Identification and characterization of irregular consumptions of load data

Abstract

The historical information of loadings on substation helps in evaluation of size of photovoltaic (PV) generation and energy storages for peak shaving and distribution system upgrade deferral. A method, based on consumption data, is proposed to separate the unusual consumption and to form the clusters of similar regular consumption. The method does optimal partition of the load pattern data into core points and border points, high and less dense regions, respectively. The local outlier factor, which does not require fixed probability distribution of data and statistical measures, ranks the unusual consumptions on only the border points, which are a few percent of the complete data. The suggested method finds the optimal or close to optimal number of clusters of similar shape of load patterns to detect regular peak and valley load demands on different days. Furthermore, identification and characterization of features pertaining to unusual consumptions in load pattern data have been done on border points only. The effectiveness of the proposed method and characterization is tested on two practical distribution systems.

An improved sliding window prediction‐based outlier detection and correction for volatile time‐series

“…Outlier detection using k NN and ISWP is done for the above three cases, and the performance is compared in Figure 3. It is evident from the figure that the application of the k NN‐based approach 10,27 on volatile time‐series like PV generation, results in the detection of a large number of false outliers along with true outliers. This is because the method is incapable of detecting an outlier at a particular time‐instant; instead, it marks many data points near an outlier 3 .…”

Performance Analysis of Different Types of Machine Learning Classifiers for Non-Technical Loss Detection

“…Sharma et al [16] have used the concept of local outlier factor (LOF) in density-based spatial clustering of applications with noise (DBSCAN) clustering algorithm to identify unusual load patterns in two datasets from USA and India. LOF is the ratio of density of a data point to the density of its k-nearest neighbors.…”