Adaptive thermal zone modeling including the storage mass of the building zone

Abstract In the course of the European Project Energy Efficiency and Risk Management in public buildings (EnRiMa), a mathematical model has been needed, predicting the room air temperatures based on the physical properties of the thermal zone and weather forecasts. Existing models based on physical building properties and weather forecasts did not deliver acceptable results. Based on the hypothesis that the missing thermal mass in the existing models is the main reason for the unacceptable results, a model based on physical properties and weather forecast, including the storage mass of a building has been developed. Based on this developed model and real data from a test site, Campus Pinkafeld of the University of Applied Science Burgenland, Austria, the model has been verified and validated. With the new developed model it is possible to predict the occurring room air temperature for a whole day with a maximum deviation of approximately ±1 K, which increases the precision compared to other models.

[1]  Josh Wall,et al.  Adaptive HVAC zone modeling for sustainable buildings , 2010 .

[2]  Vivian Loftness,et al.  Multi-structural fast nonlinear model-based predictive control of a hydronic heating system , 2013 .

[3]  Manfred Morari,et al.  Importance of occupancy information for building climate control , 2013 .

[4]  Sean Hay Kim,et al.  Building demand-side control using thermal energy storage under uncertainty: An adaptive Multiple Model-based Predictive Control (MMPC) approach , 2013 .

[5]  Francesco Borrelli,et al.  Fast stochastic MPC with optimal risk allocation applied to building control systems , 2012, 2012 IEEE 51st IEEE Conference on Decision and Control (CDC).

[6]  Xin Wang,et al.  A novel concept to determine the optimal heating mode of residential rooms based on the inverse problem method , 2015 .

[7]  Petru-Daniel Morosan,et al.  Building temperature regulation using a distributed model predictive control , 2010 .

[8]  Manfred Morari,et al.  Use of model predictive control and weather forecasts for energy efficient building climate control , 2012 .

[9]  Samuel Prívara,et al.  Building modeling: Selection of the most appropriate model for predictive control , 2012 .

[10]  Gregor P. Henze,et al.  A model predictive control optimization environment for real-time commercial building application , 2013 .

[11]  Thierry Talbert,et al.  A procedure for modeling buildings and their thermal zones using co-simulation and system identification , 2014 .

[12]  Brian Coffey,et al.  Approximating model predictive control with existing building simulation tools and offline optimization , 2013 .

[13]  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.

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

[15]  Manfred Morari,et al.  Increasing energy efficiency in building climate control using weather forecasts and model predictive control , 2010 .