IPSN 2013

1. Matchstick: A Room-to-Room Thermal Model for Predicting Indoor Temperature from Wireless Sensor Data IPSN 2013 Carl Ellis (School of Computing and Communications, Lancaster University, UK), Mike Hazas (School of Computing and Communications, Lancaster University, UK), James Scott (Microsoft Research, Cambridge, UK) NSLab study group 2013/4/15 Speaker : Chia-Chih,Lin

2. Outline • Introduction • Deployment • Modeling • Evaluation • Conclusion

3. Introduction • Home space heating accounted for 62% of total domestic energy consumed • Typically equipped with a programmable thermostate • several methods has worked to improve the comfort and energy saving • But these utilized simple heating models for their houses • Complementary ,a heating model could allow future temperature trends to be predicted

4. Introduction cont. • The paper purposes that simple temperature sensor, combined with real-time algorithms • Two features for model • Recognize different spaces heat and cool not only due to insulation, but also thermal masses • Automatically identifies rooms which appear to have a thermal relationship

5. Introduction cont. • Contribution: • Predictive performance : two-hour lookahead error 1.5 degree or better (90% confidence level) • Highlight the energy savings opportunities

6. Outline • Introduction • Deployment • Modelling • Evaluation • Conclusion

7. Deployment • 4 houses • 2 in US (US1,US2) • 2 in UK (UK1,UK2) • Variety of sensors used • UK : .NET Gadgeteer (ref. [17]) • US :iButtonThermochron sensors • UK home data: each homes radiator could be actuated independently • Over various winter periods in 2010-2011

8. Deployment • In UK deployment • WSN with 802.15.4 radio network to a PC server located in house. • (per room temperature data) logged 5 sec/time • Outdoor temperature gathered from a local weather station • Whole house gas measurement • Thermostatic radiator valves were actuated by House Heat FHT-8Vs(controlled by PC) • Reading were downsampled to one measure per 5 mins

10. FHT 8V Wireless Actuator

11. Deployment • In US deployment • 20 iButtonThermochrons • At least one in each room • 2~3 in large room • Out door temperature get by putting one iButton outside • One place on furnace directly to sense actuation time • Sensor sampled 10 mins per time

12. Deployment • Building Characteristic • UK1 : • two-floor building with a gas boiler, TRV-equipped radiators • Underfloor heating in first floor, radiator in second floor • UK2 : three floor 19th century house with wall-mounted convection radiators • US1&US2 : • north-west USA • Air heating system(powered by a furnace)

14. Outline • Introduction • Deployment • Modeling • Evaluation • Conclusion

15. Modeling • Use a regression based optimization model • Consider room-to-room interaction, thermal mass delay, and outside temperature • Use a non-linear transformation of gas use • Fits between the heating scheduler

16. Modeling • Training by historical data, then using model parameters to predict the result and adjust the schedule, parameters involves: • Current sensor data • Heating schedule

18. Thermal mass delay • Delay between thermal energy input, change of the heating element temperature, and ambient indoor air temperature

20. Recursive non-linear transformation function Gt : gas usage σ: thermal energy(stored in room’s heating element,[0~1]) RTn :empirically determined by search the solution space and finding value when traning the model with historical data

22. Internal interaction between rooms • Need to determine the thermally significant neighbors automatically [18] • Recursive likelihood test is performed • Initially fitted with no neighbors->likelihood-ratio test ->if null hypothesis is rejected->the most likely neighbor added

23. Fitting the Matchstick Model

24. The mathematical form of Matchstick’s system equations N : the set of all room Tn : temperature of room n G : gas used TO : outside temperature αt : loss of heat from the room αg :heat transfer from the heating space βnj :transfer of heat from thermally significant neighboring rooms ϒo : heat transfer with the outside

25. Outline • Introduction • Deployment • Modeling • Evaluation • Conclusion

26. Evaluation • Characterize the predictive accuracy of the model • Analyze how the predictive accuracy changes for different rooms in different houses • Investigate the effect of the model’s training aspects

27. predictive accuracy of the model • 3 weeks predict ,1 week as training data • Supply two types of future knowledge • Future gas input • Future outside temperature • Train model -> for each time step t(0~24) can predict p hours -> modeling each time step until t+p reached -> stored and compare to ground truth -> make error distribution • (p : 1.5hr~6hr)

28. predictive accuracy of the model

30. different rooms in different houses

31. Compare to other model [15] : each room relied upon the predictions of others in their model [9] : could be because the model does not capture neighboring interaction

32. Model Tuning • How training data affect the model • Length of training data • How to select initial neighboring rooms to be passed to the model

33. Length of training data

34. Using different policies for neighboring rooms

35. Saving analysis

36. Results • UK1 saved 3.3% of its total gas • UK2 saved 2.3% • Original study [7] improve 8-18%

37. Outline • Introduction • Deployment • Modeling • Evaluation • Conclusion

38. Conclusion • Matchstick, a data driven adaptive model • Relies on relatively sparse sensor deployments • Predicts across three weeks of data in four houses in two different countries. • Can achieve gas savings by trimming down furnace or boiler actuation schedules

39. Q&A • Thanks for listening !