Nonlinear model reduction and model predictive control of residential buildings with energy recovery

Abstract Residential and commercial buildings account for a significant portion of the electricity consumed in the United States. Their operation is subject to fluctuations in weather and occupancy which, in turn, are reflected in large variations in the load that buildings impose on the grid during the day and at night time. In view of mitigating such fluctuations (and their broader impact on energy generation), understanding the dynamic behavior of buildings and a focus on energy management (rather than simply temperature control), is essential. In this paper, we begin by analyzing building dynamics and use singular perturbation arguments to provide a theoretical justification for the empirically acknowledged multiple time scale dynamic response of buildings. We also derive reduced-order models for the dynamics in each time scale for a prototype residential building. Our analysis accounts for the potential use of heat recovery ventilators (HRVs), and we show that the presence of energy recovery leads to the emergence of a dynamic behavior with three time scales, including an overall, system-wide component which involves both the building and the HVAC system. We use our dynamic results to formulate a set of synthesis guidelines for control systems addressing either temperature regulation or geared towards minimizing operating cost. A detailed simulation case study demonstrates the application of the derived reduced-order models in the design of a nonlinear predictive model-based optimal energy management strategy for a model of a single-zone test building situated on the University of Texas campus. The proposed controller exhibits excellent performance, can easily be executed in real-time and has the capability to shift peak loads as part of a demand flattening strategy.

[1]  Prashant Mhaskar,et al.  Energy efficient model predictive building temperature control , 2012 .

[2]  Stéphane Ginestet,et al.  Thermal identification of building multilayer walls using reflective Newton algorithm applied to quadrupole modelling , 2013 .

[3]  坂口 隆彦.  石炭ガス化複合発電(IGCC)技術開発の動向 , 2013 .

[4]  Martine Baelmans,et al.  Efficiently produced heat and cold is squandered by inappropriate control strategies: A case study , 2009 .

[5]  Tuan Anh Nguyen,et al.  Energy intelligent buildings based on user activity: A survey , 2013 .

[6]  Michael Baldea,et al.  Tight energy integration: Dynamic impact and control advantages , 2010, Comput. Chem. Eng..

[7]  Bin Wang,et al.  Efficiency of energy recovery ventilator with various weathers and its energy saving performance in a residential apartment , 2010 .

[8]  Mohammad Rasouli,et al.  Applicability and optimum control strategy of energy recovery ventilators in different climatic conditions , 2010 .

[9]  Manfred Morari,et al.  Modeling and identification of a large multi-zone office building , 2011, 2011 IEEE International Conference on Control Applications (CCA).

[10]  Moncef Krarti,et al.  An Overview of Artificial Intelligence-Based Methods for Building Energy Systems , 2003 .

[11]  Viktor Dorer,et al.  Control of thermally-activated building systems (TABS) , 2008 .

[12]  L.J.S. Lukasse,et al.  Optimal climate control of a storage facility using local weather forecasts , 2003 .

[13]  Atila Novoselac,et al.  Use of model predictive control to enhance the flexibility of thermal energy storage cooling systems , 2012, 2012 American Control Conference (ACC).

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

[15]  Petru-Daniel Morosan,et al.  A distributed MPC strategy based on Benders' decomposition applied to multi-source multi-zone temperature regulation , 2011 .

[16]  Timothy I. Salsbury,et al.  A SURVEY OF CONTROL TECHNOLOGIES IN THE BUILDING AUTOMATION INDUSTRY , 2005 .

[17]  Peng Xu,et al.  Demand reduction in building energy systems based on economic model predictive control , 2012 .

[18]  Prabir Barooah,et al.  A method for model-reduction of non-linear thermal dynamics of multi-zone buildings , 2012 .

[19]  Sten Bay Jørgensen,et al.  Unreachable Setpoints in Model Predictive Control , 2008, IEEE Transactions on Automatic Control.

[20]  M. M. Gouda,et al.  Building thermal model reduction using nonlinear constrained optimization , 2002 .

[21]  Zhenjun Ma,et al.  Supervisory and Optimal Control of Building HVAC Systems: A Review , 2008 .

[22]  Michael Baldea,et al.  Dynamics and Nonlinear Control of Integrated Process Systems , 2012 .

[23]  Francesco Borrelli,et al.  A distributed predictive control approach to building temperature regulation , 2011, Proceedings of the 2011 American Control Conference.

[24]  Zheng O'Neill,et al.  A methodology for meta-model based optimization in building energy models , 2012 .

[25]  J. Bontsema,et al.  Singular perturbation methods applied to a variational problem in greenhouse climate control , 1992, [1992] Proceedings of the 31st IEEE Conference on Decision and Control.

[26]  Fariborz Haghighat,et al.  A software framework for model predictive control with GenOpt , 2010 .

[27]  Prashant Mhaskar,et al.  Offset-free model predictive control of a vapor compression cycle , 2012 .

[28]  S. Joe Qin,et al.  A survey of industrial model predictive control technology , 2003 .

[29]  Manfred Morari,et al.  Reducing peak electricity demand in building climate control using real-time pricing and model predictive control , 2010, 49th IEEE Conference on Decision and Control (CDC).

[30]  Alfonso P. Ramallo-González,et al.  Lumped parameter models for building thermal modelling: An analytic approach to simplifying complex multi-layered constructions , 2013 .

[31]  Gabriele Pannocchia,et al.  Disturbance models for offset‐free model‐predictive control , 2003 .

[32]  Michael Baldea,et al.  Nonlinear model predictive control of energy-integrated process systems , 2013, Syst. Control. Lett..

[33]  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.

[34]  Thomas F. Edgar,et al.  An improved method for nonlinear model reduction using balancing of empirical gramians , 2002 .

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

[36]  Thomas F. Edgar,et al.  Process energy systems: Control, economic, and sustainability objectives , 2012, Comput. Chem. Eng..

[37]  Si-Zhao Joe Qin,et al.  Model predictive control of building energy systems with balanced model reduction , 2012, 2012 American Control Conference (ACC).

[38]  Alberto Hernandez Neto,et al.  Comparison between detailed model simulation and artificial neural network for forecasting building energy consumption , 2008 .