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

arXiv: https://arxiv.org/abs/2211.00901 · HTML: https://mlsanity.com/paper/2211.00901/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 188 references that are in the map.
- First posted on arXiv 2 Nov 2022.

## Builds on (most cited first, 16 of 16)

- [A Comprehensive Survey on Graph Neural Networks](https://mlsanity.com/paper/1901.00596/) (2019)
- [Knowledge Graphs](https://mlsanity.com/paper/2003.02320/) (2020)
- [Deep Learning on Graphs: A Survey](https://mlsanity.com/paper/1812.04202/) (2018)
- [Adversarial Examples: Attacks and Defenses for Deep Learning](https://mlsanity.com/paper/1712.07107/) (2017)
- [A Review of Relational Machine Learning for Knowledge Graphs](https://mlsanity.com/paper/1503.00759/) (2015)
- [A Comprehensive Survey of Graph Embedding: Problems, Techniques and Applications](https://mlsanity.com/paper/1709.07604/) (2017)
- [Graph Embedding Techniques, Applications, and Performance: A Survey](https://mlsanity.com/paper/1705.02801/) (2017)
- [The More You Know: Using Knowledge Graphs for Image Classification](https://mlsanity.com/paper/1612.04844/) (2016)
- [Image Captioning and Visual Question Answering Based on Attributes and External Knowledge](https://mlsanity.com/paper/1603.02814/) (2016)
- [On-line Building Energy Optimization using Deep Reinforcement Learning](https://mlsanity.com/paper/1707.05878/) (2017)
- [A 128 channel Extreme Learning Machine based Neural Decoder for Brain Machine Interfaces](https://mlsanity.com/paper/1509.07450/) (2015)
- [A Two-Stage Approach for Combined Heat and Power Economic Emission Dispatch: Combining Multi-Objective Optimization with Integrated Decision Making](https://mlsanity.com/paper/1808.05704/) (2018)
- [Querying Knowledge Graphs by Example Entity Tuples](https://mlsanity.com/paper/1311.2100/) (2013)
- [Enhancement of Power Equipment Management Using Knowledge Graph](https://mlsanity.com/paper/1904.12242/) (2019)
- [Resonant Machine Learning Based on Complex Growth Transform Dynamical Systems](https://mlsanity.com/paper/1908.05377/) (2019)
- [Fault diagnosis for three-phase PWM rectifier based on deep feedforward network with transient synthetic features](https://mlsanity.com/paper/2211.00228/) (2022)

## Built on by (most cited first, 2 of 2)

- [Semantics-Empowered Communication: A Tutorial-cum-Survey](https://mlsanity.com/paper/2212.08487/) (2022)
- [Survey on Foundation Models for Prognostics and Health Management in Industrial Cyber-Physical Systems](https://mlsanity.com/paper/2312.06261/) (2023)

## Abstract (arXiv)

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.