A survey on the development status and application prospects of
knowledge graph in smart grids
- URL: http://arxiv.org/abs/2211.00901v1
- Date: Wed, 2 Nov 2022 05:57:05 GMT
- Title: A survey on the development status and application prospects of
knowledge graph in smart grids
- Authors: Jian Wang, Xi Wang, Chaoqun Ma, Lei Kou
- Abstract summary: Electric power knowledge graph provides opportunities to solve contradictions between the massive power resources and the continuously increasing demands for intelligent applications.
This work first presents a holistic study of knowledge-driven intelligent application integration.
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.
- Score: 7.070357628640114
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- 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.
Related papers
- A Perspective on Foundation Models for the Electric Power Grid [53.02072064670517]
Foundation models (FMs) currently dominate news headlines.
We argue that an FM learning from diverse grid data and topologies could unlock transformative capabilities.
We discuss a power grid FM concept, namely GridFM, based on graph neural networks and show how different downstream tasks benefit.
arXiv Detail & Related papers (2024-07-12T17:09:47Z) - Knowledge Graph Extension by Entity Type Recognition [2.8231106019727195]
We propose a novel knowledge graph extension framework based on entity type recognition.
The framework aims to achieve high-quality knowledge extraction by aligning the schemas and entities across different knowledge graphs.
arXiv Detail & Related papers (2024-05-03T19:55:03Z) - Knowledge Graphs: Opportunities and Challenges [3.868053839556011]
It has become vitally important to organize and represent the enormous volume of knowledge appropriately.
As graph data, knowledge graphs accumulate and convey knowledge of the real world.
This paper focuses on the opportunities and challenges of knowledge graphs.
arXiv Detail & Related papers (2023-03-24T12:10:42Z) - Conditional Attention Networks for Distilling Knowledge Graphs in
Recommendation [74.14009444678031]
We propose Knowledge-aware Conditional Attention Networks (KCAN) to incorporate knowledge graph into a recommender system.
We use a knowledge-aware attention propagation manner to obtain the node representation first, which captures the global semantic similarity on the user-item network and the knowledge graph.
Then, by applying a conditional attention aggregation on the subgraph, we refine the knowledge graph to obtain target-specific node representations.
arXiv Detail & Related papers (2021-11-03T09:40:43Z) - An Intelligent Question Answering System based on Power Knowledge Graph [4.424381928034146]
The article introduces a domain knowledge graph using the graph database and graph computing technologies from massive heterogeneous data in electric power.
It then proposed an IQA system based on the electrical power knowledge graph to extract the intent and constraints of natural interrogation.
The proposed work can also provide a basis for the context-aware intelligent question and answer.
arXiv Detail & Related papers (2021-06-16T17:57:51Z) - Contextualized Knowledge-aware Attentive Neural Network: Enhancing
Answer Selection with Knowledge [77.77684299758494]
We extensively investigate approaches to enhancing the answer selection model with external knowledge from knowledge graph (KG)
First, we present a context-knowledge interaction learning framework, Knowledge-aware Neural Network (KNN), which learns the QA sentence representations by considering a tight interaction with the external knowledge from KG and the textual information.
To handle the diversity and complexity of KG information, we propose a Contextualized Knowledge-aware Attentive Neural Network (CKANN), which improves the knowledge representation learning with structure information via a customized Graph Convolutional Network (GCN) and comprehensively learns context-based and knowledge-based sentence representation via
arXiv Detail & Related papers (2021-04-12T05:52:20Z) - Deep Learning for Intelligent Demand Response and Smart Grids: A
Comprehensive Survey [3.0746367873237]
Deep Learning can be leveraged to learn the patterns from the generated data and predict the demand for electricity and peak hours.
We present the fundamental of DL, smart grids, demand response, and the motivation behind the use of DL.
We review the state-of-the-art applications of DL in smart grids and demand response, including electric load forecasting, state estimation, energy theft detection, energy sharing and trading.
arXiv Detail & Related papers (2021-01-20T08:07:41Z) - KRISP: Integrating Implicit and Symbolic Knowledge for Open-Domain
Knowledge-Based VQA [107.7091094498848]
One of the most challenging question types in VQA is when answering the question requires outside knowledge not present in the image.
In this work we study open-domain knowledge, the setting when the knowledge required to answer a question is not given/annotated, neither at training nor test time.
We tap into two types of knowledge representations and reasoning. First, implicit knowledge which can be learned effectively from unsupervised language pre-training and supervised training data with transformer-based models.
arXiv Detail & Related papers (2020-12-20T20:13:02Z) - Smart Grid: A Survey of Architectural Elements, Machine Learning and
Deep Learning Applications and Future Directions [0.0]
Big data analytics, machine learning (ML), and deep learning (DL) plays a key role when it comes to the analysis of this massive amount of data and generation of valuable insights.
This paper explores and surveys the Smart grid architectural elements, machine learning, and deep learning-based applications and approaches in the context of the Smart grid.
arXiv Detail & Related papers (2020-10-16T01:40:24Z) - A Heterogeneous Graph with Factual, Temporal and Logical Knowledge for
Question Answering Over Dynamic Contexts [81.4757750425247]
We study question answering over a dynamic textual environment.
We develop a graph neural network over the constructed graph, and train the model in an end-to-end manner.
arXiv Detail & Related papers (2020-04-25T04:53:54Z) - A Survey on Knowledge Graphs: Representation, Acquisition and
Applications [89.78089494738002]
We review research topics about 1) knowledge graph representation learning, 2) knowledge acquisition and completion, 3) temporal knowledge graph, and 4) knowledge-aware applications.
For knowledge acquisition, especially knowledge graph completion, embedding methods, path inference, and logical rule reasoning, are reviewed.
We explore several emerging topics, including meta learning, commonsense reasoning, and temporal knowledge graphs.
arXiv Detail & Related papers (2020-02-02T13:17:31Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.