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. |