A survey on the development status and application prospects of knowledge graph in smart grids

Abstract

With the advent of the electric power big data era, semantic interoperability and interconnection of power data have received extensive attention. Knowledge graph technology is a new method describing the complex relationships between concepts and entities in the objective world, which is widely concerned because of its robust knowledge inference ability. Especially with the proliferation of measurement devices and exponential growth of electric power data empowers, electric power knowledge graph provides new opportunities to solve the contradictions between the massive power resources and the continuously increasing demands for intelligent applications. In an attempt to fulfil the potential of knowledge graph and deal with the various challenges faced, as well as to obtain insights to achieve business applications of smart grids, this work first presents a holistic study of knowledge-driven intelligent application integration. Specifically, a detailed overview of electric power knowledge mining is provided. Then, the overview of the knowledge graph in smart grids is introduced. Moreover, the architecture of the big knowledge graph platform for smart grids and critical technologies are described. Furthermore, this paper comprehensively elaborates on the application prospects leveraged by knowledge graph oriented to smart grids, power consumer service, decision-making in dispatching, and operation and maintenance of power equipment. Finally, issues and challenges are summarised.

Research on the Semantic Composite Network Recognition Method of Power Distribution DTU Automation Acceptance Virtual Seat System

“…The objects of knowledge extraction are the semi-formatted and non-formatted sentences in the distribution network regulations, historical alarms, and operation logs. The difficulty of extraction is that it is difficult to align the data by using graph mapping to obtain the relationship between power equipment from the circuit diagram topology (semi-formatted data) [11]. And the other difficulty of extraction is that when using traditional machine learning methods such as conditional random fields, support vector machines, and decision trees extracts information from texts (unformatted data) such as distribution network regulations and historical operation information, due to the high degree of discretization of differences part in co-referential vocabulary and the sparse distribution of differences part in coreferential vocabulary in the NLP data set, the accuracy and coverage of knowledge extraction are difficult to meet the requirements of engineering applications [12].…”

“…The core idea of knowledge extraction is using deep neural networks to format the unformatted data in the text in order to distinguish the entities, relationships, attributes and other knowledge elements in the unformatted data [11]. This part is divided into three sub-tasks including named entity recognition, attribute extraction and relationship extraction.…”

Visual Analysis of College Sports Performance Based on Multimodal Knowledge Graph Optimization Neural Network

“…e physical education sector particularly values data analysis of college students' sports performance because the study of sports performance data is one of the critical topics for researchers in the sports sector. However, due to the limitation of professional knowledge, it is not easy to discover the explicit phenomena and tacit knowledge contained in sports performance data with a large data volume [1]. ey need a tool that can visually demonstrate the characteristics of various aspects of the data to help them process it, preferably by presenting it in images.…”

Visualization and Analysis Model of Industrial Economy Status and Development Based on Knowledge Graph and Deep Neural Network

“…Traditional data mining and data analysis methods can obtain information from the data, but how to make the information understandable to the users is another difficulty. Data visualization can present the hidden patterns and features of data in a graphical way, which enables users to understand the information quickly and intuitively in the data and improves people's cognitive and exploratory ability of data [ 1 ]. In the era of Big Data, visualization is no longer limited to scientific research and enterprise application fields, and interactive visual analysis of data and intelligent computing has become the common basis for major social needs, such as smart medical care, smart transportation, digital industry, and other aspects.…”