Mahamadou Klanan Diarra
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Mahamadou Klanan Diarra has a total of 4 co-authors. Here are the top co-authors by number of shared publications:
- Amine Maniar
Laboratory of Complex Cyber Physical Systems, Hassan II University of Casablanca, Casablanca, Morocco
1 shared publication - Bruno Marhic
Laboratoire des Technologies Innovantes (LTI) EA 3899, Département Informatique, Avenue des Facultés le Bailly, 80000 Amiens Cedex 1, France
1 shared publication - Jean-Baptiste Masson
Institut Pasteur, Université Paris Cité, CNRS UMR 3571, Decision and Bayesian Computation, Paris, France
1 shared publication - Laurent Delahoche
Laboratory of Innovative Technologies, University of Picardie Jules Verne, 80000 Amiens, France
1 shared publication
Latest Publications
- Open Access
Occupancy State Prediction by Recurrent Neural Network (LSTM): Multi-Room Context
The energy consumption of a building is significantly influenced by the habits of its occupants. These habits not only pertain to occupancy states, such as presence or absence, but also extend to more detailed aspects of occupant behavior. To accurately capture this information, it is essential to use tools that can monitor occupant habits without altering them. Invasive methods such as body sensors or cameras could potentially disrupt the natural habits of the occupants. In our study, we primarily focus on occupancy states as a representation of occupant habits. We have created a model based on artificial neural networks (ANNs) to ascertain the occupancy state of a building using environmental data such as CO2 concentration and noise level. These data are collected through non-intrusive sensors. Our approach involves rule-based a priori labeling and the use of a long short-term memory (LSTM) network for predictive purposes. The model is designed to predict four distinct states in a residential building. Although we lack data on actual occupancy states, the model has shown promising results with an overall prediction accuracy ranging between 78% and 92%.
Open AccessOccupancy State Prediction by Recurrent Neural Network (LSTM): Multi-Room Context
The energy consumption of a building is significantly influenced by the habits of its occupants. These habits not only pertain to occupancy states, such as presence or absence, but also extend to more detailed aspects of occupant behavior. To accurately capture this information, it is essential to use tools that can monitor occupant habits without altering them. Invasive methods such as body sensors or cameras could potentially disrupt the natural habits of the occupants. In our study, we primarily focus on occupancy states as a representation of occupant habits. We have created a model based on artificial neural networks (ANNs) to ascertain the occupancy state of a building using environmental data such as CO2 concentration and noise level. These data are collected through non-intrusive sensors. Our approach involves rule-based a priori labeling and the use of a long short-term memory (LSTM) network for predictive purposes. The model is designed to predict four distinct states in a residential building. Although we lack data on actual occupancy states, the model has shown promising results with an overall prediction accuracy ranging between 78% and 92%.