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Traitement des données manquantes pour des capteurs de bâtiments connectés

Abstract : The processing the data from smart building is a way to both optimise their energy management and provide a high level of comfort to the occupants. Because of various possible failures in the data collection process, the information gathered can be incomplete or incorrect. In such case, so-called data imputation methods should be used in order to make possible the data processing. This article reviews methods to deal with missing data and to assess the performance of the imputation. Nine of these methods are applied to the data collected from an indoor environment-monitoring sensor located in an apartment for which the presence status of the occupant is known. The methods are compared based on the imputation quality of the multivariate time series as well as based on the performance of the final classification task, i.e. classifying the occupancy status. For this case study, it turns out that the performance imputation task has little impact on the performance of the final task.
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Contributor : Marie-Lise Pannier Connect in order to contact the contributor
Submitted on : Friday, June 3, 2022 - 2:26:56 PM
Last modification on : Tuesday, June 14, 2022 - 9:43:31 AM


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  • HAL Id : hal-03687624, version 1



Ahmed Es-Sabar, Marie-Lise Pannier, Alain Godon, David Bigaud. Traitement des données manquantes pour des capteurs de bâtiments connectés. Conférence IBPSA France 2022, May 2022, Châlons en Champagne, France. ⟨hal-03687624⟩



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