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
- Impact of ML Optimization Tactics on Greener Pre-Trained ML Models [46.78148962732881]
This study aims to (i) analyze image classification datasets and pre-trained models, (ii) improve inference efficiency by comparing optimized and non-optimized models, and (iii) assess the economic impact of the optimizations.
We conduct a controlled experiment to evaluate the impact of various PyTorch optimization techniques (dynamic quantization, torch.compile, local pruning, and global pruning) to 42 Hugging Face models for image classification.
Dynamic quantization demonstrates significant reductions in inference time and energy consumption, making it highly suitable for large-scale systems.
arXiv Detail & Related papers (2024-09-19T16:23:03Z) - 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 [60.17407932691429]
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) - Sustainable Edge Intelligence Through Energy-Aware Early Exiting [0.726437825413781]
We propose energy-adaptive dynamic early exiting to enable efficient and accurate inference in an EH edge intelligence system.
Our approach derives an energy-aware EE policy that determines the optimal amount of computational processing on a per-sample basis.
Results show that accuracy and service rate are improved up to 25% and 35%, respectively, in comparison with an energy-agnostic policy.
arXiv Detail & Related papers (2023-05-23T14:17:44Z) - Optimization Algorithms in Smart Grids: A Systematic Literature Review [4.301367153728695]
This paper focuses on novel features and applications of smart grids in domestic and industrial sectors.
Specifically, we focused on Genetic algorithm, Particle Swarm Optimization, and Grey Wolf Optimization.
arXiv Detail & Related papers (2023-01-16T12:31:06Z) - 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) - AI-based Modeling and Data-driven Evaluation for Smart Manufacturing
Processes [56.65379135797867]
We propose a dynamic algorithm for gaining useful insights about semiconductor manufacturing processes.
We elaborate on the utilization of a Genetic Algorithm and Neural Network to propose an intelligent feature selection algorithm.
arXiv Detail & Related papers (2020-08-29T14:57:53Z) - 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.