Skip to main content

Table 1 State-of-the-art: Anomaly detection or outlier rejection

From: Energy forecasting based on predictive data mining techniques in smart energy grids

Author

Problem Targeted

Method applied

Contribution & Perspective

Panapakidis (Panapakidis et al. 2018)

Missing data treatment

Data processing (Clustering phase + completion phase)

− Clustering phase is unsupervised machine learning tool K-means

− Completion phase filling technique application

− Proposed a new methodology for data filling

− Applicable for both complete and partial absence of data

− Presents a novel methodology for missing and incomplete data completion

− Methodology is not dependent on data size, data resolution and amount of missing data

− Incomplete data artificially completed with data entries of high similarity

Daliento (Daliento et al. 2017)

Monitoring & diagnosis of faults in single and multiple PV array strings

Monitoring and diagnosis techniques based data mining i.e. decision tree method, K-nearest neighbour and SVM

− Reviewed methods for fault detection

− Presented reliability issues