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Modeling and Predictive Control Strategies in Buildings with Mixed-Mode Cooling Jianjun Hu, Panagiota Karava School of Civil Engineering (Architectural.

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Presentation on theme: "Modeling and Predictive Control Strategies in Buildings with Mixed-Mode Cooling Jianjun Hu, Panagiota Karava School of Civil Engineering (Architectural."— Presentation transcript:

1 Modeling and Predictive Control Strategies in Buildings with Mixed-Mode Cooling Jianjun Hu, Panagiota Karava 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 or thermal buoyancy forces, and mechanical systems;  “Intelligent” controls to optimize mode switching  minimize building energy use and maintain occupant thermal comfort. 2

3 Background - Mixed-Mode Strategies 3 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; (Karava et al., 2012) - When should we open the windows ? - For how long? - Can we use MPC? Institutional building located in Montreal Mixed-mode cooling concept

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.,

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 5

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

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

8  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 8 MPC: Dynamic Model (Thermal & Airflow Network) Thermal model

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

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

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

12 MPC: Control Variable, Cost Function, and Constraints  Control variable: operation schedule  Cost function: Min: where: E is the energy consumption; IO t is vector of binary (open/close) decisions for the motorized envelope openings  Constraints:  Operative temperature within comfort range ( °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 ˚C, during unoccupied hours is °C);  Dew point temperature should be lower than 13.5 °C (ASHRAE 90.1);  Wind speed should be lower than 7.5 m/s. 12

13 MPC: Optimization (PSO)  “Offline” deterministic MPC: Assume future predictions are exact  Planning horizon: 20: :00, decide operation status during each hour.  find optimal sequence from 2 24 options; 13 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) 14

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: T amb ∈ [15 ℃, 25 ℃ ], T dew ≤ 13.5 ℃, W speed < 7.5 m/s  MPC 15

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); 16

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

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

19 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. 20

21 Acknowledgement 21  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 DEEE  In kind support is provided from Kawneer/Alcoa, FFI Inc., and Automated Logic Corporation

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