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Table 6 Benefits and weaknesses of machine learning algorithms

From: Anomaly detection for electricity consumption in cloud computing: framework, methods, applications, and challenges

Reference Benefits Weaknesses
Eldali et al. [73] They expose the REFIT dataset with detailed annotation for anomaly electricity consumption. The dataset excludes uncommon anomalous features, which led to an increase in MSE. The application of loss estimation and theft detection is not sufficient.
Cui and Wang [72] The method improves the speed of detection effectively. Changing the acquisition frequency constantly may cause harm to smart meters. The over-fitting challenge brings out a high false positive.
da Silva et al. [74] The classification method detects anomaly detection in early time as for real-time data. The method has lower detecting accuracy. The slight electronic abnormal consumption owns the great impossibility to be detected.
Liu et al. [75] The detection method does not affect the operation of smart home devices. It is difficult to make accurate detection when users are using non-smart devices by this method.