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Computer Science - Research and Development

, Volume 33, Issue 1–2, pp 105–115 | Cite as

PROMT: predicting occupancy presence in multiple resolution with time-shift agnostic classification

  • Fisayo Caleb Sangogboye
  • Mikkel Baun Kjærgaard
Special Issue Paper
  • 185 Downloads

Abstract

Improving methods for predicting occupant presence in commercial buildings is crucial for optimizing energy consumption. Also it is crucial for providing amiable indoor environmental conditions. To enable these improvements, we require a more accurate and flexible framework for predicting occupancy. The promt framework proposed in this paper is an accurate and flexible framework for predicting occupancy presence in multiple resolution with time-shift agnostic classification. promt assumes that no single prediction algorithm, model, or static model parameter can guarantee high fidelity occupancy prediction for varying occupancy requirements and for every kind of rooms. Given this assumption, the promt framework facilitates the deployment of several prediction algorithms and it performs an hyper-parameter optimization procedure on all deployed algorithms to obtain the optimal model for obtaining occupancy prediction in covered room. promt was benchmarked with datasets from two building cases by comparing the F-score of the prediction results obtained from all deployed algorithms. The results document that promt outperforms the performance of any single prediction algorithm by a maximum difference in F-score of 2.3% and a minimum difference in F-score of 0.58%. As a case study we demonstrate the use of promt for scheduling demand response events in a commercial building.

Keywords

Occupancy framework Presence prediction Energy savings Demand response 

Notes

Acknowledgements

This work is supported by EUDP for the Project Demand-Response Capacity Management in Commercial Buildings (64014-0512) and Innovation Fund Denmark for COORDICY (4106-00003B).

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Copyright information

© Springer-Verlag GmbH Germany 2017

Authors and Affiliations

  • Fisayo Caleb Sangogboye
    • 1
  • Mikkel Baun Kjærgaard
    • 1
  1. 1.SDU Center for Energy Informatics, Mærsk McKinney Møller InstituteUniversity of Southern DenmarkOdenseDenmark

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