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المؤلفAbigail, Copiaco
المؤلفHimeur, Yassine
المؤلفAmira, Abbes
المؤلفMansoor, Wathiq
المؤلفFadli, Fodil
المؤلفAtalla, Shadi
المؤلفSohail, Shahab Saquib
تاريخ الإتاحة2024-03-17T07:13:41Z
تاريخ النشر2023-01-06
اسم المنشورEngineering Applications of Artificial Intelligence
المعرّفhttp://dx.doi.org/10.1016/j.engappai.2022.105775
الاقتباسCopiaco, 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.
الرقم المعياري الدولي للكتاب0952-1976
معرّف المصادر الموحدhttps://www.sciencedirect.com/science/article/pii/S0952197622007655
معرّف المصادر الموحدhttp://hdl.handle.net/10576/53093
الملخصDeep 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.
اللغةen
الناشرElsevier
الموضوعDeep anomaly detection
Building energy consumption
Hyper-parameter variation
Neural network activation
Transfer learning
العنوانAn innovative deep anomaly detection of building energy consumption using energy time-series images
النوعArticle
رقم المجلد119
ESSN1873-6769
dc.accessType Full Text


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