Improving Anomaly Detection in Smart Grid Big Data with Knowledge Graphs
Yingying Zhao, Haoyu Sun, Naiwang Guo, Yi Wu, Yingjie Tian, Dawei Cheng · 2022
A knowledge graph is an intelligent database that integrates artificial intelligence technology and traditional database. It is a knowledge base that represents concepts, entities and their relationships in the objective world in the form of a graph. It can well reflect the relationship between entities. In the current era of big data, although the accuracy of abnormal value detection of power data is much higher than that of traditional methods, big data is lack of explicability, so it is difficult to trace the origin of abnormal values. Abnormal values caused by errors must also be checked manually on site, which wastes human and material resources. As a knowledge embodiment of "big data + artificial intelligence", it has natural advantages in the integrity and interpretability of behavior description. Therefore, this paper studies and discusses the relevant theories and technologies of knowledge graph construction and anomaly detection algorithm. Aiming at the power database table processing process that will cause errors, this paper constructs the knowledge graph and uses the Neo4j diagram database for storage, so as to visualize the data processing process, clarify the data processing relationship, and facilitate the tracking and traceability of the data. In addition, it also carries out experiments and analysis of different anomaly detection algorithms on the sample power consumption data.