1. 1 Thermal Comfort Control Based on Neural Network for HVAC Application Jian Liang and Ruxu Du Dept. of Automation and Computer-Aided Engineering The Chinese University of Hong Kong August 2005

2. 2 Outline  Introduction Introduction  Design of the thermal comfort Controller Design of the thermal comfort Controller  Models of the thermal comfort Controller Models of the thermal comfort Controller  Design of the Neural Networks controller Design of the Neural Networks controller  Simulation of the thermal comfort Controller Simulation of the thermal comfort Controller  Conclusion and further research Conclusion and further research

3. 3  The Heating, Ventilating and Air Conditioning ( HVAC) plays an important role in energy consumption  In China, it takes 15% of the building energy  In United States, it takes 44%  Development of air-conditioning control:  First generation: ON / OFF switch based on the sensation of the users  Second generation: ON / OFF control assisted by a temperature sensor  Third generation, digital control assisted by electronic thermostat, and humidity was also taken into consideration  Fourth generation: intelligent control (fuzzy control, adaptive control and etc.) Introduction

4. 4  Background  Most of the HVAC systems still adopt the temperature / humidity controllers  Thermal comfort control is necessary for higher comfort level  Thermal comfort indices  Standard Effective Temperature (SET) (Gagge, 1971)  Predicted Mean Vote (PMV) (Fanger, 1970): predict the mean thermal sensation vote on a standard scale for a large group of persons  PMV have been adopted by ISO 7730 standard, and ISO recommends to maintain PMV at 0 with a tolerance of 0.5 as the best thermal comfort  Thermal comfort concept: for long exposure to a constant thermal environment with a constant metabolic rate, a heat balance can be established for the human body and the bodily heat production is equal to its heat dissipation Introduction

5. 5  Background  Thermal comfort variables for PMV calculation  Four environmental-dependent variables: air temperature T a, relative air humidity RH, relative air velocity V air, mean radiant temperature T mrt  Two personal-dependent variables: activity level, clo-value (related to clothing worn by the occupants)  As a measure for the thermal comfort, one can use the seven point psycho-physical ASHRAE scale: Introduction

6. 6  Air conditioning controller  Most of the AC controllers are air temperature regulator (ATR) These regulators control the indoor temperature / humidity. Since comfort level is determined by six variables, thus these regulators can’t provide high comfort level  Comfort index regulators were proposed (CIR) (MacArthur, 1986; Scheatzle, 1991) These regulators are based on PMV / SET. The default reference input is 0 (neutral). Occupant serves as a supervisory controller by adjusting the reference value  User-adaptable comfort controller (UACC) (Federspiel and Asada, 1994 ) These controllers are based on a simplified PMV-like index proposed by Federspiel. It can tune the PMV model parameters by learning the specific occupant’s thermal sensation.  Some thermal comfort sensing systems were designed (J. Kang and S. Park, 2000) Introduction

7. 7  Our objective: design an intelligent thermal comfort controller based on neural networks for HVAC application  High comfort level Learn the comfort zone from the user’s preference, and guarantee the high comfort level and good dynamic performance  Energy saving Combine the thermal comfort control with a energy saving strategy  Air quality control Provide variable air volume (VAV) control, and adjust the fresh air and return air mix ratio to guarantee the required fresh air Introduction

8. 8  Block diagram of the thermal comfort control system Thermal comfort controller design

9. 9  Comfort zone learning logic User request? WarmerCooler Immediate Response Heat room Maintain response for duration=T1 Immediate Response Cool room Maintain response for duration=T1 Determine need to lower personal comfort zone Time since arrive Adapt- time > Time since last “cooler” request Repeat- time > Lower personal comfort zone Yes Determine need to raise personal comfort zone Time since arrive Adapt- time > Time since last “warmer” request Repeat- time > Raise personal comfort zone Yes Maintain Personal Comfort Zone Time within Comfort zone Energy-Conserving Response Let temperature drift at controlled rate Remain within limits of energy -conserving deadband Yes No Hold time > Maintain Energy Conserving deadband Thermal comfort controller design

10. 10  Thermal sensation model  The PMV formula proposed by Fanger (1970): where: M: metabolism (w/m 2 ) W: external work, equal to zero for most activity (w/m 2 ) M: metabolism (w/m 2 ) I cl : thermal resistance of clothing (clo) fcl: ratio of body’s surface area when fully clothed to body’s surface area when nude Pa: partial water vapor pressure (Pa) Heat loss by convection Heat loss by radiation Dry respiration heat loss Heat loss by skin diffusion Latent respiration heat loss Internal heat production Models of the thermal comfort controller

11. 11  Thermal sensation model  The personal-dependant variables, activity level and the clo-value can’t be measured directly, and hence, in the practical design, they are set as constant parameters according to different season  The PMV calculation formula is nonlinear and necessitate iterative calculation. In the simulation, a computer calculation model proposed by D. Int-Hout is used  If high real time performance is required, we can also adopt the PMV-like index (Federspiel and Asada, 1994):  Or we can also use Neural Network to build a PMV calculation model Models of the thermal comfort controller

12. 12  Thermal space model  A lumped parameter single-zone house model is built  The sensible and latent energy exchange is taken into consideration  The indoor air velocity is assumed proportional to the input airflow rate  A uniform wall temperature is assumed and regarded equal to the mean radiant temperature, etc. Heat exchanger Return air damper Flow mixer HVAC System Energy Input Thermal Space Supply Air Exhaust Air Fresh Air Flow Splitter Window Roof Wall Q win QrQr Q wall Pump Air velocity V air Air temperauture T a Radiant Air temperauture T mrt Air humidity RH a (P v ) Thermal load Q load TwTw TsTs T he ToTo RH o T o P vo T o T mix Q in PsPs Fa n Models of the thermal comfort controller

13. 13  Thermal space model  Three input variables: cooling capacity, air flow rate, fresh air and return air mix ratio  Three disturbances: indoor heat load, ambient temperature and humidity Models of the thermal comfort controller

14. 14  Controller design  The conventional comfort controllers are based on the on-off control or PI / PID control  To overcome the nonlinear feature of PMV calculation, time delay and system uncertainty, some advanced control algorithms have been proposed  Fuzzy adaptive control (Dounis and Manolakis, 2001; Calvino et al, 2004)  Optimal comfort control (MacArthur and Grald, 1988)  Minimum-power comfort control (Federspiel and Asada, 1994)  A kind of direct NN controller is designed based on back-propagation algorithm in this paper, which has been successfully applied in the hydronic heating systems (A. Kanarachos et al, 1998) Design of NN controller

15. 15  NN Controller design  A two-layer MISO NN controller is designed, which has two inputs and one output: e is the error between the PMV set value and feedback value, is the error derivative; and u is the output to control the HVAC system. Design of NN controller

16. 16  I. Settings of major simulation parameters  Heating and cooling performance are investigated  CAV (constant-air-volume) and VAV (variable-air-volume) applications are investigated Simulation of the thermal comfort controller Simulation ParameterSettings (Cooling)Settings (Heating) Dimension of thermal space5m × 5m × 3m Clo-value0.61.3 Activity level (Metabolic rate)1.0Met (W/m 2 ) Cooling / heating load Q Load 0.8KW–1.6KW HVAC capacity-8KW12KW Desired minimum fresh air flow rate (for VAV) 150m 3 /h (0.042 m 3 /s) 150m 3 /h (0.042 m 3 /s) Air flow rate f mix (for CAV)980 m 3 /h (0.272 m 3 /s) 980 m 3 /h (0.272 m 3 /s) Mixed air ratio r (for CAV)44 Outdoor temperature range T o 25 o C~33 o C4 o C~12 o C Outdoor Humidity range RH o 65%~85%45%~65%

17. 17  II. System performance under thermal comfort control and temperature control  For the temperature control, the reference input is 23 o C (cooling) and 25 o C (heating)  For the comfort control, the reference input is 0 Simulation of the thermal comfort controller

18. 18  III. System performance under direct NN control and PI control  For the well-tuned PI controller with integral anti-windup, When the control output reaches the limitation, the integral action is cut off  For the comfort controller, the learning coefficient is set as η* = 0.315 Simulation of the thermal comfort controller

19. 19  IV. Cooling / heating response under thermal comfort control Simulation of the thermal comfort controller

20. 20  V. Minimum-power control strategy under VAV Control  By adjusting the air flow rate f mix, mixed air ratio r, and the PMV value according to the user’s comfort zone, energy saving can be obtained Simulation of the thermal comfort controller

21. 21  VI. System Performance under CAV and VAV Control  Within 12 hours, cooling power consumed by VAV and CAV systems are 25.93KWh and 28.93KWh respectively, and hence, 3KWh cooling power can be saved Simulation of the thermal comfort controller

22. 22 Conclusion and further work  Conclusion  The conventional temperature controller (on / off control or PI control ), can’t guarantee the high comfort level (PMV = 0)  The thermal comfort controller can keep the thermal environment at the highest level  The designed NN controller has good control performance and disturbance rejection ability, and easy to fine tune in practice  The proposed minimum-power control strategy can achieve high comfort level as well as the energy saving at the same time  Further work  Measurement of the activity level and the clo-value  Location of sensor  Development of the cost-effective thermal comfort control system

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