Anomalies and major cluster-based grouping of electricity users for improving the forecasting performance of deep learning models

Abstract

Analyzing and understanding the electricity consumption of end users, especially the anomalies (outliers), are vital for the planning, operation, and management of the power grid. It will help separate the group of users with unpredictable consumption behavior and then develop and train specialized deep learning models for power load forecasting or regular and non-regular users. The aim of the current work is to divide electricity customers into numerous groups based on anomalies in consumption behavior and major clusters. Successful separation of such groups of customers will provide us with two advantages. One is the increase in the accuracy of load forecasting of other users or groups of users due to their predictable consumption behavior. The second is the opportunity to develop and train specialized deep learning models for customers with highly unpredictable behaviors. The novelty of the work is the segregation of anomalous electricity users from normal/regular users based on outliers in their past power consumption behavior over a period of 92 days. Results indicate that almost 85 percent of the users in the selected residential community attribute one major cluster in their consumption behavior over a period of 3 months of data (92 days). It is also evident from the results that only a small proportion of customers, i.e., 10 out of 69 customers (15 percent), have either more than one cluster or attribute no cluster (zero clusters), which is highly important and indicates that these are the possible users who cause higher variations in power consumption of the residential community.