HMMs Recursive Parameters Estimation for Semi-Bounded Data Modeling: Application to Occupancy Estimation in Smart Buildings

Fatemeh Nikroo, Manar Amayri, Nizar Bouguila

2023

Abstract

Optimizing energy consumption is one of the key factors in smart buildings developments. It is crucial to estimate the number of occupants and detect their presence when it comes to energy saving in smart buildings. In this paper, we propose a Hidden Markov Models (HMM)-based approach to estimate and detect the occupancy status in smart buildings. In order to dynamically estimate the occupancy level, we develop a recursive estimation algorithm. The developed models are evaluated using two different real data sets.

Paper Citation

in Harvard Style

Nikroo F., Amayri M. and Bouguila N. (2023). HMMs Recursive Parameters Estimation for Semi-Bounded Data Modeling: Application to Occupancy Estimation in Smart Buildings. In Proceedings of the 12th International Conference on Smart Cities and Green ICT Systems - Volume 1: SMARTGREENS, ISBN 978-989-758-651-4, SciTePress, pages 81-88. DOI: 10.5220/0011715200003491

in Bibtex Style

@conference{smartgreens23,
author={Fatemeh Nikroo and Manar Amayri and Nizar Bouguila},
title={HMMs Recursive Parameters Estimation for Semi-Bounded Data Modeling: Application to Occupancy Estimation in Smart Buildings},
booktitle={Proceedings of the 12th International Conference on Smart Cities and Green ICT Systems - Volume 1: SMARTGREENS,},
year={2023},
pages={81-88},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011715200003491},
isbn={978-989-758-651-4},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 12th International Conference on Smart Cities and Green ICT Systems - Volume 1: SMARTGREENS,
TI - HMMs Recursive Parameters Estimation for Semi-Bounded Data Modeling: Application to Occupancy Estimation in Smart Buildings
SN - 978-989-758-651-4
AU - Nikroo F.
AU - Amayri M.
AU - Bouguila N.
PY - 2023
SP - 81
EP - 88
DO - 10.5220/0011715200003491
PB - SciTePress