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 u...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-12-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/23/23/9603 |
| _version_ | 1797399543445192704 |
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| author | Mahamadou Klanan Diarra Amine Maniar Jean-Baptiste Masson Bruno Marhic Laurent Delahoche |
| author_facet | Mahamadou Klanan Diarra Amine Maniar Jean-Baptiste Masson Bruno Marhic Laurent Delahoche |
| author_sort | Mahamadou Klanan Diarra |
| collection | DOAJ |
| description | 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 CO<sub>2</sub> 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%. |
| first_indexed | 2024-03-09T01:41:43Z |
| format | Article |
| id | doaj.art-120093d19a194fb39c6a58e472a15bf9 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-09T01:41:43Z |
| publishDate | 2023-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-120093d19a194fb39c6a58e472a15bf92023-12-08T15:26:33ZengMDPI AGSensors1424-82202023-12-012323960310.3390/s23239603Occupancy State Prediction by Recurrent Neural Network (LSTM): Multi-Room ContextMahamadou Klanan Diarra0Amine Maniar1Jean-Baptiste Masson2Bruno Marhic3Laurent Delahoche4Laboratory of Innovative Technologies (LTI UR 3899), Picardy Jules Verne University, 80000 Amiens, FranceLaboratory of Innovative Technologies (LTI UR 3899), Picardy Jules Verne University, 80000 Amiens, FranceLaboratory of Innovative Technologies (LTI UR 3899), Picardy Jules Verne University, 80000 Amiens, FranceLaboratory of Innovative Technologies (LTI UR 3899), Picardy Jules Verne University, 80000 Amiens, FranceLaboratory of Innovative Technologies (LTI UR 3899), Picardy Jules Verne University, 80000 Amiens, FranceThe 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 CO<sub>2</sub> 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%.https://www.mdpi.com/1424-8220/23/23/9603machine learningneural networkartificial neural networkslong short-term memoryoccupant behaviorbuilding energy consumption |
| spellingShingle | Mahamadou Klanan Diarra Amine Maniar Jean-Baptiste Masson Bruno Marhic Laurent Delahoche Occupancy State Prediction by Recurrent Neural Network (LSTM): Multi-Room Context Sensors machine learning neural network artificial neural networks long short-term memory occupant behavior building energy consumption |
| title | Occupancy State Prediction by Recurrent Neural Network (LSTM): Multi-Room Context |
| title_full | Occupancy State Prediction by Recurrent Neural Network (LSTM): Multi-Room Context |
| title_fullStr | Occupancy State Prediction by Recurrent Neural Network (LSTM): Multi-Room Context |
| title_full_unstemmed | Occupancy State Prediction by Recurrent Neural Network (LSTM): Multi-Room Context |
| title_short | Occupancy State Prediction by Recurrent Neural Network (LSTM): Multi-Room Context |
| title_sort | occupancy state prediction by recurrent neural network lstm multi room context |
| topic | machine learning neural network artificial neural networks long short-term memory occupant behavior building energy consumption |
| url | https://www.mdpi.com/1424-8220/23/23/9603 |
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