AI-Driven Optimization in Renewable Energy Systems

Authors

  • Sophie Johnson
  • Kai Carter
  • Cameron Jones

Keywords:

artificial intelligence, renewable energy, optimization, machine learning, sustainability

Abstract

This study investigates the use of artificial intelligence to optimize the efficiency and reliability of renewable energy systems. By integrating machine learning algorithms, the research develops a framework for improving energy production forecasting and adaptive control mechanisms. The findings highlight significant advancements in renewable energy utilization and suggest potential for increased energy sustainability and reduced operational costs.

Author Biographies

Sophie Johnson

Ph.D.
University of Cambridge
The Old Schools, Trinity Ln, Cambridge CB2 1TN, United Kingdom

Kai Carter

Ph.D.
University of Toronto
27 King's College Cir, Toronto, ON M5S, Canada

Cameron Jones

Ph.D.
Lviv Polytechnic National University
Stepan Bandera Street, 12, Lviv, Lviv Oblast, Ukraine, 79013

References

Satyanarayana, D., Rathinam, G., Al Kalbani, A. S., Idries, N. K. S., & Al Azzani, A. (2024, March). A Robot Navigation Method Using Restricted Minimum Spanning Tree. In 2024 10th International Conference on Electrical Engineering, Control and Robotics (EECR) (pp. 155-159). IEEE.

Rahimov, E. R. (2010). BASE PRINCIPAL OF MANAGING OF NETWORK SOFTWARE SECURITY BY VULNERABILITIES DETERMINATION MODEL. Computer Sciences and Telecommunications, (5), 70-74.

Published

2024-12-26

Issue

Section

Articles