AI-Driven Approaches for Optimizing Power Consumption: A Comprehensive Survey

• URL: http://arxiv.org/abs/2406.15732v1
• Date: Sat, 22 Jun 2024 04:42:37 GMT
• Title: AI-Driven Approaches for Optimizing Power Consumption: A Comprehensive Survey
• Authors: Parag Biswas, Abdur Rashid, Angona Biswas, Md Abdullah Al Nasim, Kishor Datta Gupta, Roy George,
• Abstract summary: Reduced environmental effect, lower operating costs, and a stable and sustainable energy supply are the main reasons why power optimization is important. Power optimization and artificial intelligence (AI) integration are essential to changing the way energy is produced, used, and distributed. Real-time monitoring and analysis of power usage trends is made possible by AI-driven algorithms and predictive analytics.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Reduced environmental effect, lower operating costs, and a stable and sustainable energy supply for current and future generations are the main reasons why power optimization is important. Power optimization makes ensuring that energy is used more effectively, cutting down on waste and optimizing the utilization of resources.In today's world, power optimization and artificial intelligence (AI) integration are essential to changing the way energy is produced, used, and distributed. Real-time monitoring and analysis of power usage trends is made possible by AI-driven algorithms and predictive analytics, which enable dynamic modifications to effectively satisfy demand. Efficiency and sustainability are increased when power consumption is optimized in different sectors thanks to the use of intelligent systems. This survey paper comprises an extensive review of the several AI techniques used for power optimization as well as a methodical analysis of the literature for the study of various intelligent system application domains across different disciplines of power consumption.This literature review identifies the performance and outcomes of 17 different research methods by assessing them, and it aims to distill valuable insights into their strengths and limitations. Furthermore, this article outlines future directions in the integration of AI for power consumption optimization.

Related papers

• Advancing Generative Artificial Intelligence and Large Language Models for Demand Side Management with Internet of Electric Vehicles [52.43886862287498]
  This paper explores the integration of large language models (LLMs) into energy management. We propose an innovative solution that enhances LLMs with retrieval-augmented generation for automatic problem formulation, code generation, and customizing optimization. We present a case study to demonstrate the effectiveness of our proposed solution in charging scheduling and optimization for electric vehicles.
  arXiv  Detail & Related papers  (2025-01-26T14:31:03Z)
• An Extensive and Methodical Review of Smart Grids for Sustainable Energy Management-Addressing Challenges with AI, Renewable Energy Integration and Leading-edge Technologies [0.0]
  Authors want to cover a number of topics, including smart grid benefits and components, technical developments, integrating renewable energy sources, using artificial intelligence and data analytics, cybersecurity, and privacy. It is proposed to use AI and data analytics for better performance on the grid, reliability, and energy management.
  arXiv  Detail & Related papers  (2025-01-23T23:59:19Z)
• A Survey on Inference Optimization Techniques for Mixture of Experts Models [50.40325411764262]
  Large-scale Mixture of Experts (MoE) models offer enhanced model capacity and computational efficiency through conditional computation. deploying and running inference on these models presents significant challenges in computational resources, latency, and energy efficiency. This survey analyzes optimization techniques for MoE models across the entire system stack.
  arXiv  Detail & Related papers  (2024-12-18T14:11:15Z)
• Double-Exponential Increases in Inference Energy: The Cost of the Race for Accuracy [3.6731536660959985]
  Deep learning models in computer vision pose increasing concerns about energy consumption and sustainability. We conduct a comprehensive analysis of the inference energy consumption of 1,200 ImageNet classification models. We identify key factors contributing to energy consumption and demonstrate methods to improve energy efficiency.
  arXiv  Detail & Related papers  (2024-12-12T21:44:08Z)
• Synergistic Development of Perovskite Memristors and Algorithms for Robust Analog Computing [53.77822620185878]
  We propose a synergistic methodology to concurrently optimize perovskite memristor fabrication and develop robust analog DNNs. We develop "BayesMulti", a training strategy utilizing BO-guided noise injection to improve the resistance of analog DNNs to memristor imperfections. Our integrated approach enables use of analog computing in much deeper and wider networks, achieving up to 100-fold improvements.
  arXiv  Detail & Related papers  (2024-12-03T19:20:08Z)
• Present and Future of AI in Renewable Energy Domain : A Comprehensive Survey [0.0]
  Artificial intelligence (AI) has become a crucial instrument for streamlining processes in various industries. Nine AI-based strategies are identified here to assist Renewable Energy (RE) in contemporary power systems. This study also addressed three main topics: using AI technology for renewable power generation, utilizing AI for renewable energy forecasting, and optimizing energy systems.
  arXiv  Detail & Related papers  (2024-06-22T04:36:09Z)
• Advanced Intelligent Optimization Algorithms for Multi-Objective Optimal Power Flow in Future Power Systems: A Review [1.450405446885067]
  Review explores the application of intelligent optimization algorithms to Multi-Objective Optimal Power Flow (MOPF) It delves into the challenges posed by the integration of renewables, smart grids, and increasing energy demands. Findings suggest that algorithm selection is contingent on the specific MOPF problem at hand, and hybrid approaches offer significant promise.
  arXiv  Detail & Related papers  (2024-04-14T09:44:08Z)
• Machine Learning Insides OptVerse AI Solver: Design Principles and Applications [74.67495900436728]
  We present a comprehensive study on the integration of machine learning (ML) techniques into Huawei Cloud's OptVerse AI solver. We showcase our methods for generating complex SAT and MILP instances utilizing generative models that mirror multifaceted structures of real-world problem. We detail the incorporation of state-of-the-art parameter tuning algorithms which markedly elevate solver performance.
  arXiv  Detail & Related papers  (2024-01-11T15:02:15Z)
• Adaptive Resource Allocation for Virtualized Base Stations in O-RAN with Online Learning [55.08287089554127]
  Open Radio Access Network systems, with their base stations (vBSs), offer operators the benefits of increased flexibility, reduced costs, vendor diversity, and interoperability. We propose an online learning algorithm that balances the effective throughput and vBS energy consumption, even under unforeseeable and "challenging'' environments. We prove the proposed solutions achieve sub-linear regret, providing zero average optimality gap even in challenging environments.
  arXiv  Detail & Related papers  (2023-09-04T17:30:21Z)
• A Human-on-the-Loop Optimization Autoformalism Approach for Sustainability [27.70596933019959]
  This paper outlines a natural conversational approach to solving personalized energy-related problems using large language models (LLMs) We put forward a strategy that augments an LLM with an optimization solver, enhancing its proficiency in understanding and responding to user specifications and preferences. Our approach pioneers the novel concept of human-guided optimization autoformalism, translating a natural language task specification automatically into an optimization instance.
  arXiv  Detail & Related papers  (2023-08-20T22:42:04Z)
• Multi-Objective Constrained Optimization for Energy Applications via Tree Ensembles [55.23285485923913]
  Energy systems optimization problems are complex due to strongly non-linear system behavior and multiple competing objectives. In some cases, proposed optimal solutions need to obey explicit input constraints related to physical properties or safety-critical operating conditions. This paper proposes a novel data-driven strategy using tree ensembles for constrained multi-objective optimization of black-box problems.
  arXiv  Detail & Related papers  (2021-11-04T20:18:55Z)

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.