Hierarchical Probability Prediction Algorithm for Massive Power Marketing User Load Based on Grid Clustering

Abstract

This article proposes a hierarchical probability prediction algorithm based on grid clustering to address the complexity and high-dimensional data challenges of load forecasting for massive power users. By integrating grid density clustering and electricity consumption characteristic curve analysis, a user power consumption characteristic parameter system is constructed. Combined with typical electricity consumption patterns and behavioral information entropy, a user feature vector is formed to achieve refined user cluster identification. Innovatively design a hierarchical prediction framework, using conditional residual simulation probability prediction model in the aggregated load layer, integrating point prediction and quantile regression, effectively capturing the dynamic impact of multiple factors such as weather and date on load residuals. Based on the actual measurement data of 30000 industrial and commercial users in southern China, the verification shows that compared with traditional unified modeling and individual modeling methods, this algorithm improves the MAPE (Mean Absolute Percentage Error) and QS (Quantile Score) indicators by 18.7% and 22.3% respectively, significantly balancing prediction accuracy and computational complexity, and providing a scalable solution for demand side management of smart grids.