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 thermo-static 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.
- Dynamic Systems and Control Division
ARX Model of a Residential Heating System With Backpropagation Parameter Estimation Algorithm
- Views Icon Views
- Share Icon Share
- Search Site
Burger, EM, & Moura, SJ. "ARX Model of a Residential Heating System With Backpropagation Parameter Estimation Algorithm." Proceedings of the ASME 2017 Dynamic Systems and Control Conference. Volume 3: Vibration in Mechanical Systems; Modeling and Validation; Dynamic Systems and Control Education; Vibrations and Control of Systems; Modeling and Estimation for Vehicle Safety and Integrity; Modeling and Control of IC Engines and Aftertreatment Systems; Unmanned Aerial Vehicles (UAVs) and Their Applications; Dynamics and Control of Renewable Energy Systems; Energy Harvesting; Control of Smart Buildings and Microgrids; Energy Systems. Tysons, Virginia, USA. October 11–13, 2017. V003T42A003. ASME. https://doi.org/10.1115/DSCC2017-5315
Download citation file: