Online building thermal parameter estimation via Unscented Kalman Filtering

Radecki, Peter; Hencey, Brandon

doi:10.1109/acc.2012.6315699

Cited by 60 publications

(39 citation statements)

References 12 publications

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“…For this we leverage the minimal parameterization described in [3]. Any graphs containing cycles (non-tree graphs) are analyzed to multiplicatively cancel out redundant parameters.…”

Section: A Rc Thermal Model Parameterizationmentioning

confidence: 99%

“…Radiation is ignored but an additive heat coefficient term is included to model the influence of the heating system in the building. Although our explanation is brief; a more thorough treatment is offered in [3] or [8].…”

Section: A Rc Thermal Model Parameterizationmentioning

confidence: 99%

“…To date few scalable and costeffective methods exist for optimally controlling buildings because buildings vary vastly in their construction, usage, and heating/cooling equipment [2]. As demonstrated in our previous study [3], gray-box methods, utilizing building topology plus online temperature and weather data, can be used as a low-cost, scalable way to learn passive thermal models that have useful predictive capabilities. Based on our previous work, we propose a managed online framework involving estimation, learning, and control that is scalable to a wide range of buildings.…”

Section: Introductionmentioning

confidence: 98%

“…In [3] we proposed a multi-mode Unscented Kalman Filter (UKF) that learned buildings' multi-zone thermal dynamics and detected unknown time varying thermal loads. Given the multi-zone structure of the building, the UKF initially estimated parameters of that structure using low disturbance periods, and then was augmented to track thermal disturbances while continuing to refine its thermal parameter estimates.…”

Section: Introductionmentioning

confidence: 99%

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Online thermal estimation, control, and self-excitation of buildings

Radecki

Hencey

2013

52nd IEEE Conference on Decision and Control

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This paper investigates a method to improve building control performance via online identification and excitation (active learning process) that does not disrupt normal operations. In previous studies we have demonstrated scalable methods to acquire multi-zone thermal models of passive buildings using a gray-box approach that leverages building topology and measurement data. Here we extend the method to multi-zone actively controlled buildings and examine how to improve the thermal model estimation by using the controller to excite unknown portions of the building dynamics. Comparing against a baseline thermostat controller, we demonstrate the utility of both the initially acquired and improved models with a Model Predictive Control (MPC) framework, which includes weather uncertainty and timevarying temperature set-points. By coupling building topology, estimation, and control routines into a single online framework, we have demonstrated the potential for low-cost scalable methods to actively learn and control buildings for optimal occupant comfort and minimum energy usage, all while using the existing building's HVAC sensors and hardware.

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“…For this we leverage the minimal parameterization described in [3]. Any graphs containing cycles (non-tree graphs) are analyzed to multiplicatively cancel out redundant parameters.…”

Section: A Rc Thermal Model Parameterizationmentioning

confidence: 99%

Section: A Rc Thermal Model Parameterizationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 98%

Section: Introductionmentioning

confidence: 99%

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Online thermal estimation, control, and self-excitation of buildings

Radecki

Hencey

2013

52nd IEEE Conference on Decision and Control

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“…When physical insight is used to propose a model for the building heat dynamics with differential equations, Maximum Likelihood method [Madsen and Holst (1995), Bacher and Madsen (2011)] and Maximum a posteriori estimation [Kristensen et al (2004)] and Kalman Filtering [Maasoumy et al (2013), Fux et al (2014), Martincevic et al (2014), Radecki and Hencey (2012)] have been used for parameter identification. They are called hybrid or grey box modelling approaches that blend the two extremes (white box and black box) in various degrees.…”

Section: Introductionmentioning

confidence: 99%

Structural observability analysis and EKF based parameter estimation of building heating models

Perera¹,

Perera²,

Pfeiffer³

et al. 2016

MIC

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Research for enhanced energy-efficient buildings has been given much recognition in the recent years owing to their high energy consumptions. Increasing energy needs can be precisely controlled by practicing advanced controllers for building Heating, Ventilation, and Air-Conditioning (HVAC) systems. Advanced controllers require a mathematical building heating model to operate, and these models need to be accurate and computationally efficient. One main concern associated with such models is the accurate estimation of the unknown model parameters.This paper presents the feasibility of implementing a simplified building heating model and the computation of physical parameters using an off-line approach. Structural observability analysis is conducted using graph-theoretic techniques to analyze the observability of the developed system model. Then Extended Kalman Filter (EKF) algorithm is utilized for parameter estimates using the real measurements of a single-zone building. The simulation-based results confirm that even with a simple model, the EKF follows the state variables accurately. The predicted parameters vary depending on the inputs and disturbances.

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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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Online building thermal parameter estimation via Unscented Kalman Filtering

Cited by 60 publications

References 12 publications

Online thermal estimation, control, and self-excitation of buildings

Online thermal estimation, control, and self-excitation of buildings

Structural observability analysis and EKF based parameter estimation of building heating models

Building Heat Load Estimation Method Including Parameter Estimation from Actual Data

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