Smart Grid: A Survey of Architectural Elements, Machine Learning and Deep Learning Applications and Future Directions

• URL: http://arxiv.org/abs/2010.08094v1
• Date: Fri, 16 Oct 2020 01:40:24 GMT
• Title: Smart Grid: A Survey of Architectural Elements, Machine Learning and Deep Learning Applications and Future Directions
• Authors: Navod Neranjan Thilakarathne, Mohan Krishna Kagita, Dr. Surekha Lanka, Hussain Ahmad
• Abstract summary: 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.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The Smart grid (SG), generally known as the next-generation power grid emerged as a replacement for ill-suited power systems in the 21st century. It is in-tegrated with advanced communication and computing capabilities, thus it is ex-pected to enhance the reliability and the efficiency of energy distribution with minimum effects. With the massive infrastructure it holds and the underlying communication network in the system, it introduced a large volume of data that demands various techniques for proper analysis and decision making. 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. In addition in terms of machine learning-based data an-alytics, this paper highlights the limitations of the current research and highlights future directions as well.

Related papers

• Power Plays: Unleashing Machine Learning Magic in Smart Grids [0.0]
  Machine learning algorithms analyze vast amounts of data from smart meters, sensors, and other grid components to optimize energy distribution, forecast demand, and detect irregularities that could indicate potential failures. The use of predictive models helps in anticipating equipment failures, thereby improving the reliability of the energy supply. However, the deployment of these technologies also raises challenges related to data privacy, security, and the need for robust infrastructure.
  arXiv  Detail & Related papers  (2024-10-20T15:39:08Z)
• 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)
• Mechanistic Neural Networks for Scientific Machine Learning [58.99592521721158]
  We present Mechanistic Neural Networks, a neural network design for machine learning applications in the sciences. It incorporates a new Mechanistic Block in standard architectures to explicitly learn governing differential equations as representations. Central to our approach is a novel Relaxed Linear Programming solver (NeuRLP) inspired by a technique that reduces solving linear ODEs to solving linear programs.
  arXiv  Detail & Related papers  (2024-02-20T15:23:24Z)
• Federated Learning for Energy Constrained IoT devices: A systematic mapping study [0.0]
  Federated Machine Learning (Fed ML) is a new distributed machine learning technique applied to collaboratively train a global model. Fed ML guarantees its confidentiality, a crucial aspect of network security, which enables it to be used in the context of data-sensitive Internet of Things (IoT) Most IoT devices are particularly energy constrained, which raises the need to optimize the Fed ML process for efficient training tasks and optimized power consumption.
  arXiv  Detail & Related papers  (2023-01-09T23:30:32Z)
• A survey on the development status and application prospects of knowledge graph in smart grids [7.070357628640114]
  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.
  arXiv  Detail & Related papers  (2022-11-02T05:57:05Z)
• Machine learning applications for electricity market agent-based models: A systematic literature review [68.8204255655161]
  Agent-based simulations are used to better understand the dynamics of the electricity market. Agent-based models provide the opportunity to integrate machine learning and artificial intelligence. We review 55 papers published between 2016 and 2021 which focus on machine learning applied to agent-based electricity market models.
  arXiv  Detail & Related papers  (2022-06-05T14:52:26Z)
• Retrieval-Enhanced Machine Learning [110.5237983180089]
  We describe a generic retrieval-enhanced machine learning framework, which includes a number of existing models as special cases. REML challenges information retrieval conventions, presenting opportunities for novel advances in core areas, including optimization. REML research agenda lays a foundation for a new style of information access research and paves a path towards advancing machine learning and artificial intelligence.
  arXiv  Detail & Related papers  (2022-05-02T21:42:45Z)
• Machine Learning Empowered Intelligent Data Center Networking: A Survey [35.55535885962517]
  This paper comprehensively investigates the application of machine learning to data center networking. It covers flow prediction, flow classification, load balancing, resource management, routing optimization, and congestion control. We design a quality assessment criteria called REBEL-3S to impartially measure the strengths and weaknesses of these research works.
  arXiv  Detail & Related papers  (2022-02-28T05:27:22Z)
• Knowledge- and Data-driven Services for Energy Systems using Graph Neural Networks [0.9809636731336702]
  We propose a data- and knowledge-driven probabilistic graphical model for energy systems based on the framework of graph neural networks (GNNs) The model can explicitly factor in domain knowledge, in the form of grid topology or physics constraints, thus resulting in sparser architectures and much smaller parameters dimensionality. Results obtained from a real-world smart-grid demonstration project show how the GNN was used to inform grid congestion predictions and market bidding services.
  arXiv  Detail & Related papers  (2021-03-12T13:00:01Z)
• Towards Self-learning Edge Intelligence in 6G [143.1821636135413]
  Edge intelligence, also called edge-native artificial intelligence (AI), is an emerging technological framework focusing on seamless integration of AI, communication networks, and mobile edge computing. In this article, we identify the key requirements and challenges of edge-native AI in 6G.
  arXiv  Detail & Related papers  (2020-10-01T02:16:40Z)
• Deep Learning for Ultra-Reliable and Low-Latency Communications in 6G Networks [84.2155885234293]
  We first summarize how to apply data-driven supervised deep learning and deep reinforcement learning in URLLC. To address these open problems, we develop a multi-level architecture that enables device intelligence, edge intelligence, and cloud intelligence for URLLC.
  arXiv  Detail & Related papers  (2020-02-22T14:38:11Z)

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.