ABSTRACT
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.
- Abhay Arora, Manar Amayri, Venkataramana Badarla, Stéphane Ploix, and Sanghamitra Bandyopadhyay. 2015. Occupancy estimation using non intrusive sensors in energy efficient buildings.Google Scholar
- Federico Bartoli, Giuseppe Lisanti, Lamberto Ballan, and Alberto Del Bimbo. 2018. Context-aware trajectory prediction. In 2018 24th International Conference on Pattern Recognition (ICPR). IEEE, 1941--1946.Google ScholarCross Ref
- Anooshmita Das, Fisayo Caleb Sangogboye, Emil Stubbe Kolvig Raun, and Mikkel Baun Kjærgaard. 2019. HeteroSense: An Occupancy Sensing Framework for Multi-Class Classification for Activity Recognition and Trajectory Detection. In Proceedings of the Fourth International Workshop on Social Sensing. ACM, 12--17. Google ScholarDigital Library
- Varick L Erickson, Miguel Á Carreira-Perpiñán, and Alberto E Cerpa. 2014. Occupancy modeling and prediction for building energy management. ACM Transactions on Sensor Networks (TOSN) 10, 3 (2014), 42. Google ScholarDigital Library
- Alex Graves. 2013. Generating sequences with recurrent neural networks. arXiv preprint arXiv:1308.0850 (2013).Google Scholar
- Dirk Helbing and Peter Molnar. 1995. Social force model for pedestrian dynamics. Physical review E 51, 5 (1995), 4282.Google Scholar
- Basheer Qolomany, Ala Al-Fuqaha, Driss Benhaddou, and Ajay Gupta. 2017. Role of deep LSTM neural networks and Wi-Fi networks in support of occupancy prediction in smart buildings. In 2017 IEEE 19th International Conference on High Performance Computing and Communications; IEEE 15th International Conference on Smart City; IEEE 3rd International Conference on Data Science and Systems (HPCC/SmartCity/DSS). IEEE, 50--57.Google Scholar
- James Scott, AJ Bernheim Brush, John Krumm, Brian Meyers, Michael Hazas, Stephen Hodges, and Nicolas Villar. 2011. PreHeat: controlling home heating using occupancy prediction. In Proceedings of the 13th international conference on Ubiquitous computing. ACM, 281--290. Google ScholarDigital Library
- Xiao Wang and Patrick Tague. 2014. Non-invasive user tracking via passive sensing: Privacy risks of time-series occupancy measurement. In Proceedings of the 2014 Workshop on Artificial Intelligent and Security Workshop. ACM, 113--124. Google ScholarDigital Library
- Zixuan Wang and Hamid Aghajan. 2014. Tracking by detection algorithms using multiple cameras. In Distributed Embedded Smart Cameras. Springer, 175--188.Google Scholar
- Jie Zhao, Bertrand Lasternas, Khee Poh Lam, Ray Yun, and Vivian Loftness. 2014. Occupant behavior and schedule modeling for building energy simulation through office appliance power consumption data mining. Energy and Buildings 82 (2014), 341--355.Google ScholarCross Ref
Index Terms
- PRECEPT: occupancy presence prediction inside a commercial building
Recommendations
Prediction of building energy performance using mathematical gene-expression programming for a selected region of dry-summer climate
AbstractDeveloping energy-efficient buildings considering building design parameters can help reduce buildings' energy consumption. The energy efficiency of residential buildings is considered a top priority for the energy policies of a country. Thus, ...
Evaluating architectural layouts with neural networks
SIMAUD '17: Proceedings of the Symposium on Simulation for Architecture and Urban DesignDetermining the mixture of spaces that go into a building (the building's programming) is a difficult decision. Despite decades of research into effective layouts, designers still primarily rely on rules of thumb to determine how to allocate space. In ...
Mitigating the negative impact of new buildings on existing buildings’ user comfort—a case study analysis
Campus master plans are released every few years for developing and implementing its physical infrastructure. Open spaces, compactness, connectivity, greenness, and environmental impact have often been the focus on its framework. In particular, the ...
Comments