Research on Feature Extraction of Electricity Demand based on Knowledge Graph and Diversified Data of Power Grid

In the context of smart grids, accurately extracting electricity demand features is crucial for optimizing grid operation and management. This paper proposes a technology for extracting electricity demand features based on knowledge graphs and diversified data. First, we integrate user electricity consumption behavior, equipment operation, meteorological conditions, and other multi-source data to construct a knowledge graph system. Next, through methods such as association rule mining and graph neural networks, we analyze user behavior patterns and extract demand features. Finally, we investigate the dynamic updating mechanism of knowledge graphs to support real-time demand forecasting, fault diagnosis, and personalized services. The results show that this technology can significantly enhance the intelligent management level of the power grid and provide strong support for efficient grid operation and sustainable development.