Incorporation of scheduling and adaptive historical data in the Sensor-Utility-Network method for occupancy estimation

• Published in: Energy and buildings Vol. 61; pp. 88 - 92
• Main Authors: Ryan, Tim, Vipperman, Jeffrey S.
• Format: Journal Article
• Language: English
• Published: Oxford Elsevier B.V 01.06.2013; Elsevier
• Subjects: Adaptive historical data; Applied sciences; Buildings. Public works; Computation methods. Tables. Charts; Exact sciences and technology; Occupancy estimation; Prediction; Structural analysis. Stresses; Weighted schedule; United States; North America; America; Occupancy estimation; Weighted schedule; Adaptive historical data; Prediction; Energy consumption; Data analysis; Measurement sensor; Scheduling; Forecast model; Commercial facility; Modeling; Adaptive method; Survey; Resident
• ISSN: 0378-7788
• DOI: 10.1016/j.enbuild.2013.01.037

► A weighted scheduling term was added to the Sensor-Utility-Network method for occupancy detection. ► A mechanism for adaptively updating historical data was added to the same. ► The two improvements were reduced error by one third. Two improvements were made to the Sensor-Utility-Network method of occupancy estimation found in the literature, which casts the occupancy estimation/prediction problem as a convex program with an objective function based on various data sources that contribute to occupancy information. The improvements were the inclusion of a scheduling term in the objective function and a mechanism for updating the historical data automatically. The schedule term weighted the difference between projected estimates and scheduled occupancy based on zone usage such that zones with stricter adherence to schedules have more influence. The historical data were updated through an exponentially weighted average of estimates at the same time on past similar days. The estimates for a given time on a given day were calculated as weighted averages of all estimates for that time, including estimates from past and future times on that day. After implementing the SUN algorithm in Matlab, it was tested using data from an agent-based occupancy model. The improvements were added and resulted in a one third decrease in estimation error.