Incremental Learning of Electricity Smart Meter Data (Quick Reference Book)

BK00663

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This book focuses on ESM data analysis by proposed Cloud for Closeness-based Gaussian Mixture Incremental Clustering Algorithm (Cloud4CGMIC). The proposed system can capture and generate the hidden pattern of consumption based on day-time and night-time, season-wise, and area-specific learning. 

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This book helps us to know about -
The newly implemented incremental clustering algorithm
How incremental learning is achieved via incremental clustering
How knowledge augmentation is achieved via incremental clustering
How to implement distributed incremental clustering algorithm using Microsoft Azure platform
How to analyse electricity smart meter data from varied locations and find hidden patterns, estimate load profiles, forecasts energy requirements and work on carbon emissions too

Who should read the book:
Researchers in energy sectors,
data or business analytics team,
electricity generation and distribution team, and
all those who are interested in electricity energy varied sources data analysis

About The Authors :
Dr. Archana Chaudhari earned PhD in Computer Engineering from Symbiosis International (Deemed University), Pune.
She is currently working in Computer Engineering Department of Dr. D. Y. Patil Institute of Technology, Pimpri, Pune.

Dr. Preeti Mulay is working as Professor in the department of Computer Science with Symbiosis Institute of Technology, Pune, India.
She has authored or co-authored many technical papers in various journals and conferences.
She is recognized as IEEE reviewer and certified Springer Reviewer.

Features

PublisherSakal Prakashan
AuthorDr. Archana Chaudhary, Dr. Preeti Mulay
LanguageEnglish
ISBN9789395139526
BindingPaperback
Pages104
Publication Year2023
Dimensions11 x 8.5

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Incremental Learning of Electricity Smart Meter Data (Quick Reference Book)

Incremental Learning of Electricity Smart Meter Data (Quick Reference Book)

This book focuses on ESM data analysis by proposed Cloud for Closeness-based Gaussian Mixture Incremental Clustering Algorithm (Cloud4CGMIC). The proposed system can capture and generate the hidden pattern of consumption based on day-time and night-time, season-wise, and area-specific learning.