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ArticlePeer ReviewedOpen AccessPlagiarism Passed
Paper ID:IJSREM46295

To Analyze Energy Consumption on BESCOM and Classify using ML Approach

Stream/Area:

-

Subject:

-

Paper ID:

IJSREM46295

Volume:

9 |

4 |

Apr 2025

Pages:

Publication Frequency:

Monthly (12 issues per year)

DOI
Digital Object Identifier
10.55041/IJSREM46295

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

DMCR

Dr Manjunath C R

MC

Mithun C

KV

K Vivek

KMSVK

K M S Veerendra Kumar

LSC

Lakum Sai Charan

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Abstract

Karnataka, a rapidly developing state in India, faces significant changes in energy demand due to population growth, urbanization, and industrialization. This study analyzes Karnataka’s energy consumption patterns, particularly focusing on the Bangalore Electricity Supply Company Limited (BESCOM) area, and explores the application of Machine Learning (ML) for classification. It examines historical data, projections, and the impact of initiatives like the Gruha Jyothi Scheme, which provides free electricity to eligible households. The research highlights the need for accurate energy forecasting and efficient management to address consumption trends and ensure sustainable energy distribution. ML methods offer promising tools for predicting consumption, identifying patterns, and improving operational efficiency within the energy sector. The Gruha Jyothi scheme offers up to 200 units of free electricity each month for eligible households. II.

KEYWORDS

Energy Consumption BESCOM Classification Gruha Jyothi.

Cite This Article

Dr Manjunath C R, Mithun C, K Vivek, K M S Veerendra Kumar, Lakum Sai Charan, "To Analyze Energy Consumption on BESCOM and Classify using ML Approach," International Journal of Scientific Research in Engineering and Management, vol. 9, no. 4, Apr 2025. https://doi.org/10.55041/IJSREM46295

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Publication Date

4/28/2025

Peer Review Type

Double-Blind Peer Review

Review Rounds: 2

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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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