Thermal Modeling for a HVAC Controlled Real-life Auditorium Chenyang Lu
Endeavor on Smart Building 1. Instrumenting an auditorium 2. Modeling spatiotemporal thermal dynamics 3. Occupancy-based energy saving for HVAC 4. Micro-metering an apartment
Challenges Ø Heat, Ventilation and Air Conditioning (HVAC) consumes 33% of building energy. Ø HVAC control relies on accurate thermal models. Ø Large open spaces have complex spatiotemporal dynamics. q Examples: auditoriums, theatres, open offices, lobbies. Week-long temperature trace at different locations in an auditorium. 3
Spa6al Varia6on in an Auditorium • Temperature differs by ~2°C despite HVAC control. • Unique challenges in large open spaces. 4
Experimental Approach 1. Deploy 34 sensors in an auditorium for over three months. 2. Collect multimodal data to capture fine-grained spatiotemporal dynamics under HVAC control . 3. Identify thermal model based on data from all sensors. 4. Simplify model through sensor selection. 5
Instrumen6ng an Auditorium Ø Emerson wireless sensors: temperature, humidity. Ø HVAC sensors: air flow rate and temperature. Ø Wireless camera: occupancy and lighting (on/off). Wireless Sensors Brauer Hall Thermostats 1/2013 - 5/2013 Camera 6
Wireless Monitoring System Data Analysis Brauer Hall Database Auditorium Occupancy surveillance camera Temperature Temperature CO2 Sensor CO2 Sensor Sensor Sensor Temperature Sensor Particle Sensor Humidity Sensor Wireless Links W i r e l e s s L i n Wireless Links k s s k n i L s s e l e r i W Empirical Study Base Station 7
Instrumen6ng the Auditorium Ø Environmental monitoring q 34 temperature sensors q 15 humidity sensors q 1 condensa;on par;cle counter q 2 CO 2 sensors Ø HVAC: air flow rate, air temperature Ø Occupancy from camera Ø Database q Sensors con;nuously feed data to database over the Internet q Visualiza;on through web interface 8
Large Mul6-modal Dataset Ø Longitude: >8 months of data Ø Fine grained q Temperature: 1 reading per 1/3 degree change q Humidity: 1 reading per 1% degree change q Particle: 3 readings/second q CO2: 2 readings/hour q HVAC air flow: 4 readings/hour q Occupancy: 4 photos/hour 9
Temperature & Humidity Sensor Emerson wireless thermostats Ø Repurposed for distributed monitoring q Ø Capture fine-grained spatiotemporal dynamics Improve HVAC model and control q 2/25/13 – 3/3/13 10
Wireless Condensa6on Par6cle Counter HVAC people RetrofiHed with • Bluetooth. furnishings Particle sources Help understand • (chairs, carpet) impacts of HVAC on resuspension air quality hot food outdoors (traffic, dust) butanol wireless transmitter Instrument specifica6ons Uses butanol, single-count, and • photometric technology, to count particle number airborne par;cle numbers with a concentration ( µgm -3 ) diameter from 0.07 to 3 µm display Fast response ;me (<13 seconds) • Semi-portable • High-resolu;on (1Hz) data inlet • 11
Par6cle Number Concentra6ons Sunday HVAC switching to off mode (~ 9pm) Midterm exam 12
One Week of Data Traces (2/25 – 3/3) CO 2 Temperature Particle Occupancy Air flow 13 13
Endeavor on Smart Building 1. Instrumenting an auditorium 2. Modeling spatiotemporal thermal dynamics 3. Occupancy-based energy saving for HVAC 4. Micro-metering an apartment
Prior Modeling Approaches Ø Principle-driven: rely on detailed knowledge of building design and materials. Ø Data-driven: estimate model based on data. q Assume same temperature per room: ignore spatial variations and interactions within a large space. q Divide space into zones: reply on known inter-zone interactions. 15
Model Iden6fica6on Temperature T (k) Estimated temperature T (k+1) = AT (k) + BU (k) U (k): air flow rate & T (k+1) temperature, occupancy, light. Ø Model identification based on training data q Minimize modeling error with least square optimization q Solved using CVX toolbox for Matlab Ø Tradeoff between model complexity and accuracy q 1 st order model à simple q 2 nd order model à capture more complex dynamics 16
1 st vs. 2 nd Order Model Ø 2 nd order model more accurately captures the spatiotemporal dynamics in the auditorium. Measured vs. predicted temperature on 2/28/13 17
Model Simplifica6on Ø Disadvantages of fine-grained models based on all sensors q Complex model is unsuitable for control design. q Challenge in maintaining numerous sensors. Ø Approach: simplifying model through sensor selection q Sensor data have strong correlations. q Select a subset of sensors to capture spatiotemporal dynamics. q Identify thermal model based on selected sensors. Ø Advantage of model simplification q Practical for HVAC control. q Only need to keep the selected sensors during operation. q Dense sensor network needed only initially to collect training data. 18
Sensor Selec6on based on Clustering 1. Spectral clustering based on sensor data. q Value: group sensors with similar temperature values. q Correlation: group sensors whose data traces follow similar trends. 19
Correla6on-based Sensor Clustering Temperature correlation Two clusters 20
Sensor Selec6on based on Clustering 1. Spectral clustering based on sensor data. q Value: group sensors with similar temperature values. q Correlation: group sensors whose data traces follow similar trends. 2. Select a sensor from each cluster. q Stratified Random Selection (SRS): randomly choose one. q Stratified Mean Selection (SMS): select the sensor whose data is the closest to the cluster mean. 21
Model Simplifica6on • Clustering outperforms Random Selection (RS) • Stratified Mean Selection (SMS) is more accurate than Stratified Random Selection, especially for large clusters. 22
Summary: Thermal Modeling Ø Large open spaces have complex spatiotemporal dynamics. Ø Data-driven thermal modeling for large open spaces. Sensor network captures spatiotemporal dynamics. 1. Sensor selection based on data clustering. 2. Model identification based on data of selected sensors. 3. Ø Validated on data collected from a real-life auditorium. Ø Exciting opportunities ahead q Optimize HVAC control q Leverage air quality sensing for more aggressive energy saving Y. Fu, M. Sha, C. Wu, A. KuYa, A. Leavey, C. Lu, H. Gonzalez, W. Wang, B. Drake, Y. Chen and P. Biswas, Thermal Modeling for a HVAC Controlled Real-life Auditorium, ICDCS 2014. 23
Endeavor on Smart Building 1. Instrumenting an auditorium 2. Modeling spatiotemporal thermal dynamics 3. Occupancy-based energy saving for HVAC 4. Micro-metering an apartment
HVAC Energy Waste Ø Current HVAC operates on fixed schedule q On (occupied mode) during daytime (6am-9pm) q Off (non-occupied mode) at night Ø But the auditorium is vacant 80% of the time during the day! 25
Note: Occupancy Follows Calendar Ø Calendar predicts actual occupancy at >98% accuracy q Validated by camera Seminar Class Meeting 26
Schedule HVAC based on Calendar Ø Preconditioning: Start HVAC T p before an event q T p : time needed to reach the temperature set point q T p = 3 hours for the auditorium based on data traces Ø Save energy: Turn off HVAC if > T p till next event q Turn off HVAC immediately after the last event each day q HVAC remains off during weekends Ø Avoid thrashing: remains on if next event is within T p q Maintain comfort q Reduce unnecessary switching 27
Example Interval between events Precondi;oning Turn off aaer less than 3 hours 3 hours last event On Sun Sat Off 28
78% Energy Saving over 6 Weeks q Turning off HVAC immediately after last event à 36% q Turning off HVAC on Sat/Sun à 34% q Turning on HVAC late in the morning à 8% 29
Endeavor on Smart Building 1. Instrumenting an auditorium 2. Modeling spatiotemporal thermal dynamics 3. Occupancy-based energy saving for HVAC 4. Micro-metering an apartment
Smart Home: Objec6ves Ø Save energy while maintaining comfort. Ø Close the loop: intelligent control of appliances. Ø Human centered: incentivize residents to save energy. Ø Internet of Things: integrate sensors, appliances, cloud, and smartphones. 31
The Internet of Things UI BT / Weather station BTL AC listener 15.4 microserver WiFi Power 32 meter
Pilot – Components Ø ACme – Berkeley power meter q Based on the Epic core q Runs TinyOS q IPv6 over mesh network Ø Raspberry Pi Power meter q Very popular microserver Ø Ethernet connection to apartment router Ø Amazon EC2 as the cloud Ø Measuring major appliances power � consumption microserver 33
Endeavor on Smart Building 1. Instrumenting an auditorium 2. Modeling spatiotemporal thermal dynamics 3. Occupancy-based energy saving for HVAC 4. Micro-metering an apartment
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