Online Unsupervised Occupancy Anticipation System Applied to Residential Heat Load Management

• Published in: IEEE access Vol. 9; pp. 109806 - 109821
• Main Authors: Rueda, Luis, Agbossou, Kodjo, Henao, Nilson, Kelouwani, Sousso, Oviedo-Cepeda, Juan C., Le Lostec, Brice, Sansregret, Simon, Fournier, Michael
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 2021; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Buildings; Cost function; Dwellings; Electrical loads; Electricity pricing; Energy consumption; Hazards; Hidden Markov models; Load shifting; Markov chains; Monte Carlo simulation; Occupancy; Occupant behavior; occupant-centric control; Prediction algorithms; Predictive models; Space heating; survival analysis; Thermal comfort
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2021.3098631

Human preferences and lifestyles significantly impact buildings' energy consumption. Consequently, a better understanding of occupants' behavior is crucial to decrease energy consumption and maintain occupants' comfort. Occupant-centric control (OCC) strategies are effective approaches to fulfil such a purpose. As such, occupancy detection and prediction are of prime importance, particularly to manage Electric Space Heating (ESH) systems, due to the relatively slow dynamics of the temperature in dwellings. This paper proposes an Explicit Duration Hidden Markov Model (EDHMM) for unsupervised online presence detection and a hazard-based approach for occupancy prediction. Moreover, a control strategy using a cost function, weighted by occupancy predictions, and a load-shifting strategy based on time-varying electricity price are put forward. This work initially validates the consistency of the proposed approach by using synthetic data generated by a Monte Carlo simulation. Subsequently, the performance of our framework is compared with previous methods presented in the literature through experimental validation. Results demonstrate that the proposed EDHMM approach is efficient in detecting occupancy states. Besides, the results of the field implementation show the potential of the proposed control strategy to preserve occupants' thermal comfort while decreasing the heating energy consumption.