Table Of Content

Hybrid Intelligent Approaches for Smart Energy Scrivener Publishing 100 Cummings Center, Suite 541J Beverly, MA 01915-6106 Publishers at Scrivener Martin Scrivener ([email protected]) Phillip Carmical ([email protected]) Hybrid Intelligent Approaches for Smart Energy Practical Applications Edited by John A. Senthil Kumar Mohan Sanjeevikumar Padmanaban and Yasir Hamid This edition first published 2022 by John Wiley & Sons, Inc., 111 River Street, Hoboken, NJ 07030, USA and Scrivener Publishing LLC, 100 Cummings Center, Suite 541J, Beverly, MA 01915, USA © 2022 Scrivener Publishing LLC For more information about Scrivener publications please visit www.scrivenerpublishing.com. All rights reserved. No part of this publication may be reproduced, stored in a retrieval system, or transmitted, in any form or by any means, electronic, mechanical, photocopying, recording, or other- wise, except as permitted by law. Advice on how to obtain permission to reuse material from this title is available at http://www.wiley.com/go/permissions. Wiley Global Headquarters 111 River Street, Hoboken, NJ 07030, USA For details of our global editorial offices, customer services, and more information about Wiley prod- ucts visit us at www.wiley.com. Limit of Liability/Disclaimer of Warranty While the publisher and authors have used their best efforts in preparing this work, they make no rep- resentations or warranties with respect to the accuracy or completeness of the contents of this work and specifically disclaim all warranties, including without limitation any implied warranties of merchant- ability or fitness for a particular purpose. No warranty may be created or extended by sales representa- tives, written sales materials, or promotional statements for this work. The fact that an organization, website, or product is referred to in this work as a citation and/or potential source of further information does not mean that the publisher and authors endorse the information or services the organization, website, or product may provide or recommendations it may make. This work is sold with the understanding that the publisher is not engaged in rendering professional services. The advice and strategies contained herein may not be suitable for your situation. You should consult with a specialist where appropriate. Neither the publisher nor authors shall be liable for any loss of profit or any other commercial damages, including but not limited to special, incidental, consequential, or other damages. Further, readers should be aware that websites listed in this work may have changed or disappeared between when this work was written and when it is read. Library of Congress Cataloging-in-Publication Data ISBN 9781119821243 Cover image: Electrical Tower, Patrick Daxenbichler | Internet of Things, Monthira Yodtiwong | Dreamstime.com Cover design by Kris Hackerott Set in size of 11pt and Minion Pro by Manila Typesetting Company, Makati, Philippines Printed in the USA 10 9 8 7 6 5 4 3 2 1 Contents List of Contributors xiii Preface xv Acknowledgements xix 1 Review and Analysis of Machine Learning Based Techniques for Load Forecasting in Smart Grid System 1 Shihabudheen KV and Sheik Mohammed S 1.1 Introduction 2 1.2 Forecasting Methodology 4 1.3 AI-Based Prediction Methods 5 1.3.1 Single Prediction Methods 5 1.3.1.1 Linear Regression 5 1.3.1.2 Artificial Neural Networks (ANN) 7 1.3.1.3 Support Vector Regression (SVR) 8 1.3.1.4 Extreme Learning Machine 9 1.3.1.5 Neuro-Fuzzy Techniques 10 1.3.1.6 Deep Learning Techniques 11 1.3.2 Hybrid Prediction Methods 12 1.3.2.1 Combined AI-Based Prediction Techniques 12 1.3.2.2 Signal Decomposition Based Prediction Techniques 13 1.3.2.3 EMD Based Decomposition 14 1.3.2.4 Wavelet Based Decomposition 14 1.4 Results and Discussions 15 1.4.1 Description of Dataset 15 1.4.2 Performance Analysis of Single Prediction Methods for Load Forecasting 16 1.4.2.1 Feature Selection 16 1.4.2.2 Optimal Parameter Selection 17 1.4.2.3 Prediction Results of Single Prediction Methods 17 v vi Contents 1.4.3 Performance Analysis of Hybrid Prediction Methods for Load Forecasting 17 1.4.4 Comparative Analysis 21 1.5 Conclusion 22 References 23 2 Energy Optimized Techniques in Cloud and Fog Computing 27 N.M. Balamurugan, TKS Rathish babu, K Maithili and M. Adimoolam 2.1 Introduction 28 2.2 Fog Computing and Its Applications 33 2.3 Energy Optimization Techniques in Cloud Computing 38 2.4 Energy Optimization Techniques in Fog Computing 42 2.5 Summary and Conclusions 44 References 45 3 Energy-Efficient Cloud Computing Techniques for Next Generation: Ways of Establishing and Strategies for Future Developments 49 Praveen Mishra, M. Sivaram, M. Arvindhan, A. Daniel and Raju Ranjan 3.1 Introduction 50 3.2 A Layered Model of Cloud Computing 52 3.2.1 System of Architecture 53 3.3 Energy and Cloud Computing 54 3.3.1 Performance of Network 55 3.3.2 Reliability of Servers 55 3.3.3 Forward Challenges 55 3.3.4 Quality of Machinery 56 3.4 Saving Electricity Prices 56 3.4.1 Renewable Energy 57 3.4.2 Cloud Freedom 57 3.5 Energy-Efficient Cloud Usage 58 3.6 Energy-Aware Edge OS 58 3.7 Energy Efficient Edge Computing Based on Machine Learning 59 3.8 Energy Aware Computing Offloading 61 3.8.1 Energy Usage Calculation and Simulation 63 3.9 Comments and Directions for the Future 63 References 64 Contents vii 4 Energy Optimization Using Silicon Dioxide Composite and Analysis of Wire Electrical Discharge Machining Characteristics 67 M.S. Kumaravel, N. Alagumurthi and P. Mathiyalagan 4.1 Introduction 67 4.2 Materials and Methods 69 4.3 Results and Discussion 72 4.3.1 XRD Analysis 72 4.3.2 SEM Analysis 73 4.3.3 Grey Relational Analysis (GRA) 73 4.3.4 Main Effects Graph 76 4.3.5 Analysis of Variance (ANOVA) 77 4.3.6 Confirmatory Test 78 4.4 Conclusion 80 Acknowledgement 80 References 80 5 Optimal Planning of Renewable DG and Reconfiguration of Distribution Network Considering Multiple Objectives Using PSO Technique for Different Scenarios 83 Balmukund Kumar and Aashish Kumar Bohre 5.1 Introduction 84 5.2 Literature Review for Recent Development in DG Planning and Network Reconfiguration 84 5.3 System Performance Parameters and Index 87 5.4 Proposed Method 88 5.4.1 Formulation of Multi-Objective Fitness Function 88 5.4.2 Backward-Forward-Sweep Load Flow Based on BIBC-BCBV Method 89 5.5 PSO Based Optimization 90 5.6 Test Systems 92 5.7 Results and Discussions 92 5.8 Conclusions 101 References 102 6 Investigation of Energy Optimization for Spectrum Sensing in Distributed Cooperative IoT Network Using Deep Learning Techniques 107 M. Pavithra, R. Rajmohan, T. Ananth Kumar, S. Usharani and P. Manju Bala viii Contents 6.1 Introduction 108 6.2 IoT Architecture 111 6.3 Cognitive Spectrum Sensing for Distributed Shared Network 113 6.4 Intelligent Distributed Sensing 115 6.5 Heuristic Search Based Solutions 117 6.6 Selecting IoT Nodes Using Framework 118 6.7 Training With Reinforcement Learning 119 6.8 Model Validation 120 6.9 Performance Evaluations 123 6.10 Conclusion and Future Work 125 References 126 7 Road Network Energy Optimization Using IoT and Deep Learning 129 N. M. Balamurugan, N. Revathi and R. Gayathri 7.1 Introduction 129 7.2 Road Network 132 7.2.1 Types of Road 132 7.2.2 Road Structure Representation 134 7.2.3 Intelligent Road Lighting System 135 7.3 Road Anomaly Detection 139 7.4 Role of IoT in Road Network Energy Optimization 141 7.5 Deep Learning of Road Network Traffic 142 7.6 Road Safety and Security 142 7.7 Conclusion 144 References 144 8 Energy Optimization in Smart Homes and Buildings 147 S. Sathya, G. Karthi, A. Suresh Kumar and S. Prakash 8.1 Introduction 148 8.2 Study of Energy Management 150 8.3 Energy Optimization in Smart Home 150 8.3.1 Power Spent in Smart-Building 153 8.3.2 Hurdles of Execution in Energy Optimization 156 8.3.3 Barriers to Assure SH Technologies 156 8.4 Scope and Study Methodology 157 8.4.1 Power Cost of SH 158 8.5 Conclusion 159 References 159