Revolutionizing Energy Management: The Impact of AI and Machine Learning Technologies

The integration of Artificial Intelligence and Machine Learning technologies is revolutionizing the energy sector by transforming energy optimization, predictive maintenance, and smart grid management. From advanced demand forecasting to dynamic pricing mechanisms, these technologies enable sophisticated control and monitoring of power distribution networks. The implementation of predictive maintenance systems with sensor analytics and anomaly detection frameworks has enhanced equipment reliability and operational efficiency. Smart grid management through AI-driven optimization and edge computing capabilities has improved grid stability and monitoring capabilities. Technical considerations in infrastructure requirements and algorithm selection have led to optimized system performance, while emerging developments in quantum computing and privacy preservation technologies promise further advancements in energy management systems.

Authors

Publication Details

Journal
Journal of Computer Science and Technology Studies
Published
2025-05-03
DOI
https://doi.org/10.32996/jcsts.2025.7.3.37
Primary Topic
Energy Load and Power Forecasting
Type
article
Field-Weighted Citation Impact
0.00
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article

Revolutionizing Energy Management: The Impact of AI and Machine Learning Technologies

Venkat Devarajan
Journal of Computer Science and Technology Studies
Energy Load and Power Forecasting
article

Revolutionizing Energy Management: The Impact of AI and Machine Learning Technologies

Venkat Devarajan
article en

Abstract

The integration of Artificial Intelligence and Machine Learning technologies is revolutionizing the energy sector by transforming energy optimization, predictive maintenance, and smart grid management. From advanced demand forecasting to dynamic pricing mechanisms, these technologies enable sophisticated control and monitoring of power distribution networks. The implementation of predictive maintenance systems with sensor analytics and anomaly detection frameworks has enhanced equipment reliability and operational efficiency. Smart grid management through AI-driven optimization and edge computing capabilities has improved grid stability and monitoring capabilities. Technical considerations in infrastructure requirements and algorithm selection have led to optimized system performance, while emerging developments in quantum computing and privacy preservation technologies promise further advancements in energy management systems.

Journal of Computer Science and Technology StudiesVol. 7(3)
Openalex Percentile: Top 92%
Energy Load and Power Forecasting
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