Building Efficiency and Sustainability in the Tropics ( SinBerBEST ) Title Online Simultaneous State Estimation and Parameter Adaptation for Building Predictive

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.

[1]  Alexandre M. Bayen,et al.  Combined state-parameter estimation for shallow water equations , 2011, Proceedings of the 2011 American Control Conference.

[2]  Alberto L. Sangiovanni-Vincentelli,et al.  Total and Peak Energy Consumption Minimization of Building HVAC Systems Using Model Predictive Control , 2012, IEEE Design & Test of Computers.

[3]  S. Haykin.  Kalman Filtering and Neural Networks , 2001 .

[4]  Alberto L. Sangiovanni-Vincentelli,et al.  Co-design of control algorithm and embedded platform for building HVAC systems , 2013, 2013 ACM/IEEE International Conference on Cyber-Physical Systems (ICCPS).

[5]  Mehdi Maasoumy.  Modeling and Optimal Control Algorithm Design for HVAC Systems in Energy Efficient Buildings , 2014 .

[6]  Frauke Oldewurtel,et al.  Building modeling as a crucial part for building predictive control , 2013 .

[7]  Gregory L. Plett,et al.  Sigma-point Kalman filtering for battery management systems of LiPB-based HEV battery packs Part 2: Simultaneous state and parameter estimation , 2006 .

[8]  Jeffrey K. Uhlmann,et al.  New extension of the Kalman filter to nonlinear systems , 1997, Defense, Security, and Sensing.

[9]  Max Donath,et al.  American Control Conference , 1993 .

[10]  Mark A. Goforth,et al.  Cloud effects on thermal downwelling sky radiance , 2002, SPIE Defense + Commercial Sensing.

[11]  H.F. Durrant-Whyte,et al.  A new approach for filtering nonlinear systems , 1995, Proceedings of 1995 American Control Conference - ACC'95.

[12]  Alberto Sangiovanni-Vincentelli,et al.  Model-Based Hierarchical Optimal Control Design for HVAC Systems , 2014 .

[13]  Soroosh Sorooshian,et al.  Dual state-parameter estimation of hydrological models using ensemble Kalman filter , 2005 .

[14]  Bruce H. Krogh,et al.  Parameter identifiability for multi-zone building models , 2012, 2012 IEEE 51st IEEE Conference on Decision and Control (CDC).

[15]  H. Musoff,et al.  Unscented Kalman Filter , 2015 .

[16]  M Morari,et al.  Energy efficient building climate control using Stochastic Model Predictive Control and weather predictions , 2010, Proceedings of the 2010 American Control Conference.

[17]  Rudolph van der Merwe,et al.  The unscented Kalman filter for nonlinear estimation , 2000, Proceedings of the IEEE 2000 Adaptive Systems for Signal Processing, Communications, and Control Symposium (Cat. No.00EX373).

[18]  Prahlad Patel,et al.  Modeling and optimal control algorithm design for HVAC systems in energy efficient buildings , 2013 .