Recursive parameter estimation of thermostatically controlled loads via unscented Kalman filter

“…(ii) System identification based approaches: System identification techniques are concerned with using statistical methods to construct mathematical models of dynamical systems. Most of the previous works of this category focus on modeling room temperatures in the presence of HVAC utilizing various strategies, for example, Monte-Carlo simulation [31,32], Kalman filters [33], Evolutionary algorithms [34], and Least Square Estimator [35]. While these works concentrate on solely building parameter identification, none of them actually consider finding the occupancy distribution.…”

Section: Introductionmentioning

confidence: 99%

Mobility Map Inference from Thermal Modeling of a Building

Islam,

Lokhov,

Lemons

et al. 2020

Preprint

View full text Add to dashboard Cite

We consider the problem of inferring the mobility map, which is the distribution of the building occupants at each time stamp, from the temperatures of the rooms. We also want to explore the effects of noise in the temperature measurement, room layout etc. in the reconstruction of the movement of people within the building. Our proposed algorithm tackles down the aforementioned challenges leveraging a parameter learner, the modified Least Square Estimator. In the absence of a complete data set with mobility map, room and ambient temperatures and HVAC data in the public domain, we simulate a physics-based thermal model of the rooms in a building and evaluate the performance of our inference algorithm on this simulated data. We find an upper bound of the noise standard deviation (η std ≤ 1F ) in the input temperature data of our model. Within this bound, our algorithm can reconstruct the mobility map with reasonable reconstruction error. Our work can be used in wide range of applications, for example, ensuring the physical security of office buildings, elderly and infant monitoring, building resources management, emergency building evacuation, and vulnerability assessment of HVAC data. Our work brings together multiple research areas, Thermal Modeling and Parameter Estimation, towards achieving a common goal of inferring the distribution of people within a large office building.

show abstract

“…The processes that determine the evolution of temperatures within a building are complex and uncertain. A reliable model improves the ability of a controller to forecast conditions and meet cost, efficiency, and/or comfort objectives [9,10]. Simulation software, such as EnergyPlus and TRNSYS, is capable of high fidelity modeling of building HVAC systems.…”

Section: Introductionmentioning

confidence: 99%

ARX Model of a Residential Heating System With Backpropagation Parameter Estimation Algorithm

Burger

Moura

2017

Volume 3: Vibration in Mechanical Systems; Modeling and Validation; Dynamic Systems and Control Education; Vibrations and Contr

Self Cite

View full text Add to dashboard Cite

Model predictive control (MPC) strategies hold great potential for improving the performance and energy efficiency of building heating, ventilation, and air-conditioning (HVAC) systems. A challenge in the deployment of such predictive thermostatic control systems is the need to learn accurate models for the thermal characteristics of individual buildings. This necessitates the development of online and data-driven methods for system identification. In this paper, we propose an autoregressive with exogenous terms (ARX) model of a thermal zone within a building. To learn the model, we present a backpropagation approach for recursively estimating the parameters. Finally, we fit the linear model to data collected from a residential building with a forced-air heating and ventilation system and validate the accuracy of the trained model.

show abstract

Recursive parameter estimation of thermostatically controlled loads via unscented Kalman filter

Cited by 10 publications

References 29 publications

Boundary dynamic feedback control for a class of semi‐linear distributed parameter systems

Boundary dynamic feedback control for a class of semi‐linear distributed parameter systems

Mobility Map Inference from Thermal Modeling of a Building

ARX Model of a Residential Heating System With Backpropagation Parameter Estimation Algorithm

Contact Info

Product

Resources

About