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AuthorAbigail, Copiaco
AuthorHimeur, Yassine
AuthorAmira, Abbes
AuthorMansoor, Wathiq
AuthorFadli, Fodil
AuthorAtalla, Shadi
AuthorSohail, Shahab Saquib
Available date2024-03-17T07:13:41Z
Publication Date2023-01-06
Publication NameEngineering Applications of Artificial Intelligence
Identifierhttp://dx.doi.org/10.1016/j.engappai.2022.105775
CitationCopiaco, A., Himeur, Y., Amira, A., Mansoor, W., Fadli, F., Atalla, S., & Sohail, S. S. (2023). An innovative deep anomaly detection of building energy consumption using energy time-series images. Engineering Applications of Artificial Intelligence, 119, 105775.
ISSN0952-1976
URIhttps://www.sciencedirect.com/science/article/pii/S0952197622007655
URIhttp://hdl.handle.net/10576/53093
AbstractDeep anomaly detection (DAD) is essential in optimizing building energy management. Nonetheless, most existing works concerning this field consider unsupervised learning and involve the analysis of sensor readings through a one-dimensional (1D) energy time series, which limits the options for detecting anomalies within the building’s energy consumption. To the best of the authors’ knowledge, this paper presents the first study that explores using two-dimensional (2D) image representations as features of a supervised deep transfer learning (DTL) approach. Specifically, using 2D image representations allows taking advantage of any underlying data within the feature set, providing more possibilities to encode data and detect pertinent features which may not be considered in standard 1D time-series. Furthermore, the effects of using CNN activations as machine learning (ML) model features are also investigated to combine the advantages of both techniques. Additionally, the concept of layer and hyperparameter variation for the CNN model is also studied, with the objective of reducing the overall time computation and resource requirements of the proposed system. Hence, this makes our approach a candidate for edge-based applications. As per the conducted experiments, the top methodology rests at the F1-scores of 93.63% and 99.89% under simulated and real-world energy datasets, respectively. This involves using grayscale 2D image representations that combine CNN activations extracted from AlexNet and GoogleNet pre-trained models as features to a linear support vector machine (SVM) classifier. Finally, the comparison analysis with the state-of-the-art has shown the superiority of the proposed method in various assessment criteria.
Languageen
PublisherElsevier
SubjectDeep anomaly detection
Building energy consumption
Hyper-parameter variation
Neural network activation
Transfer learning
TitleAn innovative deep anomaly detection of building energy consumption using energy time-series images
TypeArticle
Volume Number119
ESSN1873-6769
dc.accessType Full Text


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