Power Plays: Unleashing Machine Learning Magic in Smart Grids

• URL: http://arxiv.org/abs/2410.15423v1
• Date: Sun, 20 Oct 2024 15:39:08 GMT
• Title: Power Plays: Unleashing Machine Learning Magic in Smart Grids
• Authors: Abdur Rashid, Parag Biswas, abdullah al masum, MD Abdullah Al Nasim, Kishor Datta Gupta,
• Abstract summary: 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.
• Score: 0.0
• License:
• Abstract: The integration of machine learning into smart grid systems represents a transformative step in enhancing the efficiency, reliability, and sustainability of modern energy networks. By adding advanced data analytics, these systems can better manage the complexities of renewable energy integration, demand response, and predictive maintenance. 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. This enables more precise load balancing, reduces operational costs, and enhances the resilience of the grid against disturbances. Furthermore, the use of predictive models helps in anticipating equipment failures, thereby improving the reliability of the energy supply. As smart grids continue to evolve, the role of machine learning in managing decentralized energy sources and enabling real-time decision-making will become increasingly critical. However, the deployment of these technologies also raises challenges related to data privacy, security, and the need for robust infrastructure. Addressing these issues in this research authors will focus on realizing the full potential of smart grids, ensuring they meet the growing energy demands while maintaining a focus on sustainability and efficiency using Machine Learning techniques. Furthermore, this research will help determine the smart grid's essentiality with the aid of Machine Learning. Multiple ML algorithms have been integrated along with their pros and cons. The future scope of these algorithms are also integrated.

Related papers

• GAN-GRID: A Novel Generative Attack on Smart Grid Stability Prediction [53.2306792009435]
  We propose GAN-GRID a novel adversarial attack targeting the stability prediction system of a smart grid tailored to real-world constraints. Our findings reveal that an adversary armed solely with the stability model's output, devoid of data or model knowledge, can craft data classified as stable with an Attack Success Rate (ASR) of 0.99.
  arXiv  Detail & Related papers  (2024-05-20T14:43:46Z)
• Empowering Distributed Solutions in Renewable Energy Systems and Grid Optimization [3.8979646385036175]
  Machine learning (ML) advancements play a crucial role in empowering renewable energy sources and improving grid management. The incorporation of big data and ML into smart grids offers several advantages, including heightened energy efficiency. However, challenges like handling large data volumes, ensuring cybersecurity, and obtaining specialized expertise must be addressed.
  arXiv  Detail & Related papers  (2023-10-24T02:45:16Z)
• Distributed Energy Management and Demand Response in Smart Grids: A Multi-Agent Deep Reinforcement Learning Framework [53.97223237572147]
  This paper presents a multi-agent Deep Reinforcement Learning (DRL) framework for autonomous control and integration of renewable energy resources into smart power grid systems. In particular, the proposed framework jointly considers demand response (DR) and distributed energy management (DEM) for residential end-users.
  arXiv  Detail & Related papers  (2022-11-29T01:18:58Z)
• Evaluating Distribution System Reliability with Hyperstructures Graph Convolutional Nets [74.51865676466056]
  We show how graph convolutional networks and hyperstructures representation learning framework can be employed for accurate, reliable, and computationally efficient distribution grid planning. Our numerical experiments show that the proposed Hyper-GCNNs approach yields substantial gains in computational efficiency.
  arXiv  Detail & Related papers  (2022-11-14T01:29:09Z)
• Vision Paper: Causal Inference for Interpretable and Robust Machine Learning in Mobility Analysis [71.2468615993246]
  Building intelligent transportation systems requires an intricate combination of artificial intelligence and mobility analysis. The past few years have seen rapid development in transportation applications using advanced deep neural networks. This vision paper emphasizes research challenges in deep learning-based mobility analysis that require interpretability and robustness.
  arXiv  Detail & Related papers  (2022-10-18T17:28:58Z)
• Robust, Deep, and Reinforcement Learning for Management of Communication and Power Networks [6.09170287691728]
  The present thesis first develops principled methods to make generic machine learning models robust against distributional uncertainties and adversarial data. We then build on this robust framework to design robust semi-supervised learning over graph methods. The second part of this thesis aspires to fully unleash the potential of next-generation wired and wireless networks.
  arXiv  Detail & Related papers  (2022-02-08T05:49:06Z)
• Artificial Intelligence Based Prognostic Maintenance of Renewable Energy Systems: A Review of Techniques, Challenges, and Future Research Directions [3.1123064748686287]
  Data Analytics and Machine Learning (ML) techniques are being used to increase the overall efficiency of these prognostic maintenance systems. This paper provides an overview of the predictive/prognostic maintenance frameworks reported in the literature. Being a key aspect of ML-based solutions, we also discuss some of the commonly used publicly available datasets in the domain.
  arXiv  Detail & Related papers  (2021-04-20T11:41:00Z)
• 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)
• 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)
• Risk-Aware Energy Scheduling for Edge Computing with Microgrid: A Multi-Agent Deep Reinforcement Learning Approach [82.6692222294594]
  We study a risk-aware energy scheduling problem for a microgrid-powered MEC network. We derive the solution by applying a multi-agent deep reinforcement learning (MADRL)-based advantage actor-critic (A3C) algorithm with shared neural networks.
  arXiv  Detail & Related papers  (2020-02-21T02:14:38Z)
• Multi-Agent Meta-Reinforcement Learning for Self-Powered and Sustainable Edge Computing Systems [87.4519172058185]
  An effective energy dispatch mechanism for self-powered wireless networks with edge computing capabilities is studied. A novel multi-agent meta-reinforcement learning (MAMRL) framework is proposed to solve the formulated problem. Experimental results show that the proposed MAMRL model can reduce up to 11% non-renewable energy usage and by 22.4% the energy cost.
  arXiv  Detail & Related papers  (2020-02-20T04:58:07Z)

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.