Call for Book Chapters

Green Machine Learning and Big Data for Smart Grids:

Practices and Applications

 

A book edited by Elsevier Book Series in "Advances in Intelligent Energy Systems"

 

Scope of the Book:

The main goal of this book is to provide the most relevant information on bringing the green machine learning concepts using big data techniques in smart grid applications to academicians, researchers, and for those from industry who wish to know more about the solutions for complex real-time problems. The Green Machine Learning and Big Data for Smart Grids book will concentrate more on the operational aspects of smart grids and certainly the only book that will discuss the combined analytics, algorithmic and operational concerns into a coherent picture of the Smart Grid practices and applications.

This edited book aims to bring together cutting-edge research, innovative theories, practical insights, and emerging trends related to Machine Learning and Big Data for Smart Grids and Renewable Energy. We encourage contributions that offer novel perspectives, address current challenges, and provide valuable contributions to the academic and professional community.

 

Topics of interest include, but are not limited to:

• Introduction to Smart Grids and the Need for Green Solutions
• Overview of Renewable Energy Sources and their Integration in Smart Grids
• Data Preprocessing and Feature Engineering for Smart Grid Data
• Data Analytical Models for Smart Grid and Management
• Virtual Power Plants and Distributed Energy Management
• Grid Integration of Renewable Energy Sources: Challenges and Solutions
• Energy Efficiency and Conservation using Machine Learning
• Analysis and Real-time Implementation of Power Line Disturbances Test in smart grid
• Demand Response Strategies and Machine Learning Applications
• Smart Edge Devices for Electric Grid Computing
• Case Studies and Real-world Applications of Green Machine Learning in Smart Grids
• Challenges, Future Directions and Ethical Considerations of Smart Grid

Important Dates:

Abstract Submission: 15 August 2023

Abstract Acceptance: 30 August 2023

Full Chapter Submission: 30 October 2023

Chapter Acceptance: 15 November 2023

Final Chapter Submission: 30 November 2024

 

Submission:

All submissions must be original and should not have been published or under review elsewhere. Kindly submit your chapter by Email: submitmanuscript365@gmail.com With the subject line "Book Chapter for Green ML and Big Data".

 

Publication:

The book edited will be published by Elsevier Book Series in "Advances in Intelligent Energy Systems" and will be indexed by Scopus and offered to Web of Science and Thomson Reuters. No publication fee applicable for this call for book chapter.

 

Editors of the Book: 

1. Dr. R. Elakkiya, Assistant Professor, Department of Computer Science, Birla Institute of Technology & Science Pilani, Dubai Campus, Dubai International Academic City, Dubai, UAE – 345055 
2. Dr. V. Subramaniyaswamy, Professor, School of Computing, SASTRA Deemed University, Thanjavur, Tamilnadu, India – 613 401
3. Dr. V. Indra Gandhi, Professor, School of Electrical Engineering, VIT , Vellore. Tamilnadu, India – 632 014

All inquiries about submissions should be emailed to elakkiya@dubai.bits-pilani.ac.in

 

Objectives and Scope

This special session aims to examine the revolutionary developments and ramifications of large language models (LLMs) in several research and innovation domains. Recent advances in LLMs, like GPT-3.5, have completely changed how we create, interpret, and comprehend natural language, opening up intriguing new possibilities for research, business, and society in general. This session will examine LLMs' current uses, difficulties, and possible future directions, demonstrating how they might support and improve the sessions featuring reviewed material from the leading conference.

Due to their capacity to produce writing that resembles that of a person, comprehend complicated linguistic patterns, and carry out a variety of tasks related to natural language processing, big language models have attracted much interest in recent years. Massive training data sets and cutting-edge deep learning architectures have allowed LLMs to outperform earlier benchmarks in tasks including sentiment analysis, question-answering, machine translation, and question-answering. Additionally, they have the potential to advance cutting-edge study and innovation in a variety of fields.

Subtopics

The topics include, but are not limited to:

• LLMs for data analysis, information retrieval, and knowledge extraction
• LLMs for natural language interpretation and creation
• LLMs in educational and online learning settings
• LLMs for opinion mining and sentiment analysis
• Virtual assistants and chatbots Using LLMs
• LLMs for content regulation and social media analysis
• LLMs in business and financial applications
• LLMs for user experience and human-computer interaction
• LLMs for humanitarian and social purposes

Paper publications

• Proceedings will be published in Lecture Notes in Networks and Systems, Springer (https://www.springer.com/series/15179)
• Indexed by SCOPUS, INSPEC, WTI Frankfurt eG, zbMATH, SCImago
• Paper maximum length is 10 pages
• Papers must be formatted according to Springer format (Latex/word) available at:https://www.springer.com/de/authors-editors/book-authors-editors/manuscriptpreparation/5636#c3324
• Submission Link: http://www.mirlabs.org/his23/submission.php

 Important Dates

• Dr. R. Elakkiya, Birla Institute of Technology & Science, Pilani (Dubai Campus)
• Dr. Subramaniyaswamy V, SASTRA Deemed University, India
• Dr. Ketan Kotecha, Symbiosis International (Deemed University), India