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

TL;DR

This survey reviews AI techniques for power optimization, highlighting their effectiveness in reducing environmental impact and costs, and discusses future research directions in intelligent energy management systems.

Contribution

It provides a comprehensive analysis of 17 AI-based methods for power optimization, assessing their performance, strengths, and limitations across various application domains.

Findings

AI algorithms improve real-time power management.

Power optimization enhances sustainability and cost-efficiency.

The survey identifies promising future research areas.

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.