operation.…”

Section: State and Parameter Estimation With An Unscented Kalman Filtermentioning

confidence: 99%

“…4) of the energy supply model, based on an Unscented Kalman Filter (UKF), (Julier et al, 1995), leading to the Parameter-Adaptive Energy Supply Model. Adaptive MPC schemes for building control in which both the unknown parameters and unmeasured states are estimated using UKF or Extended Kalman Filters (EKF), (Peter S. Maybeck, 1979) and (Peter S. Maybeck, 1982), were investigated in (Fux, 2013), (Maasoumy et al, 2014) and (Radecki and Hencey, 2015). The two latter compared EKF and UKF and both concluded, that UKF is superior.…”

Section: Introductionmentioning

confidence: 99%

Model Predictive Control for Building Automation

Bolt¹,

Jaeger²,

Zierbart³

et al. 2018

Proceedings of EuroSun 2018

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We propose a building HVAC system, integrating local energy production and storage, together with a model based controller. The heating system integrates several local heat production and storage devices and multiple fluid circuits at different temperatures to minimize entropy production through mixing. The controller uses a model of the system and predictive knowledge of demand and weather information to minimize electrical energy import, while maintaining thermal comfort by solving mixed integer optimization problems online. Time-varying and unknown system parameters are estimated and adapted online, using an unscented Kalman filter. The adaptation greatly reduces modeling effort and maintenance cost. The proposed setup is tested in a co-simulation, using a physical (modelica-) model of the building and energy system as well as realistic weather and demand data. Our system delivers nearly seven times more energy in the form of heat, than it needs to import (electrical) energy from external sources.

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“…Other research works use building energy simulation software to produce the static house load profiles. The software tools such as EnergyPlus, Building Energy Optimization (BEopt), eQUEST, etc., are able to model houses with various characteristics in different locations [33]- [40]. The Integrated District Energy Assessment (IDEA) provides another method to model and control the building energy usage considering the environment, networks and building characteristics [41].…”

Section: Introductionmentioning

confidence: 99%

Dynamic Modeling and Optimal Design for Net Zero Energy Houses Including Hybrid Electric and Thermal Energy Storage

Gong

Rallabandi

Ionel

et al. 2020

IEEE Trans. on Ind. Applicat.

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Net zero energy (NZE) houses purchase zero net metered electricity from the grid over a year. Technical challenges brought forth by NZE homes are related to the intermittent nature of solar generation, and are due to the fact that peak solar generation and load are not coincident. This leads to a large rate of change of load, and in case of high PV penetration communities, often requires the installation of gas power plants to service this variability. This paper proposes a hybrid energy storage system including batteries and a variable power electric water heater which enables the NZE homes to behave like dispatchable generators or loads, thereby reducing the rate of change of the net power flow from the house. A co-simulation framework, INSPIRE+D, which enables the dynamic simulation of electricity usage in a community of NZE homes, and their connection to the grid is enabled. The calculated instantaneous electricity usage is validated through experimental data from a field demonstrator in southern Kentucky. It is demonstrated that when the operation of the proposed hybrid energy storage system is coordinated with solar PV generation, the required size and ratings of the battery would be substantially reduced while still maintaining the same functionality. Methodologies for sizing the battery and solar panels are developed. Index Terms-Net Zero Energy (NZE) houses, Home Energy Management (HEM), Electrical Water Heater (EWH), Battery Energy Storage System (BESS), Virtual Power Plant (VPP).

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Online Model Estimation for Predictive Thermal Control of Buildings

Cited by 26 publications

References 22 publications

Heat Pump Controls to Exploit the Energy Flexibility of Building Thermal Loads

Heat Pump Controls to Exploit the Energy Flexibility of Building Thermal Loads

Model Predictive Control for Building Automation

Dynamic Modeling and Optimal Design for Net Zero Energy Houses Including Hybrid Electric and Thermal Energy Storage

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