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 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 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 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 9 Machine Learning Based Approach for Energy Management in the Smart City Revolution 161 Deepica S., S. Kalavathi, Angelin Blessy J. and D. Maria Manuel Vianny 9.1 Introduction 162 9.1.1 Smart City: What is the Need? 162 9.1.2 Development of Smart City 163 9.2 Need for Energy Optimization 166 9.3 Methods for Energy Effectiveness in Smart City 166 9.3.1 Smart Electricity Grids 166 9.3.2 Smart Transportation and Smart Traffic Management 169 9.3.3 Natural Ventilation Effect 172 9.4 Role of Machine Learning in Smart City Energy Optimization 173 9.4.1 Machine Learning: An Overview 173 9.5 Machine Learning Applications in Smart City 175 9.6 Conclusion 177 References 178 10 Design of an Energy Efficient IoT System for Poultry Farm Management 181 G. Rajakumar, G. Gnana Jenifer, T. Ananth Kumar and T. S. Arun Samuel 10.1 Introduction 182 10.2 Literature Survey 183 10.3 Proposed Methodology 187 10.3.1 Monitoring and Control Module 188 10.3.2 Monitoring Temperature 188 10.3.3 Monitoring Humidity 189 10.3.4 Monitoring Air Pollutants 189 10.3.5 Artificial Lightning 190 10.3.6 Monitoring Water Level 190 10.4 Hardware Components 190 10.4.1 Arduino UNO 190 10.4.2 Temperature Sensor 190 10.4.3 Humidity Sensor 191 10.4.4 Gas Sensor 192 10.4.5 Water Level Sensor 192 10.4.6 LDR Sensor 193 10.4.7 GSM (Global System for Mobile Communication) Modem 194 10.5 Results and Discussion 195 10.5.1 Hardware Module 195 10.5.2 Monitoring Temperature 196 10.5.3 Monitoring Gas Content 198 10.5.4 Monitoring Humidity 198 10.5.5 Artificial Lighting 198 10.5.6 Monitoring Water Level 198 10.5.7 Poultry Energy-Efficiency Tips 199 10.6 Conclusion 201 References 203 11 IoT Based Energy Optimization in Smart Farming Using AI 205 N. Padmapriya, T. Ananth Kumar, R. Aswini, R. Rajmohan, P. Kanimozhi and M. Pavithra 11.1 Introduction 206 11.2 IoT in Smart Farming 208 11.2.1 Benefits of Using IoT in Agriculture 208 11.2.2 The IoT-Based Smart Farming Cycle 209 11.3 AI in Smart Farming 210 11.3.1 Artificial Intelligence Revolutionises Agriculture 210 11.4 Energy Optimization in Smart Farming 211 11.4.1 Energy Optimization in Smart Farming Using IoT and AI 212 11.5 Experimental Results 215 11.5.1 Analysis of Network Throughput 216 11.5.2 Analysis of Network Latency 217 11.5.3 Analysis of Energy Consumption 218 11.5.4 Applications of IoT and AI in Smart Farming 219 11.6 Conclusion 220 References 221 12 Smart Energy Management Techniques in Industries 5.0 225 S. Usharani, P. Manju Bala, T. Ananth Kumar, R. Rajmohan and M. Pavithra 12.1 Introduction 226 12.2 Related Work 227 12.3 General Smart Grid Architecture 229 12.3.1 Energy Sub-Sectors 230 12.3.1.1 Smart Grid: State-of-the-Art Inside Energy Sector 230 12.3.2 EV and Power-to-Gas: State-of-the-Art within Biomass and Transport 231 12.3.3 Constructing Zero Net Energy (CZNE): State-of-the-Art Inside Field of Buildings 233 12.3.4 Manufacturing Industry: State-of-the-Art 234 12.3.5 Smart Energy Systems 235 12.4 Smart Control of Power 236 12.4.1 Smart Control Thermal System 236 12.4.2 Smart Control Cross-Sector 237 12.5 Subsector Solutions 238 12.6 Smart Energy Management Challenges in Smart Factories 239 12.7 Smart Energy Management Importance 240 12.8 System Design 241 12.9 Smart Energy Management for Smart Grids 241 12.10 Experimental Results 247 12.11 Conclusions 250 References 251 13 Energy Optimization Techniques in Telemedicine Using Soft Computing 253 R. Indrakumari 13.1 Introduction 253 13.2 Essential Features of Telemedicine 255 13.3 Issues Related to Telemedicine Networks 256 13.4 Telemedicine Contracts 257 13.5 Energy Efficiency: Policy and Technology Issue 258 13.5.1 Soft Computing 258 13.5.2 Fuzzy Logic 260 13.5.3 Artificial Intelligence 260 13.5.4 Genetic Algorithms 263 13.5.5 Expert System 263 13.5.6 Expert System Based on Fuzzy Logic Rules 264 13.6 Patient Condition Monitoring 266 13.7 Analysis of Physiological Signals and Data Processing 271 13.8 M-Health Monitoring System Architecture 272 13.9 Conclusions 275 References 276 14 Healthcare: Energy Optimization Techniques Using IoT and Machine Learning 279 G. Vallathan, Senthilkumar Meyyappan and T. Rajani 14.1 Introduction 280 14.2 Energy Optimization Process 281 14.3 Energy Optimization Techniques in Healthcare 283 14.3.1 Energy Optimization in Building 283 14.3.2 Machine Learning for Energy Optimization 284 14.3.3 Reinforcement Learning for Energy Optimization 286 14.3.4 Energy Optimization of Sustainable Internet of Things (IoT) 287 14.4 Future Direction of Energy Optimizations 288 14.5 Conclusion 289 References 289 15 Case Study of Energy Optimization: Electric Vehicle Energy Consumption Minimization Using Genetic Algorithm 291 Pedram Asef 15.1 Introduction 292 15.2 Vehicle Modelling to Optimisation 295 15.2.1 Vehicle Mathematical Modelling 295 15.2.2 Vehicle Model Optimisation Process: Applied Genetic Algorithm 298 15.2.3 GA Optimisation Results and Discussion 301 15.3 Conclusion 305 References 305 About the Editors 307 Index 309

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