A Probabilistic Model to Predict Household Occupancy Profiles for Home Energy Management Applications
Due to the impact of human lifestyle on building energy consumption, the development of occupants' behavior models is crucial for energy-saving purposes. In this regard, occupancy modeling is an effective approach to intend such a purpose. However, the literature reveals that existing occupancy...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2021-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/9367182/ |
| _version_ | 1819139624139751424 |
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| author | Luis Rueda Simon Sansregret Brice Le Lostec Kodjo Agbossou Nilson Henao Sousso Kelouwani |
| author_facet | Luis Rueda Simon Sansregret Brice Le Lostec Kodjo Agbossou Nilson Henao Sousso Kelouwani |
| author_sort | Luis Rueda |
| collection | DOAJ |
| description | Due to the impact of human lifestyle on building energy consumption, the development of occupants' behavior models is crucial for energy-saving purposes. In this regard, occupancy modeling is an effective approach to intend such a purpose. However, the literature reveals that existing occupancy models have limitations related to the representation of occupancy state duration and the integration of occupancy variability among individuals. Accordingly, this paper proposes an explicit differentiated duration probabilistic model to generate realistic daily occupancy profiles in residential buildings. The discrete-time Markov chain theory and the semi-parametric Cox proportional hazards model (Cox regression) are used to predict household occupancy profiles. The proposed model is able to capture occupancy states duration and integrate human behavior variability according to individuals' characteristics. Moreover, a parametric analysis is employed to investigate these characteristics' impact on the model performance and consequently, select the most significant input variables. A validation process is conducted by comparing the model performance with that of previous methods, presented in the literature. For this purpose, the k crossvalidation technique is utilized. Validation results demonstrate that the proposed approach is highly efficient in generating realistic household occupancy profiles. |
| first_indexed | 2024-12-22T11:25:37Z |
| format | Article |
| id | doaj.art-9d1452df2bf147dfb06ac2605c05d38f |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-22T11:25:37Z |
| publishDate | 2021-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-9d1452df2bf147dfb06ac2605c05d38f2022-12-21T18:27:45ZengIEEEIEEE Access2169-35362021-01-019381873820110.1109/ACCESS.2021.30635029367182A Probabilistic Model to Predict Household Occupancy Profiles for Home Energy Management ApplicationsLuis Rueda0https://orcid.org/0000-0002-8452-608XSimon Sansregret1Brice Le Lostec2Kodjo Agbossou3https://orcid.org/0000-0003-1441-424XNilson Henao4https://orcid.org/0000-0002-1286-2869Sousso Kelouwani5https://orcid.org/0000-0002-1163-9147Department of Electrical and Computer Engineering, Hydrogen Research Institute, University of Quebec at Trois-Rivières, Trois-Rivières, QC, CanadaLaboratoire des Technologies de l’Énergie, Institut de Recherche Hydro-Québec, Shawinigan, QC, CanadaLaboratoire des Technologies de l’Énergie, Institut de Recherche Hydro-Québec, Shawinigan, QC, CanadaDepartment of Electrical and Computer Engineering, Hydrogen Research Institute, University of Quebec at Trois-Rivières, Trois-Rivières, QC, CanadaDepartment of Electrical and Computer Engineering, Hydrogen Research Institute, University of Quebec at Trois-Rivières, Trois-Rivières, QC, CanadaDepartment of Mechanical Engineering, Hydrogen Research Institute, University of Quebec at Trois-Rivières, Trois-Rivières, QC, CanadaDue to the impact of human lifestyle on building energy consumption, the development of occupants' behavior models is crucial for energy-saving purposes. In this regard, occupancy modeling is an effective approach to intend such a purpose. However, the literature reveals that existing occupancy models have limitations related to the representation of occupancy state duration and the integration of occupancy variability among individuals. Accordingly, this paper proposes an explicit differentiated duration probabilistic model to generate realistic daily occupancy profiles in residential buildings. The discrete-time Markov chain theory and the semi-parametric Cox proportional hazards model (Cox regression) are used to predict household occupancy profiles. The proposed model is able to capture occupancy states duration and integrate human behavior variability according to individuals' characteristics. Moreover, a parametric analysis is employed to investigate these characteristics' impact on the model performance and consequently, select the most significant input variables. A validation process is conducted by comparing the model performance with that of previous methods, presented in the literature. For this purpose, the k crossvalidation technique is utilized. Validation results demonstrate that the proposed approach is highly efficient in generating realistic household occupancy profiles.https://ieeexplore.ieee.org/document/9367182/Occupancybehaviorsurvival analysishazard ratemarkov-chainCox regression |
| spellingShingle | Luis Rueda Simon Sansregret Brice Le Lostec Kodjo Agbossou Nilson Henao Sousso Kelouwani A Probabilistic Model to Predict Household Occupancy Profiles for Home Energy Management Applications IEEE Access Occupancy behavior survival analysis hazard rate markov-chain Cox regression |
| title | A Probabilistic Model to Predict Household Occupancy Profiles for Home Energy Management Applications |
| title_full | A Probabilistic Model to Predict Household Occupancy Profiles for Home Energy Management Applications |
| title_fullStr | A Probabilistic Model to Predict Household Occupancy Profiles for Home Energy Management Applications |
| title_full_unstemmed | A Probabilistic Model to Predict Household Occupancy Profiles for Home Energy Management Applications |
| title_short | A Probabilistic Model to Predict Household Occupancy Profiles for Home Energy Management Applications |
| title_sort | probabilistic model to predict household occupancy profiles for home energy management applications |
| topic | Occupancy behavior survival analysis hazard rate markov-chain Cox regression |
| url | https://ieeexplore.ieee.org/document/9367182/ |
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