1. PREDICTIVE PRE-COOLING CONTROL FOR LOW LIFT RADIANT COOLING USING BUILDING THERMAL MASS Nick Gayeski, PhD candidate in Building Technology August 2010, Dissertation Defense

2. Topics 1.Thesis 2.Motivation 3.Low lift cooling systems (LLCS) 3.1Low lift chiller performance 3.2Zone temperature model identification 3.3Predictive pre-cooling control optimization 4.Experimental assessment of LLCS 5.Future research 6.Summary of original contributions

3. 1. Thesis Predictive pre-cooling control for low lift radiant cooling using building thermal mass can lead to significant sensible cooling energy savings.  What is a low-lift cooling system (LLCS)?  How is it implemented using building thermal mass?  How is predictive pre-cooling control achieved?  How significant are the energy savings in a real installation?

4. 2. Motivation: energy and climate Addressing energy, climate and development challenges  Buildings are responsible for 40% of energy and 70% of electricity consumption in the US 1  Low cost carbon emission reduction potential 2  Most rapidly developing cities in cooling-dominated climates 3  Increasing demand for thermal comfort 4 1.USDOE 2006. Building Energy Databook 2.IPCC 2007. Fourth Assessment Report 3.Sivak 2009. Energy Policy 37 4.McNeil and Letschert 2007. ECEEE 2007 Summer Study

5. 2. Motivation: better buildings Leveraging integrated design, advanced HVAC, and building monitoring and automation  Using integrated design to enhance active mechanical system efficiency through thermal storage  Applying HVAC technologies in a coordinated manner for synergistic energy savings  Growth in building monitoring and automation creates opportunities for intelligent control

6. Topics 1.Thesis 2.Motivation 3.Low lift cooling systems (LLCS) 3.1Low lift chiller performance 3.2Zone temperature model identification 3.3Predictive pre-cooling control optimization 4.Experimental assessment of LLCS 5.Future research 6.Summary of original contributions

7. 3. Low lift cooling systems (LLCS) Low lift cooling system: a cooling strategy that leverages the following technologies to reduce cooling energy:  Variable speed compressors  Hydronic distribution with variable flow  Radiant cooling  Thermal energy storage (TES)  Pre-cooling control  Dedicated outdoor air systems (DOAS)

8. 3.Low lift cooling systems (LLCS) Low lift cooling systems save cooling energy by  Operating chillers more efficiently at low lift more of the time through predictive pre-cooling control  Night time operation  Spreading out the cooling load to operate at part capacity  Radiant cooling  Reducing energy for transporting cooling to a space  Providing ventilation and dehumidification efficiently

9. Prior research on component strategies  Variable speed compressors, pumps and fans (Hiller, Glicksman, Purdue, Armstrong, UIUC, NIST)  Radiant cooling (Olesen, Adlam, Simmonds, Scheatzle, Feustel, Stetiu)  Thermal energy storage (TES) (Braun, Henze, Norford, Rabl, Koschenz, Lehmann, Roth, Kintner-Meyer, Emery, Armstrong)  Pre-cooling control (Braun, Henze, Armstrong)  Dedicated outdoor air systems (DOAS) (Mumma, Dieckmann, Larranga)

10. Prior research on LLCS shows significant cooling energy savings potential Simulated energy savings: 12 building types in 16 cities relative to a DOE benchmark HVAC system Total annual cooling energy savings  37 to 84% in standard buildings, average 60-70%  -9 to 70% in high performance buildings, average 40-60% (Katipamula et al 2010, PNNL-19114)

11. LLCS cooling energy savings in Atlanta Simulated total annual cooling energy savings:  in a medium size office building  in Atlanta  over a full year  with respect to a variable air volume (VAV) system served by a variable-speed chiller with an economizer and ideal storage  similar to a split-system air conditioner (SSAC) used as an experimental base line, with some differences 28 % annual cooling energy savings (Katipamula et al 2010, PNNL-19114)

12. 0 20 40 T - Temperature (°C) 60 11.21.4 S - Entropy (kJ/kg-K) 1.61.8 100 200 300 400500600 700 psia Radiant cooling and variable speed pump Predictive pre-cooling of thermal storage and variable speed fans Low-lift refers to a lower temperature difference between evaporation and condensation Variable speed compressor Low lift vapor compression cycle requires less work

13. Predict 24-hour optimal chiller control schedule Variable capacity chiller Load forecasts Building data Identify building temperature response models Charge active TES Direct zone cooling Pre-cool concrete-core thermal energy storage Pre-cool passive TES LLCS operates a chiller at low lift more of the time Occupied zone

14. LLCS research overview Develop the pre-cooling control and experimentally test an LLCS Optimize control of a chiller over a 24-hour look-ahead schedule to minimize daily chiller energy consumption by operating at low lift conditions while maintaining thermal comfort  Informed by data-driven zone temperature response models and forecasts of climate conditions and loads  Informed by a chiller performance model that predicts chiller power and cooling rate at future conditions for a chosen control

15. Topics 1.Thesis 2.Motivation 3.Low lift cooling systems (LLCS) 3.1Low lift chiller performance 3.2Zone temperature model identification 3.3Predictive pre-cooling control optimization 4.Experimental assessment of LLCS 5.Future research 6.Summary of original contributions

16. 3.1Low lift chiller performance Predictive pre-cooling control requires a chiller model to predict chiller power consumption, cooling capacity and COP at low-lift To identify a chiller model under low lift conditions:  Built a heat pump test stand  Experimentally tested the performance of a heat pump at low pressure ratios, which was later converted to a chiller for LLCS  Identified an empirical model of chiller performance useful for predictive control

17. Outdoor temp (C) Indoor temp (C) Compressor speed (Hz) Fan speed (RPM) 15 22.5 30 37.5 45 14 24 34 19 30 60 95 300 450 600 750 900 1050 1200 Measured heat pump performance at many steady state conditions Tested 131 combinations of the following conditions To identify a model of chiller power, cooling rate, and COP as a function of all 4 variables

18. EER 34 17 51 Typical operation COP ~ 3.5 Low lift operation COP ~ 5-10 Test results show expected higher COPs at low lift conditions

19. 4-variable cubic polynomial models Empirical models accurately represent chiller cooling capacity, power and COP

20. Night time operation Radiant cooling T e = Evaporating temperature, T o = Outdoor air temperature Load spreading LLCS controls enable the chiller to operate at low lift conditions and higher COPs

21. Topics 1.Thesis 2.Motivation 3.Low lift cooling systems (LLCS) 3.1Low lift chiller performance 3.2Zone temperature model identification 3.3Predictive pre-cooling control optimization 4.Experimental assessment of LLCS 5.Future research 6.Summary of original contributions

22. 3.2 Zone temperature model identification LLCS control requires zone temperature response models to predict temperatures and chiller performance  Develop data-driven models from which to predict  Zone operative temperature (OPT)  The temperature underneath the concrete slab (UST)  Return water temperature (RWT) and ultimately chiller evaporating temperature (EVT) from which chiller power and cooling rate can be calculated  Assume ideal forecasts of outdoor climate and internal loads  Implement data-driven modeling on a real test chamber

23. OPT = operative temperature OAT = outdoor air temperature AAT = adjacent zone air temperature QI=heat rate from internal loads QC = cooling rate from mechanical system a,b,c,d,e = weights for time series of each variable  (Inverse) comprehensive room transfer function (CRTF) [Seem 1987]  Steady state heat transfer physics constrain CRTF coefficients Existing transfer function modeling methods can be applied to predict zone temperature

24.  Chiller power and cooling rate depend on  evaporating temperature, which is coupled to  return water temperature, and thus to the  state of thermal energy storage, in this case a radiant concrete floor  Predict concrete floor under-slab temperature (UST) using a transfer function model  Predict return water temperature (RWT) using a low-order transfer function model in UST and cooling rate QC  Superheat relates RWT to evaporating temperature (EVT) Evaporating temperature is predicted from intermediate temperature response models

25. Temperature sensors: OPT, OAT, AAT, UST, RWT Power to internal loads: QI Radiant concrete floor cooling rate:QC Data-driven models identified for a test chamber with a radiant concrete floor

26. Typical temperature measurements at each location below:  Outdoor air temperature  Surface temperature  Zone air temperature  Concrete floor temperature In a real building:  Outdoor air temperature  Zone globe temperature  Zone air temperature  Concrete slab temperature x x X X X X X X X X X x x x x X XX South Wall Floor/Ceiling East Wall West Wall North Wall Data-driven models can be identified from a small set of temperature measurements 17 ft 8 ft 12 ft

27. Sample training temperature data Sample training thermal load data Models trained using a few days of data

28. Sample training temperature data

29. Sample training thermal load data

30. Operative temperature (OPT)Under-slab temperature (UST) Return water temperature (RWT) Root mean square error (RMSE) for training data OPT RMSE = 0.03 K UST RMSE = 0.04 K RWT RMSE = 0.68 K Transfer function models accurately predict training data temperatures

31. Models validated based on accuracy of predicting different data 24-hours-ahead Sample validation temperature data Sample validation thermal load data

32. Operative temperature (OPT)Under-slab temperature (UST) Return water temperature (RWT) Root mean square error (RMSE) for 24 hour ahead prediction of validation data OPT RMSE = 0.08 K UST RMSE = 0.15 K RWT RMSE = 0.84 K Transfer function models accurately predict zone temperatures 24-hours-ahead

33. Topics 1.Thesis 2.Motivation 3.Low lift cooling systems (LLCS) 3.1Low lift chiller performance 3.2Zone temperature model identification 3.3Predictive pre-cooling control optimization 4.Experimental assessment of LLCS 5.Future research 6.Summary of original contributions

34. 3.3 Pre-cooling control optimization Optimize chiller operation over 24 hours to minimize energy consumption and maintain thermal comfort  Employ a direct pattern search 1 to minimize the objective function by selecting an optimal schedule of 24 compressor speeds 2, one for each hour  Use chiller model to calculate cooling rate and power consumption  Use temperature response models to predict zone temperatures to ensure comfort is maintained 1.See Lewis et al 1999, SIAM J. of Optimization or MATLAB Optimization Toolbox 2.Given forecasts of OAT, optimal condenser fan speeds are determined by the choice of compressor speed

35. r t = electric rate at time t, or one for energy optimization P t = system power consumption as a function of past compressor speeds and exogenous variables = weight for operative temperature penalty PENOPT t = operative temperature penalty when OPT exceeds ASHRAE 55 comfort conditions PENEVT t =evaporative temperature penalty for temperatures below freezing =Vector of 24 compressor speeds, one for each hour of the 24 hours ahead Optimization minimizes energy, maintains comfort, and avoids freezing the chiller

36. Pattern search initial guess at current hour Pattern search algorithm determines optimal compressor speed schedule for the next 24 hours Operate chiller for one hour at optimal state 24-hour-ahead forecasts of outdoor air temperature, adjacent zone temperature, and internal loads (OAT, AAT, QI) Perform optimization at every hour with current building data and new forecasts

37. Predictive pre-cooling control maintains comfort and reduces energy consumption

39. Topics 1.Thesis 2.Motivation 3.Low lift cooling systems (LLCS) 3.1Low lift chiller performance 3.2Zone temperature model identification 3.3Predictive pre-cooling control optimization 4.Experimental assessment of LLCS 5.Future research 6.Summary of original contributions

40. 4. Experimental assessment of LLCS Prior research shows dramatic savings from LLCS, but  Based entirely on simulation  Assumes idealized thermal storage, not a real concrete floor  Chiller power and cooling rate are not coupled to thermal storage, as it is for a concrete radiant floor How real are these savings? What practical technical obstacles exist?

41.  Built a chiller by modifying the heat pump outdoor unit  Built an LLCS test system with a radiant concrete floor  Implemented the pre-cooling optimization control  Tested LLCS under a typical summer week in Atlanta (and next Phoenix) subject to internal loads  Compared the LLCS performance to a baseline system - a high efficiency (SEER ~ 16) variable capacity split-system air conditioner (SSAC) Built and tested a near full-scale LLCS

42. CONDENSER ELECTRONIC EXPANSION VALVE COMPRESSOR BPHX TO RADIANT FLOOR FROM RADIANT FLOOR FROM INDOOR UNIT (CLOSED) TO INDOOR UNIT (CLOSED) TEST CHAMBERCLIMATE CHAMBER IDENTICAL FOR LLCS AND BASE CASE SSAC LLCS and SSAC use the same outdoor unit

43. TEST CHAMBERCLIMATE CHAMBER BPHX WATER PUMP TO CHILLER FROM CHILLER FILTER EXPANSION TANK 12’ 17’ RADIANT MANIFOLD RADIANT FLOOR LLCS provides chilled water to a radiant concrete floor (thermal energy storage)

44. Chiller/heat pumpRadiant concrete floor

45. LLCS chiller Brazed plate heat exchanger SSAC (SEER~16) Standard mini-split indoor unit

46. Atlanta typical summer week and standard efficiency loads  Based on typical meteorological year weather data  Assuming two occupants and ASHRAE 90.1 2004 loads Run LLCS for one week *(after a stabilization period) Run split-system air conditioner (SSAC) for one week*  Compare sensible cooling only  Mixing fan treated as an internal load Repeat for Phoenix typical summer week, high efficiency loads – to be completed after climate chamber HVAC repairs Tested LLCS for a typical summer week in Atlanta subject to standard internal loads

47. Atlanta test Phoenix test Outdoor climate conditionsInternal loads

48. LLCS energy consumption (Wh) SSAC (SEER~16) energy consumption (Wh) Measured 10,982 Measured14,64525% Deducting latent cooling 1 14,05322% 2 1 Latent cooling is deducted by measuring condensate water from the SSAC, calculating the total enthalpy associated with its condensation, and dividing it by the average SSAC COP over the week. 2 Assuming no latent cooling by the LLCS LLCS ENERGY SAVINGS relative to SSAC in Atlanta subject to standard loads Similar to simulated total annual cooling energy savings, 28 percent, by (Katipamula et al 2010)

49.  Standard efficiency loads result in large air temperature rise  OPT rises by as much as a 6 Celsius over 10 occupied hours  Below the ASHRAE 55 limit of 3.3 C/4 hr, but may still be a comfort issue Occupied hours Temperature (C) 67 77 °F LLCS THERMAL PERFORMANCE relative to SSAC in Atlanta subject to standard loads

50.  Radiant floor capacity should be increased by decreasing pipe spacing, permitting higher evaporating temperatures  Zone thermal load should be better matched to both the chiller capacity and the radiant floor capacity  Improve the design and control of the BPHX chiller COP, as-built COPs are lower than COPs measured on the test stand  Add insulation below the concrete floor to increase thermal storage efficiency and create a more representative LLCS  Improvements likely to yield better LLCS performance Limitations of the existing LLCS test chamber

51. SSAC Thermostatic control SSAC Predictive control Concrete-floor predictive control Radiant panel predictive control Weekly average COP 4.324.974.807.46 Cooling delivered (Wh) -47,940-39,920-53,200-39,420 Simulated energy (Wh) 11,1108,03811,0725,285 Measured energy (Wh) 14,053n/a10,982n/a Error in simulation 20.9%n/a-0.8%n/a Savings relative to simulated base case base27.6%base52.3%  Simulated the performance of predictive pre-cooling control on the SSAC and with radiant ceiling panels  Significant savings potential for predictive control on other systems Predictive pre-cooling control can be applied to other systems to achieve low lift

52. Topics 1.Thesis 2.Motivation 3.Low lift cooling systems (LLCS) 3.1Low lift chiller performance 3.2Zone temperature model identification 3.3Predictive pre-cooling control optimization 4.Experimental assessment of LLCS 5.Future research 6.Summary of original contributions

53. 5. Future LLCS research  Refine LLCS methods  Determine evaporating temperature without measuring under-slab concrete temperature  Refine temperature response model identification methods, e.g. real-time model identification with updated training data  Simplify and improve the pre-cooling optimization and control  Combine concrete-core with direct cooling (e.g. chilled beams) and adapt the predictive control algorithm  Perform testing subject to actual outdoor conditions at MASDAR  Install and test LLCS in a real building (medium size office)  Pre-cooling control for other LLCS configurations

54. Future of LLCS in real buildings  Concrete-core and radiant systems gaining market share, and familiarity among architects and engineers (primarily in Europe)  Automation systems are becoming more prevalent/sophisticated  Capital cost savings for LLCS in medium office buildings, -0.58 $/sqft incremental cost relative to $7.91/sqft base cost 1  Adapt components of LLCS to existing buildings and different new and existing building types, e.g.  Direct cooling combined with active or passive thermal storage  Radiant concrete-core using a “topping slab” for existing buildings  Adapt low-lift predictive control to existing concrete-core buildings 1. Katipamula et al 2010, PNNL-19114

55. 6. Summary of original contributions  Detailed data on the performance of an inverter-driven rolling- piston compressor heat pump over a wide range of conditions including low lift, over a capacity range of 5:1  Methodology for integrating chiller models and zone temperature response models into a pre-cooling optimization algorithm for controlling LLCS with real building thermal mass  Experimental validation of significant LLCS sensible cooling energy savings relative to a state-of-the-art split system air conditioner (SEER 16), 25 percent in Atlanta with standard efficiency internal loads

56. Thank you! Professors Leslie Norford, Leon Glicksman and Peter Armstrong Massachusetts Institute of Technology Masdar Institute of Science and Technology Stephen Samouhos and Siân Kleindienst Rob Darnell, Tom Pittsley, the BAC and the solar decathletes Professor Marilyne Andersen and all the Daylighters Srinivas Katipamula and the Pacific Northwest National Laboratory Daniel Nikovski, Ankur Jain, Chris Laughman, Mitsubishi Electric Research Laboratory Volker Ruhle and Uponor Amanda Graham, Beth Conlin, and the Martin Family Society of Fellows Peter Cooper, Walt Henry and MIT Facilities Evan Samouhos and EVCO mechanical Kathleen Ross, Ali Mulcahy, Jim Harrington, Renee Caso All my colleagues in Building Technology My friends and colleagues at MIT, especially Yanni, Saeed, Zach, and Brandon David, Andrea, Yanni, Steve, Bruno for their late hour feedback Mom, Dad, Emily, Jeanie, Pat, Sophia Celina, and our dogs Nicholas Gayeski, gayeski@mit.edu