Reducing Transient and Steady State Electricity Consumption in HVAC Using Learning-Based Model-Predictive Control

Heating, ventilation, and air conditioning (HVAC) systems are an important target for efficiency improvements through new equipment and retrofitting because of their large energy footprint. One type of equipment that is common in homes and some offices is an electrical, single-stage heat pump air conditioner (AC). To study this setup, we have built the Berkeley Retrofitted and Inexpensive HVAC Testbed for Energy Efficiency (BRITE) platform. This platform allows us to actuate an AC unit that controls the room temperature of a computer laboratory on the Berkeley campus that is actively used by students, while sensors record room temperature and AC energy consumption. We build a mathematical model of the temperature dynamics of the room, and combining this model with statistical methods allows us to compute the heating load due to occupants and equipment using only a single temperature sensor. Next, we implement a control strategy that uses learning-based model-predictive control (MPC) to learn and compensate for the amount of heating due to occupancy as it varies throughout the day and year. Experiments on BRITE show that our techniques result in a 30%-70% reduction in energy consumption as compared to two-position control, while still maintaining a comfortable room temperature. The energy savings are due to our control scheme compensating for varying occupancy, while considering the transient and steady state electrical consumption of the AC. Our techniques can likely be generalized to other HVAC systems while still maintaining these energy saving features.

[1]  E. Gilder,et al.  The Authors , 1977 .

[2]  J. MacArthur.  Transient heat pump behaviour: a theoretical investigation , 1984 .

[3]  V W Goldschmidt,et al.  Heat Pumps: Basics, Types, and Performance Characteristics , 1984 .

[4]  P. Robinson.  ROOT-N-CONSISTENT SEMIPARAMETRIC REGRESSION , 1988 .

[5]  S. Sastry,et al.  Adaptive Control: Stability, Convergence and Robustness , 1989 .

[6]  Karl Johan Åström,et al.  Adaptive Control , 1989, Embedded Digital Control with Microcontrollers.

[7]  Standard Ashrae.  Thermal Environmental Conditions for Human Occupancy , 1992 .

[8]  P. Bickel.  Efficient and Adaptive Estimation for Semiparametric Models , 1993 .

[9]  K. Do,et al.  Efficient and Adaptive Estimation for Semiparametric Models. , 1994 .

[10]  J. Vargas,et al.  Simulation in transient regime of a heat pump with closed-loop and on-off control , 1995 .

[11]  B. Pasik-Duncan,et al.  Adaptive Control , 1996, IEEE Control Systems.

[12]  John Lygeros,et al.  Controllers for reachability specifications for hybrid systems , 1999, Autom..

[13]  Drury B. Crawley,et al.  EnergyPlus: Energy simulation program , 2000 .

[14]  J. Lygeros,et al.  A game theoretic approach to controller design for hybrid systems , 2000, Proceedings of the IEEE.

[15]  Luigi Chisci,et al.  Systems with persistent disturbances: predictive control with restricted constraints , 2001, Autom..

[16]  Daniel E. Fisher,et al.  EnergyPlus: creating a new-generation building energy simulation program , 2001 .

[17]  Jianxin Xu,et al.  Linear and Nonlinear Iterative Learning Control , 2003 .

[18]  David Q. Mayne,et al.  Robust model predictive control using tubes , 2004, Autom..

[19]  Gregor P. Henze,et al.  Evaluation of optimal control for active and passive building thermal storage , 2004 .

[20]  Mato Baotic,et al.  Multi-Parametric Toolbox (MPT) , 2004, HSCC.

[21]  Andrew R. Teel,et al.  Examples when nonlinear model predictive control is nonrobust , 2004, Autom..

[22]  Alberto Bemporad,et al.  Dynamic programming for constrained optimal control of discrete-time linear hybrid systems , 2005, Autom..

[23]  Michael A. Saunders,et al.  SNOPT: An SQP Algorithm for Large-Scale Constrained Optimization , 2005, SIAM Rev..

[24]  Stephen P. Boyd,et al.  Enhancing Sparsity by Reweighted ℓ1 Minimization , 2007, 0711.1612.

[25]  David E. Culler,et al.  Design and implementation of a high-fidelity AC metering network , 2009, 2009 International Conference on Information Processing in Sensor Networks.

[26]  Russ Tedrake,et al.  LQR-trees: Feedback motion planning on sparse randomized trees , 2009, Robotics: Science and Systems.

[27]  MengChu Zhou,et al.  Technologies toward thermal comfort-based and energy-efficient HVAC systems: A review , 2009, 2009 IEEE International Conference on Systems, Man and Cybernetics.

[28]  Claire J. Tomlin,et al.  Statistics for sparse, high-dimensional, and nonparametric system identification , 2009, 2009 IEEE International Conference on Robotics and Automation.

[29]  David M. Auslander,et al.  Low-Frequency Pulse Width Modulation Design for HVAC Compressors , 2009 .

[30]  Jeff W. Thornton,et al.  TRNSYS - FEATURES AND FUNCTIONALITITY FOR BUILDING SIMULATION 2009 CONFERENCE , 2009 .

[31]  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.

[32]  David E. Culler,et al.  sMAP: a simple measurement and actuation profile for physical information , 2010, SenSys '10.

[33]  Chenda Liao,et al.  An integrated approach to occupancy modeling and estimation in commercial buildings , 2010, Proceedings of the 2010 American Control Conference.

[34]  Pieter Abbeel,et al.  Autonomous Helicopter Aerobatics through Apprenticeship Learning , 2010, Int. J. Robotics Res..

[35]  Sean P. Meyn,et al.  Building thermal model reduction via aggregation of states , 2010, Proceedings of the 2010 American Control Conference.

[36]  Xiaofan Jiang,et al.  Enabling green building applications , 2010, HotEmNets.

[37]  Eduardo F. Camacho,et al.  Robust tube-based MPC for tracking of constrained linear systems with additive disturbances , 2010 .

[38]  George J. Pappas,et al.  Receding-horizon supervisory control of green buildings , 2011, Proceedings of the 2011 American Control Conference.

[39]  Yanpei Chen,et al.  An information-centric energy infrastructure: The Berkeley view , 2011, Sustain. Comput. Informatics Syst..

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

[41]  P. Bickel,et al.  Regression on manifolds: Estimation of the exterior derivative , 2011, 1103.1457.

[42]  W. Marsden.  I and J , 2012 .

[43]  S. Shankar Sastry,et al.  Provably safe and robust learning-based model predictive control , 2011, Autom..