Maasoumy, Mehdi (UC Berkeley), Moridian, Barzin (Michigan Technological University), Razmara, Meysam (Michigan Technological University), Shahbakhti, Mahdi (Michigan Technological University), Sangiovanni Vincentelli, Alberto (UNIVERSITY OF CALIFORNIA BERKELEY)

Online Simultaneous State Estimation and Parameter Adaptation for Building Predictive Control

Scheduled for presentation during the Invited session "Estimation and Identification of Energy Systems" (TuAT4), Tuesday, October 22, 2013, 11:55−12:15, Paul Brest West

6th Annual Dynamic Systems and Control Conference, October 21-23, 2020, Stanford University, Munger Center, Palo Alto, CA

This information is tentative and subject to change. Compiled on April 24, 2024

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 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 with experimental data collected from a university building. Our results indicate that the new framework can accurately predict state and parameters of the building thermal model. The new modeling framework is expected to simplify design of a building predictive control by replacing nonlinear terms in a control model with linear adaptive parameters.