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Table 4 Proposed frameworks of anomaly detection for electricity consumption

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

Reference Purpose References
Rajasegarar et al. [57] Discover unusual events in real-time ∙ It could be a predictor before anomalies happening.
   ∙ It might identify multilevel anomalies.
Chou and Telaga [58] Assess the energy consumption for discovering potential power ∙ It was designed two schemes to identify energy theft attacks and faulty meters.
  theft ∙ NTLs’ detection precision was improved and false positives were reduced.
   ∙ Technical losses also could be estimated.
Liu et al. [59] Detect electricity theft and discover consumers involved ∙ Energy theft detection model can work for both dependent and independent data.
   ∙ The predictor variables were uncorrelated unless power theft occurs.
Arayaa et al. [60] Describe the patterns of consumers’ electricity consumption ∙ Users were mapped into the 2D plane through PCA for showing data and detecting anomalies.
   ∙ Grid processing technology was used.
Xu et al. [61] More accuracy of energy theft detection ∙ An information fusion method was used to discover energy theft attempts.
   ∙ Data mining techniques were utilized to detect anomalies by non-intrusive load monitoring.
   ∙A practical household load simulator was created to assess diverse techniques.