Modeling and Predictive Control Strategies in Buildings with Mixed-Mode Cooling

1. Modeling and Predictive Control Strategies in Buildings with Mixed-Mode Cooling Jianjun Hu, PanagiotaKarava School of Civil Engineering (Architectural Engineering Group) Purdue University

2. Background - Mixed-Mode Cooling • Hybrid approach for space conditioning; • Combination of natural ventilation, driven by wind orthermal buoyancy forces, and mechanical systems; • “Intelligent” controls to optimize mode switching • minimize building energy use and maintain occupant thermal comfort.

3. Background - Mixed-Mode Strategies When outdoor conditions are appropriate: • Corridor inlet grilles and atria connecting grilles open; • Atrium mechanical air supply flow rate reduced to minimum value, corridor air supply units close; • Atrium exhaust vent open; Institutional building located in Montreal - When should we open the windows ? - For how long? - Can we use MPC? Mixed-mode cooling concept (Karava et al., 2012)

4. Background – MPC for Mixed-Mode Buildings • Modeling Complexity • Pump and fan speed, opening position (inverse model identified from measurement data)- Spindler, 2004 • Window opening schedule (rule extraction for real time application) - May-Ostendorp, 2011 • Shading percentage, air change rate (look-up table for a single zone) – Coffey, 2011 • Blind and window opening schedule (bi-linear state space model for a single zone) – Lehmann et al., 2012

5. Objectives • Develop model-predictive control strategies for multi-zone buildings with mixed-mode cooling, high solar gains, and exposed thermal mass. • Switching modes of operation for space cooling (window schedule, fan assist, night cooling, HVAC) • Coordinated shading control

6. MPC: Problem Formulation Offline MPC (deterministic); baseline simulation study for a mixed-mode building Linearized prediction models (state-space) Thermal Dynamic Model: Nonlinear Discrete Control Variables: Open/Close (1/0) Algorithms for discrete optimization On-line MPC (implementation, identification, uncertainty) Operable vents

7. MPC: Dynamic Model (Thermal & Airflow Network) • Building section (9 thermal zones)

8. MPC: Dynamic Model (Thermal & Airflow Network) • Heat balance for atrium air node • is the air exchange flow rate between zones (obtained from the airflow network model) : • pressure difference ΔP: • Solved by FDM method and Newton-Raphson Thermal model

9. MPC: Dynamic Model (State-Space) • State-space representation: is a nonlinear term, i.e.: heat transfer due to the air exchange. A, B, C, D: coefficient matrices X: state vector U: input vector Y:Output vector obtained from the airflow network model Linear time varying (LTV-SS)

10. MPC: Dynamic Model (State-Space) • States (X): X = [Ti , Tij , Tij,k]T • i – zone index • j – wall index • k – mass node index • Inputs (U): U = [Tout, Sij, Load]T • Tout – outside air temperature; • Sij – solar radiation on surfaces ij; • Load – heating/cooling load; • Outputs (Y):Y= [Ti , Tij , Tij,k]T • Zone air temperature; • Wall temperature; • …………

11. MPC: Dynamic Model (LTV-SS) • Find the matrices from the heat balance equations e.g. atrium zone air node:

12. MPC: Control Variable, Cost Function, and Constraints • Control variable:operation schedule • Cost function: • Min: • where: E is the energy consumption; IOt is vector of binary (open/close) decisions for the motorized envelope openings • Constraints: • Operative temperature within comfort range (23-27.6 °C, which corresponds to PPD of 10%) during occupancy hours; • Use minimal amount of energy: cooling/heating (set point during occupancy hours 8:00-18:00 is 21-23 ˚C, during unoccupied hours is 13-30 °C); • Dew point temperature should be lower than 13.5 °C (ASHRAE 90.1); • Wind speed should be lower than 7.5 m/s.

13. MPC: Optimization (PSO) • “Offline” deterministic MPC: Assume future predictions are exact • Planning horizon: 20:00 -- 19:00, decide operation status during each hour. • find optimal sequence from 224options; Wetter (2011)

14. MPC: Optimization (Progressive Refinement) • Multi-level optimization • Decide operation status for each two hours at night (20:00-5:00); • Use simple rules (based on off-line MPC)

15. Simulation Study • Assumptions: • Local controllers were ideal such that all feedback controllers follow set-points exactly; • Internal heat gains (occupancy, lighting) were not considered; • An idealized mechanical cooling system with a COP value of 3.5 was modeled. • TMW3 data (Montreal) • Cases: • Baseline: mechanical cooling with night set back • Heuristic: Tamb∈ [15℃, 25℃], Tdew≤ 13.5 ℃, Wspeed< 7.5 m/s • MPC

16. Results: Operation Schedule (Heuristic & MPC) • Hours during which vents are open are illustrated by cells with grey background • Heuristic strategy leads to higher risk of over-cooling during early morning (Day 1, Day 4, and Day 5);

17. Results: Energy Consumption & Operative Temperature (FDM & LTV-SS) 1.3 °C -3.0 °C Comfort Acceptability reduced from 80% to 60%

18. Results: MPC with PSO and Progressive Refinement (ProRe) • Similar energy consumption and operative temperature; • Much faster calculation with ProRe; 3 Days 3 Hours

19. Results: MPC with PSO and Progressive Refinement (ProRe) • Fine-tune rules in Progressive Refinement method for different climate (LA)

20. Conclusions • For the simulation period considered in the present study, mixed-mode cooling strategies (MPC and heuristic) effectively reduced building energy consumption. • The heuristic strategy can lead to a mean operative temperature deviation up to 0.7 °C, which may decrease the comfort acceptability from 80% to 60%. The predictive control strategy maintained the operative temperature in desired range. • The linear time-variant state-space model can predict the thermal dynamics of the mixed-mode building with good accuracy. • The progressive refinement optimization method can find similar optimal decisions with the PSO algorithm but with significantly lower computational effort.

21. Acknowledgement • This work is funded by the Purdue Research Foundation and the Energy Efficient Buildings Hub, an energy innovation HUB sponsored by the Department of Energy under Award Number DEEE0004261. • In kind support is provided from Kawneer/Alcoa, FFI Inc., and Automated Logic Corporation