Occupancy data analytics and prediction: A case study Xin Liang, Tianzhen Hong, Geoffrey Qiping Shen Presented by: Debarun Das

Outline Background Applications Methodology Case Study Conclusions Machine Learning Algorithms Case Study Conclusions Further Study Outline

Background Occupant Presence Modelled By: Fixed Schedules Occupants categorized into groups Early Bird, Timetable complier, Flexible worker Each group assigned to a specific schedule Occupant presence satisfies a probability distribution Binomial Distribution, Poisson Distribution etc Analyzing Practical Observation Data Limited to single or a few offices T. Zhang, P.-O. Siebers, U. Aickelin, Modelling electricity consumption in office D. Wang, C.C. Federspiel, F. Rubinstein, Modeling occupancy in single person K. Sun, D. Yan, T. Hong, S. Guo, Stochastic modeling of overtime occupancy and its application in building energy simulation and calibration offices,

https://irail.be/spitsgids Applications Train Occupancy Prediction iRail - Spitsgids from Belgium Uses Crowdsourced data for Occupancy prediction Hospital Occupancy Prediction Time Series data Statistical models of prediction Control Of HVAC systems Logistic Regression Model Reduces Electricity Consumption Improves User Comfort https://irail.be/spitsgids Steven J. Littig & Mark W. Isken, «Short term hospital occupancy prediction” Jie Shia, Nanpeng Yua, Weixin Yaob, “Energy efficient building HVAC control algorithm with real-time occupancy prediction”

Methodology

Machine Learning Algorithms Unsupervised Clustering Algorithm- k means Supervised Decision Tree Learning - Algorithm C4.5

k-means Discovers patterns of occupancy schedule Needs a predefined value of k Needs a predefined distance definition Chooses among: Euclidean Distance Correlation Similarity Dynamic Time Wrap

k-means - Choosing k and Distance Metric Davies-Bouldin Index 𝐷𝐵𝐼= 1 𝑛 𝑖=1 𝑛 max 𝑗≠1 𝜎 𝑖 +𝜎 𝑗 𝑑 𝑐 𝑖 , 𝑐 𝑗 Optimal Parameters k = 4 Distance Metric = Euclidean

Decision Tree Summarizes rules within the patterns The attribute to split is chosen by Information Gain, 𝐺𝑎𝑖𝑛 𝑆, 𝐴 𝐺𝑎𝑖𝑛 𝑆, 𝐴 =𝐻 𝑆 − 𝑣∈𝑉𝑎𝑙𝑢𝑒𝑠(𝐴) | 𝑆 𝑣 | |𝑆| 𝐻( 𝑆 𝑣 ) where 𝐻 𝑆 = − 𝑝 𝑖 log 2 𝑝 𝑖

Decision Tree - Example http://www.inf.ed.ac.uk/teaching/courses/iaml/2011/slides/dt.pdf

Case Study Building 101 in the Navy Yard, Philadelphia Four sensors installed at the gates of the building Records no. of Occupants Entering and Exiting the building 𝑁 𝑡𝑜𝑡𝑎𝑙 = 1 𝑖 (𝑁 𝑖1 − 𝑁 𝑖2 + 𝑁 𝑖3 − 𝑁 𝑖4 + 𝑁 𝑖5 − 𝑁 𝑖6 + 𝑁 𝑖7 − 𝑁 18 ) Uses Matlab and RapidMiner

General Characteristics of Occupant Presence Low occupant presence on weekends and holidays Excludes weekend and holiday data High Variance of data from 7am to 4pm Stochastic and highly variable

General Characteristics of Occupant Presence Night (7pm - 6am) Going-to-Work (7am – 9am) Morning (10am – 12pm) Noon-break (12 pm – 1pm) Afternoon (2pm – 3pm) Going-home (4pm – 6pm)

Patterns of Occupant Presence Occupancy Rate Working Time Going To Work Going To Home Noon Break Pattern 1 Lowest Shortest Latest Earliest NA Pattern 2 Highest Longest Later 12 pm Pattern 3 Medium 2 pm Pattern 4 Earlier 1 pm

Rules of Patterns 3 influencing factors Seasons (temperatures) Weekdays Daylight Saving Time (DST) DST cannot contribute to enough information gain

Prediction of Occupancy Schedule Method 1 𝑃𝑟𝑒𝑑𝑖𝑐𝑡𝑖𝑜𝑛 𝑡 = 𝑀 𝑑𝑎𝑦 𝑡 Method 2 𝑃𝑟𝑒𝑑𝑖𝑐𝑡𝑖𝑜𝑛 𝑤𝑒𝑒𝑘𝑑𝑎𝑦,𝑡 = 𝑀 𝑤𝑒𝑒𝑘𝑑𝑎𝑦 𝑡 Method 3 𝑃𝑟𝑒𝑑𝑖𝑐𝑡𝑖𝑜𝑛 𝑑𝑎𝑦,𝑡 = 𝑀 𝑝1 . 𝑃 𝑝1 + 𝑀 𝑝2 . 𝑃 𝑝2 + 𝑀 𝑝3 . 𝑃 𝑝3 + 𝑀 𝑝4 . 𝑃 𝑝4

Validation 𝑅𝑀𝑆𝐸= 𝑖=1 𝑛 ( 𝐸 𝑖 − 𝐸 𝑖 ) 2 𝑛 𝑀𝐴𝐸= 𝑖=1 𝑛 | 𝐸 𝑖 − 𝐸 𝑖 | 𝑛 𝑅𝑀𝑆𝐸= 𝑖=1 𝑛 ( 𝐸 𝑖 − 𝐸 𝑖 ) 2 𝑛 𝑀𝐴𝐸= 𝑖=1 𝑛 | 𝐸 𝑖 − 𝐸 𝑖 | 𝑛 𝑚𝑒𝑑𝐸=𝑚𝑒𝑑𝑖𝑎𝑛( 𝐸 𝑖 − 𝐸 𝑖 )

Conclusions High accuracy in prediction of occupant behavior Simple input data Relatively Simple Algorithms Lower Complexity compared to learning algorithms like Neural Nets Simple Prediction method – weighted mean Can be applied for control of energy consumption Accuracy of occupancy detection procedure is not discussed in details Does not compare against other learning algorithms

Further Readings Usman Habib, Gerhard Zucker, “Automatic occupancy prediction using unsupervised learning in buildings data” James Scott , A.J. Bernheim Brush, John Krumm, Brian Meyers, Mike Hazas, Steve Hodges, Nicolas Villar, “PreHeat: Controlling Home Heating Using Occupancy Prediction”

Thank you!