Yuxiang Chen Andreas K. Athienitis Ph.D. Candidate Professor and Director

1. Predictive Operation of ActiveBuilding-Integrated Thermal Energy Storage (BITES)Using Frequency Domain Models Yuxiang Chen Andreas K. Athienitis Ph.D. Candidate Professor and Director *Department of Building, Civil and Environmental Engineering Concordia University, Montreal, Canada *NSERC Smart Net-zero Energy Buildings Strategic Research Network

2. Outline • Introduction • Active BITES systems • Frequency domain modeling • Dynamic responses • Integrating design and operation • Procedure • MPC design (off-line) • Methodology • Verification • Conclusions

3. Introduction • Active Building-Integrated Thermal Energy Storage (BITES) systems • Building fabric • TES and direct space conditioning • Active charge; active/passive discharge • Open- or close-loop Ventilated (air-based) system Hydronic system

4. Introduction • Frequency domain modeling • Linear, time invariant system • Steady periodic excitations • Temperature, heat flux • Represented by complex discrete Fourier series (DFS) • Frequency domain transfer functions • No need for spatial discretization • Insights for design optimization • Predictive control

5. Introduction Complex DFS representations • Frequency domain modeling Complex frequency domain transfer functions Transformed cross section for 1-D model Heat flow division

6. Introduction • Frequency domain modeling • Final outputs • Important frequency • 1 cycle per day (1 CPD) Oscillatory response by transfer functions Steady-state response Amplitude spectrum for ambient temperature for two weeks • Frequency domain response Excitations “” (temp. , heat flux ) Summation of all harmonics* Ambient Temperature (C) Harmonic number • Time domain response = Real()

7. Introduction • Dynamic Responses • Analysis of relevant transfer functions • Phase angle (i.e. time lag/delay) • Magnitude or self-admittance 1 Cycle per day responses: Surface temperature to surface heat flux Surface heat flux to source layer heat input

8. Integrating Design and Control • Proper operation improves energy performance • Design for operation • Bounding performance

9. MPC design • Two prerequisites: • Weather forecast • Space conditioning load estimation Source level responses External excitations Output of predictive control

10. MPC design • Modeled zone & its energy flows • A single zone with large equator-facing window • 0.3m open-supply BITES slab • 3 ACH air flow rate

11. MPC design • Room air temperature set-profile • Pivots • Temperature (y-axis) • A function of sol-air temperature • Neutralization • Buffer excessive heat • Pre-conditioning • Favorable weather conditions • Off-peak period • Time delay (x-axis) • Phase angles from transfer functions Defined by phase angle Typical winter conditions

12. MPC design • Space conditioning load estimation • Explicit finite difference • or inverse model (online self-tuning) • DFS for room air temperature • Space conditioning load = required thermal output Required thermal output Room temp. set-profile

13. MPC design Simulated and required thermal output • Preliminary results • Required heat input to achieve room set-profile • Large input fluctuations and waste of energy Simulated and set room temp. Required heat input

14. MPC design • Smoothing input (preliminary) • Average the adjacent heat input • Verification • Explicit finite difference model • Provide desired room temperature • Off-peak charging • Relative flat demands • Enable utilization of ambient renewable Smoothed heat input Final room temp.

15. Conclusions • Active BITES system • Thermal energy storage • Primary space conditioning device • Frequency domain modeling • Integrates design and operation • Directly connect outputs to inputs (phase angle and magnitude) • Predictive operation • Provides desired room temperature • Off-peak charging • Enable utilization of ambient renewable energy • Relatively flat demands