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ArticlePeer ReviewedOpen AccessPlagiarism Passed
Paper ID:IJSREM43955

Artificial Intelligence-Driven Optimization for Smart Grid Energy Management and Load Forecasting

Stream/Area:

-

Subject:

-

Paper ID:

IJSREM43955

Volume:

9 |

4 |

Apr 2025

Pages:

Publication Frequency:

Monthly (12 issues per year)

DOI
Digital Object Identifier
10.55041/IJSREM43955

License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.

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Authors

LF

Load Forecasting

DAG

Dr Anand Gondesi

SV

Seerapu Varalakshmi

CRK

Ch Ravi Kumar

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Abstract

– The fast transformation of energy infrastructure has led to the adoption of clever grids, which leverage advanced technology for green energy management and cargo forecasting. synthetic Intelligence (AI)-driven optimization plays a important role in improving the performance of smart grids by means of permitting real-time energy distribution, demand forecasting, and fault detection. This examine explores the combination of AI strategies including gadget gaining knowledge of, deep mastering, and reinforcement studying in optimizing smart grid operations. The outcomes highlight how AI-powered forecasting models enhance strength performance, reduce operational fees, and decrease energy losses. moreover, this study discusses the demanding situations and boundaries of AI-driven optimization in smart grids, inclusive of information privateness concerns and computational complexities. The look at concludes by way of emphasizing the ability of AI in revolutionizing smart grid power management and presenting destiny guidelines for studies on this area.

KEYWORDS

Smart Grid Artificial Intelligence Load Forecasting Energy Management Machine Learning

Cite This Article

Load Forecasting, Dr Anand Gondesi, Seerapu Varalakshmi, Ch Ravi Kumar, "Artificial Intelligence-Driven Optimization for Smart Grid Energy Management and Load Forecasting," International Journal of Scientific Research in Engineering and Management, vol. 9, no. 4, Apr 2025. https://doi.org/10.55041/IJSREM43955

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Publication Date

4/6/2025

Peer Review Type

Double-Blind Peer Review

Review Rounds: 2

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5

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The authors declare no conflict of interest related to this research work.

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