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 |