PRECEPT: Occupancy Presence Prediction Inside A Commercial Building

With the increasing number of low-cost sensing modalities, bulk amount of spatial and temporal data is collected and accumulated from building systems. Substantial information could be extracted about occupant behavior and actions from the data gathered. Understanding the data provides an opportunity to decode movement patterns, circulation-flow i.e. how an occupant tends to move inside the building and extract occupant presence impressions. Occupant Presence can be defined as digital traces of spatial coordinates (x,y) of an occupant at a particular instant that moves within the monitored space and is represented by a chronologically ordered sequence of those position coordinates. This study analyzes the occupant presence inside a building and makes predictions on the next location, i.e., where an occupant possibly could be in the future. This paper introduces a predictive model for occupancy presence prediction using the data collected from an instrumented commercial building spanning for over 30 days - May 2019 to June 2019. The proposed prediction model named PRECEPT - is a variant of Recurrent Neural Network known as Gated Recurrent Unit (GRU) Network. PRECEPT is capable of learning mobility patterns and predict presence impressions based on the occupant's past spatial coordinates. We evaluate the performance of PRECEPT on a dataset using metrics such as Mean Squared Error (MSE) and Mean Absolute Error (MAE) for each training epoch. The model results in a Root Mean Squared Error (RMSE) value of 4.79 centimeters for a single occupant. We also illustrate how the prediction model can be used for the task of identifying important zones and extract unique space-usage patterns. This could further assist the Building Management System (BMS) authorities to reduce energy wastage and perform efficient HVAC control and intelligent building operations.