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Article Dans Une Revue Energy and Buildings Année : 2013

Development of an RDP neural network for building energy consumption fault detection diagnosis

F. Magoules
H. X. Zhao
  • Fonction : Auteur
D. Elizondo
  • Fonction : Auteur

Résumé

Fault detection and diagnosis (FDD) is an important issue in building energy conservation. This paper proposes a new option for solving this problem at the building level by using a recursive deterministic perceptron (RDP) neural network. Results show a higher than 97% level of generalization in all the designed experiments. Based on this high detection ability of RDP model, a new diagnostic architecture is proposed. Our experiments demonstrate that it is able to not only report correct source of faults but also sort sources in the order of degradation likelihood.

Dates et versions

hal-00802024 , version 1 (18-03-2013)

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Citer

F. Magoules, H. X. Zhao, D. Elizondo. Development of an RDP neural network for building energy consumption fault detection diagnosis. Energy and Buildings, 2013, 62, pp.133-138. ⟨10.1016/j.enbuild.2013.02.050⟩. ⟨hal-00802024⟩
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