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Online Simultaneous State Estimation and Parameter Adaptation for Building Predictive Control

[+] Author Affiliations
Mehdi Maasoumy, Alberto Sangiovanni-Vincentelli

University of California, Berkeley, CA

Barzin Moridian, Meysam Razmara, Mahdi Shahbakhti

Michigan Technological University, Houghton, MI

Paper No. DSCC2013-4064, pp. V002T23A006; 10 pages
doi:10.1115/DSCC2013-4064
From:
  • ASME 2013 Dynamic Systems and Control Conference
  • Volume 2: Control, Monitoring, and Energy Harvesting of Vibratory Systems; Cooperative and Networked Control; Delay Systems; Dynamical Modeling and Diagnostics in Biomedical Systems; Estimation and Id of Energy Systems; Fault Detection; Flow and Thermal Systems; Haptics and Hand Motion; Human Assistive Systems and Wearable Robots; Instrumentation and Characterization in Bio-Systems; Intelligent Transportation Systems; Linear Systems and Robust Control; Marine Vehicles; Nonholonomic Systems
  • Palo Alto, California, USA, October 21–23, 2013
  • Conference Sponsors: Dynamic Systems and Control Division
  • ISBN: 978-0-7918-5613-0
  • Copyright © 2013 by ASME

abstract

Model-based control of building energy offers an attractive way to minimize energy consumption in buildings. Model-based controllers require mathematical models that can accurately predict the behavior of the system. For buildings, specifically, these models are difficult to obtain due to highly time varying, and nonlinear nature of building dynamics. Also, model-based controllers often need information of all states, while not all the states of a building model are measurable. In addition, it is challenging to accurately estimate building model parameters (e.g. convective heat transfer coefficient of varying outside air). In this paper, we propose a modeling framework for “on-line estimation” of states and unknown parameters of buildings, leading to the Parameter-Adaptive Building (PAB) model. Extended Kalman filter (EKF) and unscented Kalman filter (UKF) techniques are used to design the PAB model which simultaneously tunes the parameters of the model and provides an estimate for all states of the model. The proposed PAB model is tested against experimental data collected from Lakeshore Center building at Michigan Tech University. Our results indicate that the new framework can accurately predict states and parameters of the building thermal model.

Copyright © 2013 by ASME

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