A University Building Test Case for Occupancy-Based Building Automation

Heating, ventilation and air-conditioning (HVAC) units in buildings form a system-of-subsystems entity that must be accurately integrated and controlled by the building automation system to ensure the occupants' comfort with reduced energy consumption. As control of HVACs involves a standardized hierarchy of high-level set-point control and low-level Proportional-Integral-Derivative (PID) controls, there is a need for overcoming current control fragmentation without disrupting the standard hierarchy. In this work, we propose a model-based approach to achieve these goals. In particular: The set-point control is based on a predictive HVAC thermal model, and aims at optimizing thermal comfort with reduced energy consumption; the standard low-level PID controllers are auto-tuned based on simulations of the HVAC thermal model, and aims at good tracking of the set points. One benefit of such control structure is that the PID dynamics are included in the predictive optimization: in this way, we are able to account for tracking transients, which are particularly useful if the HVAC is switched on and off depending on occupancy patterns. Experimental and simulation validation via a three-room test case at the Delft University of Technology shows the potential for a high degree of comfort while also reducing energy consumption.

[1]  Gregor P. Henze,et al.  Advances in Near-Optimal Control of Passive Building Thermal Storage , 2010 .

[2]  Ian Paul Knight,et al.  Assessing electrical energy use in HVAC systems , 2012 .

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

[4]  Simone Baldi,et al.  An integrated control-oriented modelling for HVAC performance benchmarking , 2016 .

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

[6]  Fiorella Lauro,et al.  An adaptive distributed predictive control strategy for temperature regulation in a multizone office building , 2014, 2014 IEEE International Workshop on Intelligent Energy Systems (IWIES).

[7]  Aaron P. Wemhoff,et al.  Calibration of HVAC equipment PID coefficients for energy conservation , 2012 .

[8]  Tomas Núñez,et al.  Modelling of an adsorption chiller for dynamic system simulation , 2009 .

[9]  Michael Sebek,et al.  Economical nonlinear model predictive control for building climate control , 2014, 2014 American Control Conference.

[10]  P. Fanger Calculation of Thermal Comfort, Introduction of a Basic Comfort Equation , 1967 .

[11]  M. Barak,et al.  Energy saving in agricultural buildings through fan motor control by variable frequency drives , 2008 .

[12]  Martin Kozek,et al.  Implementation of cooperative Fuzzy model predictive control for an energy-efficient office building , 2018 .

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

[14]  Elias B. Kosmatopoulos,et al.  Automating occupant-building interaction via smart zoning of thermostatic loads: A switched self-tuning approach , 2018, Applied Energy.

[15]  John Zhai,et al.  Comparative Energy Analysis of VRF and VAV Systems Under Cooling Mode , 2009 .

[16]  Iakovos Michailidis,et al.  Model-based and model-free “plug-and-play” building energy efficient control , 2015 .

[17]  Gail Brager,et al.  Climate, Comfort & Natural Ventilation: A new adaptive comfort standard for ASHRAE Standard 55 , 2001 .

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

[19]  Alberto L. Sangiovanni-Vincentelli,et al.  Total and Peak Energy Consumption Minimization of Building HVAC Systems Using Model Predictive Control , 2012, IEEE Design & Test of Computers.

[20]  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.

[21]  Iakovos Michailidis,et al.  Proactive control for solar energy exploitation: A german high-inertia building case study , 2015 .

[22]  Xinhua Xu,et al.  A supervisory control strategy for building cooling water systems for practical and real time applications , 2008 .

[23]  Iakovos Michailidis,et al.  Joint energy demand and thermal comfort optimization in photovoltaic-equipped interconnected microgrids , 2015 .

[24]  Manfred Morari,et al.  Model Predictive Climate Control of a Swiss Office Building: Implementation, Results, and Cost–Benefit Analysis , 2016, IEEE Transactions on Control Systems Technology.

[25]  F. H. Rohles,et al.  Thermal Sensations of Sedentary Man in Moderate Temperatures , 1971, Human factors.

[26]  Farrokh Janabi-Sharifi,et al.  Theory and applications of HVAC control systems – A review of model predictive control (MPC) , 2014 .

[27]  Nabil Nassif,et al.  A cost‐effective operating strategy to reduce energy consumption in a HVAC system , 2008 .

[28]  Ondrej Holub,et al.  Adaptive quantile estimation in performance monitoring of building automation systems , 2016, 2016 European Control Conference (ECC).

[29]  E.B. Kosmatopoulos,et al.  A model-assisted adaptive controller fine-tuning methodology for efficient energy use in buildings , 2011, 2011 19th Mediterranean Conference on Control & Automation (MED).

[30]  Elias B. Kosmatopoulos,et al.  Adaptive-fine tuning of building energy management systems using co-simulation , 2012, 2012 IEEE International Conference on Control Applications.

[31]  S. Baldi,et al.  Dual estimation: Constructing building energy models from data sampled at low rate , 2016 .

[32]  Luis Pérez-Lombard,et al.  A review on buildings energy consumption information , 2008 .

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

[34]  Bing Dong,et al.  Occupancy behavior based model predictive control for building indoor climate—A critical review , 2016 .