Distributed air conditioning control in commercial buildings based on a physical-statistical approach

Abstract This paper presents a method to optimally control a commercial building air conditioning operation based on building thermal physics and human behavior. Control plans are developed for zone cooling and according to occupancy patterns at zone levels, and using a novel response model developed in this paper. The thermal response model is a statistical model, which is built on the basis of insights gained from physics-based model of zone thermal behavior. The control model attempts to optimize cooling system scheduling on the basis of occupancy patterns, price signal from utilities and human comfort and productivity. The optimization follows an on-demand routine, such that zone level air conditioning starts at a time epoch that is optimal according to zone thermal response and cooling requirements. We toss the term “pre-occupy” control. We also develop, what we call, “post-occupy” control, where the system shutdown follows thermal inertia and cooling requirements at the time that the zone is expected to become unoccupied. The thermal response modeling of a zone and the two control modes are considered major contributions of this article. EnergyPlus reference models are used to train our models and illustrative examples are presented.

[1]  Sehyun Park,et al.  Development of a self-adapting intelligent system for building energy saving and context-aware smart services , 2011, IEEE Transactions on Consumer Electronics.

[2]  Dimitrios Tzovaras,et al.  A context-aware method for building occupancy prediction , 2016 .

[3]  Donal Finn,et al.  Estimating Demand Response Potential in Building Clusters , 2015 .

[4]  Francesco Borrelli,et al.  Predictive Control for Energy Efficient Buildings with Thermal Storage: Modeling, Stimulation, and Experiments , 2012, IEEE Control Systems.

[5]  Fiorella Lauro,et al.  Model predictive control for building active demand response systems , 2015 .

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

[7]  Roger W. Haines,et al.  HVAC Systems Design Handbook , 1988 .

[8]  Harry J. Sauer,et al.  Principles of Heating, Ventilating and Air Conditioning , 1993 .

[9]  Siyu Wu,et al.  A physics-based linear parametric model of room temperature in office buildings , 2012 .

[10]  Mohsen A. Jafari,et al.  Operational planning for multi-building portfolio in an uncertain energy market , 2015 .

[11]  Francesco Borrelli,et al.  Building temperature distributed control via explicit MPC and “Trim and Respond” methods , 2013, 2013 European Control Conference (ECC).

[12]  Ram Rajagopal,et al.  Short Term Electricity Load Forecasting on Varying Levels of Aggregation , 2014, ArXiv.

[13]  Yoseba K. Penya,et al.  Optimal combined short-term building load forecasting , 2011, 2011 IEEE PES Innovative Smart Grid Technologies.

[14]  Peter Cappers,et al.  Future Opportunities and Challenges with Using Demand Response as a Resource in Distribution System Operation and Planning Activities , 2015 .

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

[16]  Luca Ferrarini,et al.  Temperature Control of a Commercial Building With Model Predictive Control Techniques , 2015, IEEE Transactions on Industrial Electronics.

[17]  Ross Montgomery,et al.  Fundamentals of HVAC control systems , 2011 .

[18]  Hao Huang,et al.  A hybrid model predictive control scheme for energy and cost savings in commercial buildings: Simulation and experiment , 2015, 2015 American Control Conference (ACC).

[19]  Alberto Cerpa,et al.  Optimal HVAC building control with occupancy prediction , 2014, BuildSys@SenSys.

[20]  Yan Lu,et al.  A Hybrid Physics-Based and Data Driven Approach to Optimal Control of Building Cooling/Heating Systems , 2016, IEEE Transactions on Automation Science and Engineering.

[21]  Bing Dong,et al.  A real-time model predictive control for building heating and cooling systems based on the occupancy behavior pattern detection and local weather forecasting , 2013, Building Simulation.

[22]  Joaquim Melendez,et al.  Short-term load forecasting in a non-residential building contrasting models and attributes , 2015 .

[23]  Haorong Li,et al.  Analysis of HVAC system oversizing in commercial buildings through field measurements , 2014 .

[24]  T. Mikuls,et al.  Environmental Exposures and Rheumatoid Arthritis Risk , 2011, Current rheumatology reports.

[25]  Deborah Snoonian,et al.  Control systems: smart buildings , 2003 .

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

[27]  William J. Fisk,et al.  Effect of temperature on task performance in officeenvironment , 2006 .

[28]  Evangelos Vrettos,et al.  Predictive Control of buildings for Demand Response with dynamic day-ahead and real-time prices , 2013, 2013 European Control Conference (ECC).

[29]  Ning Lu,et al.  An Evaluation of the HVAC Load Potential for Providing Load Balancing Service , 2012, IEEE Transactions on Smart Grid.

[30]  Brandon Hencey,et al.  Model Predictive HVAC Control with Online Occupancy Model , 2014, ArXiv.

[31]  Manuel Domínguez,et al.  Machine learning methods to forecast temperature in buildings , 2013, Expert Syst. Appl..

[32]  Frank P. Incropera,et al.  Fundamentals of Heat and Mass Transfer , 1981 .

[33]  Alberto Bemporad,et al.  Hybrid model predictive control for optimal energy management of a smart house , 2015, 2015 IEEE 15th International Conference on Environment and Electrical Engineering (EEEIC).

[34]  Prabir Barooah,et al.  Occupancy-based zone-climate control for energy-efficient buildings: Complexity vs. performance , 2013 .