Glasdasha: predicting arrival times from crowd sourced smartphone data without localisation or route maps Nominated for UCC best invention award 2012 Nominated for UCC best invention award 2012 Finals May 14 th 2013. Finals May 14 th 2013. Dr. Damien Fay Smart Technology Research Centre, School of Design Engineering and Computing, University of Bournemouth. Dr. Ken Brown, Han Wang, ITOBO PI, Ph.D., Co-inventor UCC, Cork, Ireland NUIG, Galway, Ireland
Background to Building Energy systems. ITOBO: I nformation and C ommunication T echnology for S ustainable and O ptimised B uilding O peration. Energy usage in buildings accounts for between 20% and 40% [1] of total energy consumption, Heating Ventilation and Air Conditioning (HVAC) systems accounting for 50% of this figure 20% of total energy consumption (in the USA [1]) [1] L. Prez-Lombard, J. Ortiz, and C. Pout. A review on buildings energy consumption information. Energy and Buildings, 40(3):394 – 398, 2008.
Overview of the wider field. Building systems research focuses on: Retro-fitting HVAC* systems Storage heating systems. Boiler systems 50 % of energy expended “moving air” Sensor networks: detecting presence, indoor localisation, temperature, black body radiation, humidity, CO 2 Preference modelling. Weather forecasting and BMS* BMS not connected to the network. MPC* control Predictive control; requires forecasts of occupancy . HVAC – Heating ventilation and Air Conditioning. MPC – Model Predictive Control. BMS – Building Management System (the computer that operates the HVAC).
Occupancy modelling prior research. References [1] B. Dong and B. Andrews, ”Sensor-based occupancy behavioral pattern recognition for energy and comfort management in intelligent buildings”, IBPSA Conf. Glasgow, Scotland, July 27-30, 2009. [2] M. Hoeynck and B. W. Andrews. Sensor-based occupancy and behavior prediction method for intelligently controlling energy consumption within a building,Patent 20 100 025 483, 04 02, 2010. [Online]. Available: http://www.faqs.org/patents/app/20100025483. [3] D. Bourgeois, I. Macdonald, J. Hand, C. Reinhart. Adding Sub-Hourly Occupancy Prediction, Occupancy- Sensing Control And Manual Environmental control to ESP-r, Proceedings of the ESIM 2004 Conference, Vancouver, B.C., June 10-11, 2004, pp. 1-8 [41] R. Sallehuddin and S. M. Hj. Shamsuddin, Hybrid grey relational artificial neural network and auto regressive integrated moving average model for forecasting time- series data, Appl. Artif. Intell., vol. 23, pp. 443486, May 2009. [42] E. Manavoglu, D. Pavlov, and C. Giles, Probabilistic user behavior mod-ls, in Data Mining, 2003. ICDM 2003. Third IEEE International Conference on, nov. 2003, pp. 203 210. [43] A. Mahdavi and C. Prglhf, User behavior and energy performance in buildings, in 6th Internationalen En- ergiewirtschaftstagung an der TU Wien, IEWT 2009, February 2009. [44] D.J.C. MacKay, Information Theory, Inference and Learning Algorithm Cambridge University Press, 2003. 10 [45] C. Liao, Y. Lin, P. Barooah. Agent-based and graphical modelling ofbuilding occupancy. Journal of Building Performance Simulation, 2011.
Occupancy prediction via smartphone localisation – prior research. References [1] M. Gupta, S. S. Intille, and K. Larson. Adding gps-control to traditional thermostats: An exploration of potential energy savings and design challenges. In Proceedings of the 7th International Conference on Pervasive Computing, Pervasive ’09, pages 95–114,Berlin, Heidelberg, 2009. Springer-Verlag. [2] J. Scott, J. Krumm, B. Meyers, A. J. Brush, and A. Kapoor. Home heating using gps-based arrival prediction, MSR research paper, 2010. [3] J. Krumm and A. J. B. Brush. Learning time-based presence probabilities. In Pervasive computing, pages 79–96, 2011. Biagioni, T. Gerlich, T. Merrifield, and J. Eriksson. Easytracker: automatic transit tracking, mapping, and arrival time prediction using smartphones. In SenSys, pages 68– 81, 2011. A. Thiagarajan and J. Biagioni. Cooperative transit tracking using smart-phones. Challenges, pages 85–98, 2010. G. Liu and J. Maguire, G.Q. A predictive mobility management algorithm for wireless mobile computing and communications. In Universal Personal Communications. 1995. Record., 1995 Fourth IEEE International Conference on, pages 268 –272, nov 1995. K. Laasonen. Clustering and prediction of mobile user routes from cellular data. In PKDD, pages 569–576, 2005.
Occupancy prediction via smartphone localisation – prior research. References [1] M. Gupta, S. S. Intille, and K. Larson. Adding gps-control to traditional thermostats: An exploration of potential energy savings and design challenges. In Proceedings of the 7th International Conference on Pervasive Computing, Pervasive ’09, pages 95–114,Berlin, Heidelberg, 2009. Springer-Verlag. Current location and home address given to third party. GPS Transit time assumes direct Current GPS route and mode of transport Home GPS known. MapQuest MapQuest estimates up to Transit time date?? Switch On heating May not be travelling home!.
Occupancy prediction via smartphone localisation – prior research. References [2] J. Scott, J. Krumm, B. Meyers, A. J. Brush, and A. Kapoor. Home heating using gps-based arrival prediction, MSR research paper, 2010. Current location and home address given to third party. GPS/WIFI/Basestation Prior on probability you are Current ∆ going home. Home GPS MapQuest Prediction time based on previous transit times (how?) Transit time Switch On heating
Occupancy prediction via smartphone localisation – prior research. References [2] J. Scott, J. Krumm, B. Meyers, A. J. Brush, and A. Kapoor. Home heating using gps-based arrival prediction, MSR research paper, 2010. Building centric: GPS/WIFI/Basestation User is not the target but rather the building. Current ∆ Third party not necessary Home GPS MapQuest Privacy central to system: 1. Employer ~ has the right Transit time to know when you arrive, Switch 2. Not your location outside On heating of work.
Glas - Dasha GlasDasha is a smartphone (Gaelic for Green; Chinese for building.) application . Designed to automatically predict when the user will arrive in work. Turn on heating (or server!) in advance. Requires ~2 minutes of user setup and then no interaction from user at all . No similar product currently exists. Current research in this area is sparse and not targeted at BMS control.
GlasDasha setup screen. When at home users selects home and their wifi access point. When in work user selects work and their wifi access point. Select 'launch application on startup' User interaction is finished forever .
GlasDasha system overview . Wifi router Wifi router Wifi router Wifi router Route to work Western Gateway Building Soap message via data connection BMS connected server
GlasDasha system overview . Wifi router Wifi router Wifi router Wifi router Route to work Western Gateway Building Predicted arrival time:20 mins Confidence: 75% Later arrival: 15% Not going to work at all: 10% BMS connected server
Crowd sourcing for prediction . Predicting arrival time at a particular location is very difficult . Current approaches try to predict the sequence of points seen. GlasDasha is different: Only interested in the work building => one end location. Many users/workers expected to approach building => combine their information The building lies at the centre of a sea of access points (~1000). The aim is to discover these points in relation to the buliding. Western Gateway Building
Crowd sourcing for prediction . Solution: Crowd Source ; combine all journeys from all users to the building to form an estimate of the minimum time it takes from a point to the building. Western Gateway Building
Crowd sourcing for prediction . 10 min 10 min 1 min 1 min 5 min 10 min 10 min Western Gateway Building 1 min 1 min 5 min 10 min 1 min 10 min 1 min 5 min 5 min 5 min 10 min 10 min 10 min
Crowd sourcing for prediction . Build up a field with respect to the building surroundings: For a particular user his movements relative to this field form a pattern . These patterns are used to determine his arrival time& probability / if he is even on the way to work. 5 mins Western Gateway Building 10 mins 15 mins
Random waiting time Localisation: Create false field WIFI/GPS/Base station update Create field Information update Information Comparison to Field Compare to Transmit field stored patterns, J update message to server anonymously Transmit arrival Prediction message Private/Smartphone side. to server Public/Server logic side. Collect field information and Construct the field Decision code for systems Transmit field Statistical analysis information Block. to phones (trx initiated by phone)
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