The Pattern and Visualization Detection of the Load Profile Data Based on Bootstrap Approach

This work presents an in-depth analysis of load profile data utilizing the bootstrap method, emphasizing the importance of data management and visualization in electricity consumption patterns. By exploring load profiles from different substations, this study leverages various techniques to predict and analyse data patterns and visualizations effectively.Using R language tools, the paper navigates through the bootstrap method, illustrating the approach with visualizations of yearly, weekly, and seasonal data distributions across three substations. It brings to light the variations in electricity demand and highlights the efficiency of the bootstrap method in evaluating errors in statistical measurements without relying on traditional assumptions. The research indicates that modern computing capabilities can effectively execute multiple iterations of the bootstrap method, offering valuable insights into statistical parameters, including variance and standard deviation.In addition, this work provides a framework for understanding energy usage trends over various timescales, essential for pinpointing periods of peak demand.