Unsupervised Anomaly Detection for Energy Consumption in Time Series using Clustering Approach


Mohd Zebaral Hoque, Jesmeen and Hossen, Md. Jakir and Abd. Aziz, Azlan (2021) Unsupervised Anomaly Detection for Energy Consumption in Time Series using Clustering Approach. Emerging Science Journal, 5 (6). pp. 840-854. ISSN 2610-9182

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Recent years have seen significant growth in the adoption of smart home devices. It involves a Smart Home System for better visualisation and analysis with time series. However, there are a few challenges faced by the system developers, such as data quality or data anomaly issues. These anomalies can be due to technical or non-technical faults. It is essential to detect the non-technical fault as it might incur economic cost. In this study, the main objective is to overcome the challenge of training learning models in the case of an unlabelled dataset. Another important consideration is to train the model to be able to discriminate abnormal consumption from seasonal-based consumption. This paper proposes a system using unsupervised learning for Time-Series data in the smart home environment. Initially, the model collected data from the real-time scenario. Following seasonal-based features are generated from the time-domain, followed by feature reduction technique PCA to 2-dimension data. This data then passed through four known unsupervised learning models and was evaluated using the Excess Mass and Mass-Volume method. The results concluded that LOF tends to outperform in the case of detecting anomalies in electricity consumption. The proposed model was further evaluated by benchmark anomaly dataset, and it was also proved that the system could work with the different fields containing time-series data. The model will cluster data into anomalies and not. The developed anomaly detector will detect all anomalies as soon as possible, triggering real alarms in real-time for time-series data's energy consumption. It has the capability to adapt to changing values automatically.

Item Type: Article
Uncontrolled Keywords: Anomaly Detection, Energy Consumption, Unsupervised Learning, Time-series Data
Subjects: T Technology > TK Electrical engineering. Electronics Nuclear engineering > TK5101-6720 Telecommunication. Including telegraphy, telephone, radio, radar, television
Divisions: Faculty of Engineering and Technology (FET)
Depositing User: Ms Nurul Iqtiani Ahmad
Date Deposited: 19 Jan 2022 08:14
Last Modified: 06 Mar 2023 06:43
URII: http://shdl.mmu.edu.my/id/eprint/9862


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