A load forecasting method for high-proportion renewable energy integration into the distribution network

Abstract

This invention relates to the field of distribution network load forecasting technology, specifically disclosing a load forecasting method for a high proportion of renewable energy access to the distribution network. The method includes: establishing a comprehensive multi-source data acquisition system using power Internet of Things (IoT) technology; collecting multi-source data; processing the collected multi-source data; constructing a hybrid load forecasting model for the distribution network; outputting initial load forecasting characteristics of the distribution network; collecting quantification parameters of the uncertainty in the distribution network load forecast; obtaining the quantification result of the uncertainty in the distribution network load forecast; and matching the optimized load forecasting result of the distribution network based on the quantification result of the uncertainty in the distribution network load forecast and the initial load forecasting characteristics. This invention solves the problems of existing load forecasting models that may struggle to accurately characterize the complex relationship between these characteristics and the distribution network load, have limited model generalization ability, and insufficient forecasting accuracy. It achieves multi-source collaborative operation, improving the overall operating efficiency and stability of the distribution network.

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