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

Parag Biswas, Abdur Rashid, Angona Biswas, Md Abdullah Al Nasim, Kishor Datta Gupta, Roy George

TL;DR

This survey evaluates AI-driven approaches for power optimization across four domains—photovoltaic systems, thermal energy management, smart grids, and industrial automation—by analyzing 17 methods spanning fuzzy logic, reinforcement learning, genetic algorithms, swarm intelligence, machine learning, neural networks, and predictive analytics. It identifies concrete gains in real-time monitoring, fault diagnosis, MPC-based control, and energy-cost reductions, while outlining cross-domain strengths and limitations. Key contributions include a domain-wide taxonomy, synthesis of performance outcomes, and guidance on investment opportunities and future research directions in AI-enabled energy efficiency and grid resilience. The study emphasizes the practical impact of integrating AI with power systems to enhance sustainability and reliability.

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.

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

TL;DR

This survey evaluates AI-driven approaches for power optimization across four domains—photovoltaic systems, thermal energy management, smart grids, and industrial automation—by analyzing 17 methods spanning fuzzy logic, reinforcement learning, genetic algorithms, swarm intelligence, machine learning, neural networks, and predictive analytics. It identifies concrete gains in real-time monitoring, fault diagnosis, MPC-based control, and energy-cost reductions, while outlining cross-domain strengths and limitations. Key contributions include a domain-wide taxonomy, synthesis of performance outcomes, and guidance on investment opportunities and future research directions in AI-enabled energy efficiency and grid resilience. The study emphasizes the practical impact of integrating AI with power systems to enhance sustainability and reliability.

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.
Paper Structure (21 sections, 3 equations, 17 figures)

This paper contains 21 sections, 3 equations, 17 figures.

Table of Contents

  1. Introduction
  2. Motivation
  3. Survey Framework
  4. Application Domains: Intelligent Systems in Diverse Power Consumption Fields
  5. Photovoltaic System
  6. Thermal Energy Management
  7. Smart Grids
  8. Industrial Automation
  9. AI-Methods Used For Power Consumption
  10. Fuzzy Logic Control
  11. Reinforcement Learning
  12. Genetic Algorithms
  13. Swarm Intelligence
  14. Machine learning
  15. Neural Networks
  16. ...and 6 more sections

Figures (17)

  • Figure 1: Power consumption worldwide and use of renewable energy. wang2023ai
  • Figure 2: Sustainable energy supply, intelligent energy use, sophisticated grid analytics, mobile and stationary energy storage, and real-time control and management with AI and data at the core are all features of a typical smart grid architecture. wang2023ai
  • Figure 3: Sustainable energy supply, intelligent energy use, sophisticated grid analytics, mobile and stationary energy storage, and real-time control and management with AI and data at the core are all features of a typical smart grid architecture. heymann2024reviewing
  • Figure 4: Visual depiction of this survey article's structure.
  • Figure 5: The PV sector's significance continues to grow (a) The evolution of PV systems' energy share; (b) The evolution of PV energy investments; and (c) The evolution of PV industry employment. mateo2022applications
  • ...and 12 more figures