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


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.

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  1. 1.

    Arendt K, Ionesi A, Jradi M, Singh A, Kjærgaard M, Veje C, Jørgensen B (2016) A building model framework for a genetic algorithm multi-objective model predictive control. In: CLIMA 2016

  2. 2.

    Beltran A, Cerpa AE (2014) Optimal HVAC building control with occupancy prediction. In: BuildSys’14, pp 168–171

  3. 3.

    Dawson-Haggerty S, Jiang X, Tolle G, Ortiz J, Culler D (2010) sMAP: a simple measurement and actuation profile for physical information. In: SenSys. ACM, pp 197–210

  4. 4.

    Erickson V, Carreira-Perpinan MA, Cerpa AE (2011) OBSERVE: occupancy-based system for efficient reduction of HVAC energy. IPSN 2011:258–269

    Google Scholar 

  5. 5.

    Forman G, Scholz M (2010) Apples-to-apples in cross-validation studies: pitfalls in classifier performance measurement. SIGKDD Explor Newsl 12(1):49–57

    Article  Google Scholar 

  6. 6.

    Kjærgaard MB, Johansen A, Sangogboye FC, Holmegaard E (2016) Occure: an occupancy reasoning platform for occupancy-driven applications. In: CBSE. ACM

  7. 7.

    Kjærgaard MB, Sangogboye FC (2017) Categorization framework and survey of occupancy sensing systems. Pervasive Mob Comput 38:1–13

  8. 8.

    Koehler C, Banovic N, Oakley I, Mankoff J, Dey AK (2014) Indoor-alps: an adaptive indoor location prediction system. In: UbiComp, pp 171–181

  9. 9.

    Koehler C, Ziebart BD, Mankoff J, Dey AK (2013) Therml: occupancy prediction for thermostat control. In: UbiComp, pp 103–112

  10. 10.

    McKinney W (2011) pandas: a foundational Python library for data analysis and statistics. In: PyHPC 2011: Workshop on Python for High Performance and Scientific Computing, SC11, Seattle, WA, USA, pp 1–9

  11. 11.

    Pedregosa F, Varoquaux G, Gramfort A, Michel V, Thirion B, Grisel O, Blondel M, Prettenhofer P, Weiss R, Dubourg V, Vanderplas J, Passos A, Cournapeau D, Brucher M, Perrot M, Duchesnay E (2011) Scikit-learn: machine learning in Python. JMLR 12:2825–2830

    MathSciNet  MATH  Google Scholar 

  12. 12.

    Sakoe H, Chiba S (1978) Dynamic programming algorithm optimization for spoken word recognition. IEEE TASSP 26(1):43–49

    Article  MATH  Google Scholar 

  13. 13.

    Sangoboye FC, Kjærgaard MB (2016) Predicting occupancy presence in multiple resolutions for commercial buildings: poster abstract. In: Proceedings of the 3rd ACM international conference on systems for energy-efficient built environments, BuildSys’16, New York, NY, ACM, pp 229–230

  14. 14.

    Sangogboye FC, Arendt K, Singh A, Veje CT, Kjærgaard MB, Jørgensen BN (2017) Performance comparison of occupancy count estimation and prediction with common versus dedicated sensors for building model predictive control. Build Simul. doi:10.1007/s12273-017-0397-5

  15. 15.

    Sangogboye FC, Imamovic K, Kjaergaard MB (2016) Improving occupancy presence prediction via multi-label classification. In: PerEnergy 2016. IEEE

  16. 16.

    Scott J, Brush AJB, Krumm J, Meyers B, Hazas M, Hodges S, Villar N (2011) Preheat: controlling home heating using occupancy prediction. UbiComp 2011:281–290

    Google Scholar 

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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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Correspondence to Fisayo Caleb Sangogboye.

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Sangogboye, F.C., Kjærgaard, M.B. PROMT: predicting occupancy presence in multiple resolution with time-shift agnostic classification. Comput Sci Res Dev 33, 105–115 (2018).

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  • Occupancy framework
  • Presence prediction
  • Energy savings
  • Demand response