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Une nouvelle méthode de modélisation de capteurs virtuels basée sur l'apprentissage approfondi et son application aux systèmes de chauffage, de ventilation et de conditionnement d'air. 

A Novel Virtual Sensor Modeling Method Based on Deep Learning and Its Application in Heating, Ventilation, and Air-Conditioning System.

Auteurs : WANG D., LI X.

Type d'article : Article de périodique

Résumé

Realizing the dynamic redundancy of sensors is of great significance to ensure the energy saving and normal operation of the heating, ventilation, and air-conditioning (HVAC) system. Building a virtual sensor model is an effective method of redundancy and fault tolerance for hardware sensors. In this paper, a virtual sensor modeling method combining the maximum information coefficient (MIC) and the spatial–temporal attention long short-term memory (STA-LSTM) is proposed, which is named MIC-STALSTM, to achieve the dynamic and nonlinear modeling of the supply and return water temperature at both ends of the chiller. First, MIC can extract the influencing factors highly related to the target variables. Then, the extracted impact factors via MIC are used as the input variables of the STA-LSTM algorithm in order to construct an accurate virtual sensor model. The STA-LSTM algorithm not only makes full use of the LSTM algorithm’s advantages in handling historical data series information, but also achieves adaptive estimation of different input variable feature weights and different hidden layer temporal correlations through the attention mechanism. Finally, the effectiveness and feasibility of the proposed method are verified by establishing two virtual sensors for different temperature variables in the HVAC system.

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Pages : 18 p.

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Détails

  • Titre original : A Novel Virtual Sensor Modeling Method Based on Deep Learning and Its Application in Heating, Ventilation, and Air-Conditioning System.
  • Identifiant de la fiche : 30030197
  • Langues : Anglais
  • Sujet : Technologie
  • Source : Energies - vol. 15 - n. 15
  • Éditeurs : MDPI
  • Date d'édition : 08/2022
  • DOI : http://dx.doi.org/10.3390/en15155743

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