John A., PhD, is an assistant professor at Galgotias University, Greater Noida, India, and he received his PhD in computer science and engineering from Manonmaniam Sundaranar University, Tirunelveli, India. He has presented papers in various national and international conferences and has published papers in scientific journals.Senthil Kumar Mohan, PhD, is an associate professor in the Department of Software and System Engineering at the School of Information Technology and Engineering, Vellore Institute of Technology, Vellore, India. He received his PhD in engineering and technology from Vellore Institute of Technology, and he has contributed to many research articles in various technical journals and conferences.Sanjeevikumar Padmanaban, PhD, is a faculty member with the Department of Energy Technology, Aalborg University, Esbjerg, Denmark. He has almost ten years of teaching, research and industrial experience and is an associate editor on a number of international scientific refereed journals. He has published more than 300 research papers and has won numerous awards for his research and teaching.Yasir Hamid, PhD, is an assistant professor in the Department of Information Security Engineering Technology at Abu Dhabi Polytechnic. He earned his PhD in 2019 from Pondicherry University in Computer Science and Engineering. Before joining ADPOLY, he was an assistant professor in the Department of Computer Science, Islamic University of Science and Technology, India. He is an editorial board member on many scientific and technical journals.

• List of Contributors xiiiPreface xvAcknowledgements xix  1 Review and Analysis of Machine Learning Based Techniques for Load Forecasting in Smart Grid System 1Shihabudheen KV and Sheik Mohammed S1.1 Introduction 21.2 Forecasting Methodology 41.3 AI-Based Prediction Methods 51.3.1 Single Prediction Methods 51.3.1.1 Linear Regression 51.3.1.2 Artificial Neural Networks (ANN) 71.3.1.3 Support Vector Regression (SVR) 81.3.1.4 Extreme Learning Machine 91.3.1.5 Neuro-Fuzzy Techniques 101.3.1.6 Deep Learning Techniques 111.3.2 Hybrid Prediction Methods 121.3.2.1 Combined AI-Based Prediction Techniques 121.3.2.2 Signal Decomposition Based Prediction Techniques 131.3.2.3 EMD Based Decomposition 141.3.2.4 Wavelet Based Decomposition 141.4 Results and Discussions 151.4.1 Description of Dataset 151.4.2 Performance Analysis of Single Prediction Methods for Load Forecasting 161.4.2.1 Feature Selection 161.4.2.2 Optimal Parameter Selection 171.4.2.3 Prediction Results of Single Prediction Methods 171.4.3 Performance Analysis of Hybrid Prediction Methods for Load Forecasting 171.4.4 Comparative Analysis 211.5 Conclusion 22References 232 Energy Optimized Techniques in Cloud and Fog Computing 27N.M. Balamurugan, TKS Rathish babu, K Maithili and M. Adimoolam2.1 Introduction 282.2 Fog Computing and Its Applications 332.3 Energy Optimization Techniques in Cloud Computing 382.4 Energy Optimization Techniques in Fog Computing 422.5 Summary and Conclusions 44References 453 Energy-Efficient Cloud Computing Techniques for Next Generation: Ways of Establishing and Strategies for Future Developments 49Praveen Mishra, M. Sivaram, M. Arvindhan, A. Daniel and Raju Ranjan3.1 Introduction 503.2 A Layered Model of Cloud Computing 523.2.1 System of Architecture 533.3 Energy and Cloud Computing 543.3.1 Performance of Network 553.3.2 Reliability of Servers 553.3.3 Forward Challenges 553.3.4 Quality of Machinery 563.4 Saving Electricity Prices 563.4.1 Renewable Energy 573.4.2 Cloud Freedom 573.5 Energy-Efficient Cloud Usage 583.6 Energy-Aware Edge OS 583.7 Energy Efficient Edge Computing Based on Machine Learning 593.8 Energy Aware Computing Offloading 613.8.1 Energy Usage Calculation and Simulation 633.9 Comments and Directions for the Future 63References 644 Energy Optimization Using Silicon Dioxide Composite and Analysis of Wire Electrical Discharge Machining Characteristics 67M.S. Kumaravel, N. Alagumurthi and P. Mathiyalagan4.1 Introduction 674.2 Materials and Methods 694.3 Results and Discussion 724.3.1 XRD Analysis 724.3.2 SEM Analysis 734.3.3 Grey Relational Analysis (GRA) 734.3.4 Main Effects Graph 764.3.5 Analysis of Variance (ANOVA) 774.3.6 Confirmatory Test 784.4 Conclusion 80Acknowledgement 80References 805 Optimal Planning of Renewable DG and Reconfiguration of Distribution Network Considering Multiple Objectives Using PSO Technique for Different Scenarios 83Balmukund Kumar and Aashish Kumar Bohre5.1 Introduction 845.2 Literature Review for Recent Development in DG Planning and Network Reconfiguration 845.3 System Performance Parameters and Index 875.4 Proposed Method 885.4.1 Formulation of Multi-Objective Fitness Function 885.4.2 Backward-Forward-Sweep Load Flow Based on BIBC-BCBV Method 895.5 PSO Based Optimization 905.6 Test Systems 925.7 Results and Discussions 925.8 Conclusions 101References 1026 Investigation of Energy Optimization for Spectrum Sensing in Distributed Cooperative IoT Network Using Deep Learning Techniques 107M. Pavithra, R. Rajmohan, T. Ananth Kumar, S. Usharani and P. Manju Bala6.1 Introduction 1086.2 IoT Architecture 1116.3 Cognitive Spectrum Sensing for Distributed Shared Network 1136.4 Intelligent Distributed Sensing 1156.5 Heuristic Search Based Solutions 1176.6 Selecting IoT Nodes Using Framework 1186.7 Training With Reinforcement Learning 1196.8 Model Validation 1206.9 Performance Evaluations 1236.10 Conclusion and Future Work 125References 1267 Road Network Energy Optimization Using IoT and Deep Learning 129N. M. Balamurugan, N. Revathi and R. Gayathri7.1 Introduction 1297.2 Road Network 1327.2.1 Types of Road 1327.2.2 Road Structure Representation 1347.2.3 Intelligent Road Lighting System 1357.3 Road Anomaly Detection 1397.4 Role of IoT in Road Network Energy Optimization 1417.5 Deep Learning of Road Network Traffic 1427.6 Road Safety and Security 1427.7 Conclusion 144References 1448 Energy Optimization in Smart Homes and Buildings 147S. Sathya, G. Karthi, A. Suresh Kumar and S. Prakash8.1 Introduction 1488.2 Study of Energy Management 1508.3 Energy Optimization in Smart Home 1508.3.1 Power Spent in Smart-Building 1538.3.2 Hurdles of Execution in Energy Optimization 1568.3.3 Barriers to Assure SH Technologies 1568.4 Scope and Study Methodology 1578.4.1 Power Cost of SH 1588.5 Conclusion 159References 1599 Machine Learning Based Approach for Energy Management in the Smart City Revolution 161Deepica S., S. Kalavathi, Angelin Blessy J. and D. Maria Manuel Vianny9.1 Introduction 1629.1.1 Smart City: What is the Need? 1629.1.2 Development of Smart City 1639.2 Need for Energy Optimization 1669.3 Methods for Energy Effectiveness in Smart City 1669.3.1 Smart Electricity Grids 1669.3.2 Smart Transportation and Smart Traffic Management 1699.3.3 Natural Ventilation Effect 1729.4 Role of Machine Learning in Smart City Energy Optimization 1739.4.1 Machine Learning: An Overview 1739.5 Machine Learning Applications in Smart City 1759.6 Conclusion 177References 17810 Design of an Energy Efficient IoT System for Poultry Farm Management 181G. Rajakumar, G. Gnana Jenifer, T. Ananth Kumar and T. S. Arun Samuel10.1 Introduction 18210.2 Literature Survey 18310.3 Proposed Methodology 18710.3.1 Monitoring and Control Module 18810.3.2 Monitoring Temperature 18810.3.3 Monitoring Humidity 18910.3.4 Monitoring Air Pollutants 18910.3.5 Artificial Lightning 19010.3.6 Monitoring Water Level 19010.4 Hardware Components 19010.4.1 Arduino UNO 19010.4.2 Temperature Sensor 19010.4.3 Humidity Sensor 19110.4.4 Gas Sensor 19210.4.5 Water Level Sensor 19210.4.6 LDR Sensor 19310.4.7 GSM (Global System for Mobile Communication) Modem 19410.5 Results and Discussion 19510.5.1 Hardware Module 19510.5.2 Monitoring Temperature 19610.5.3 Monitoring Gas Content 19810.5.4 Monitoring Humidity 19810.5.5 Artificial Lighting 19810.5.6 Monitoring Water Level 19810.5.7 Poultry Energy-Efficiency Tips 19910.6 Conclusion 201References 20311 IoT Based Energy Optimization in Smart Farming Using AI 205N. Padmapriya, T. Ananth Kumar, R. Aswini, R. Rajmohan, P. Kanimozhi and M. Pavithra11.1 Introduction 20611.2 IoT in Smart Farming 20811.2.1 Benefits of Using IoT in Agriculture 20811.2.2 The IoT-Based Smart Farming Cycle 20911.3 AI in Smart Farming 21011.3.1 Artificial Intelligence Revolutionises Agriculture 21011.4 Energy Optimization in Smart Farming 21111.4.1 Energy Optimization in Smart Farming Using IoT and AI 21211.5 Experimental Results 21511.5.1 Analysis of Network Throughput 21611.5.2 Analysis of Network Latency 21711.5.3 Analysis of Energy Consumption 21811.5.4 Applications of IoT and AI in Smart Farming 21911.6 Conclusion 220References 22112 Smart Energy Management Techniques in Industries 5.0 225S. Usharani, P. Manju Bala, T. Ananth Kumar, R. Rajmohan and M. Pavithra12.1 Introduction 22612.2 Related Work 22712.3 General Smart Grid Architecture 22912.3.1 Energy Sub-Sectors 23012.3.1.1 Smart Grid: State-of-the-Art Inside Energy Sector 23012.3.2 EV and Power-to-Gas: State-of-the-Art within Biomass and Transport 23112.3.3 Constructing Zero Net Energy (CZNE): State-of-the-Art Inside Field of Buildings 23312.3.4 Manufacturing Industry: State-of-the-Art 23412.3.5 Smart Energy Systems 23512.4 Smart Control of Power 23612.4.1 Smart Control Thermal System 23612.4.2 Smart Control Cross-Sector 23712.5 Subsector Solutions 23812.6 Smart Energy Management Challenges in Smart Factories 23912.7 Smart Energy Management Importance 24012.8 System Design 24112.9 Smart Energy Management for Smart Grids 24112.10 Experimental Results 24712.11 Conclusions 250References 25113 Energy Optimization Techniques in Telemedicine Using Soft Computing 253R. Indrakumari13.1 Introduction 25313.2 Essential Features of Telemedicine 25513.3 Issues Related to Telemedicine Networks 25613.4 Telemedicine Contracts 25713.5 Energy Efficiency: Policy and Technology Issue 25813.5.1 Soft Computing 25813.5.2 Fuzzy Logic 26013.5.3 Artificial Intelligence 26013.5.4 Genetic Algorithms 26313.5.5 Expert System 26313.5.6 Expert System Based on Fuzzy Logic Rules 26413.6 Patient Condition Monitoring 26613.7 Analysis of Physiological Signals and Data Processing 27113.8 M-Health Monitoring System Architecture 27213.9 Conclusions 275References 27614 Healthcare: Energy Optimization Techniques Using IoT and Machine Learning 279G. Vallathan, Senthilkumar Meyyappan and T. Rajani14.1 Introduction 28014.2 Energy Optimization Process 28114.3 Energy Optimization Techniques in Healthcare 28314.3.1 Energy Optimization in Building 28314.3.2 Machine Learning for Energy Optimization 28414.3.3 Reinforcement Learning for Energy Optimization 28614.3.4 Energy Optimization of Sustainable Internet of Things (IoT) 28714.4 Future Direction of Energy Optimizations 28814.5 Conclusion 289References 28915 Case Study of Energy Optimization: Electric Vehicle Energy Consumption Minimization Using Genetic Algorithm 291Pedram Asef15.1 Introduction 29215.2 Vehicle Modelling to Optimisation 29515.2.1 Vehicle Mathematical Modelling 29515.2.2 Vehicle Model Optimisation Process: Applied Genetic Algorithm 29815.2.3 GA Optimisation Results and Discussion 30115.3 Conclusion 305References 305About the Editors 307Index 309

John A., PhD, is an assistant professor at Galgotias University, Greater Noida, India, and he received his PhD in computer science and engineering from Manonmaniam Sundaranar University, Tirunelveli, India. He has presented papers in various national and international conferences and has published papers in scientific journals.Senthil Kumar Mohan, PhD, is an associate professor in the Department of Software and System Engineering at the School of Information Technology and Engineering, Vellore Institute of Technology, Vellore, India. He received his PhD in engineering and technology from Vellore Institute of Technology, and he has contributed to many research articles in various technical journals and conferences.Sanjeevikumar Padmanaban, PhD, is a faculty member with the Department of Energy Technology, Aalborg University, Esbjerg, Denmark. He has almost ten years of teaching, research and industrial experience and is an associate editor on a number of international scientific refereed journals. He has published more than 300 research papers and has won numerous awards for his research and teaching.Yasir Hamid, PhD, is an assistant professor in the Department of Information Security Engineering Technology at Abu Dhabi Polytechnic. He earned his PhD in 2019 from Pondicherry University in Computer Science and Engineering. Before joining ADPOLY, he was an assistant professor in the Department of Computer Science, Islamic University of Science and Technology, India. He is an editorial board member on many scientific and technical journals.

• List of Contributors xiiiPreface xvAcknowledgements xix  1 Review and Analysis of Machine Learning Based Techniques for Load Forecasting in Smart Grid System 1Shihabudheen KV and Sheik Mohammed S1.1 Introduction 21.2 Forecasting Methodology 41.3 AI-Based Prediction Methods 51.3.1 Single Prediction Methods 51.3.1.1 Linear Regression 51.3.1.2 Artificial Neural Networks (ANN) 71.3.1.3 Support Vector Regression (SVR) 81.3.1.4 Extreme Learning Machine 91.3.1.5 Neuro-Fuzzy Techniques 101.3.1.6 Deep Learning Techniques 111.3.2 Hybrid Prediction Methods 121.3.2.1 Combined AI-Based Prediction Techniques 121.3.2.2 Signal Decomposition Based Prediction Techniques 131.3.2.3 EMD Based Decomposition 141.3.2.4 Wavelet Based Decomposition 141.4 Results and Discussions 151.4.1 Description of Dataset 151.4.2 Performance Analysis of Single Prediction Methods for Load Forecasting 161.4.2.1 Feature Selection 161.4.2.2 Optimal Parameter Selection 171.4.2.3 Prediction Results of Single Prediction Methods 171.4.3 Performance Analysis of Hybrid Prediction Methods for Load Forecasting 171.4.4 Comparative Analysis 211.5 Conclusion 22References 232 Energy Optimized Techniques in Cloud and Fog Computing 27N.M. Balamurugan, TKS Rathish babu, K Maithili and M. Adimoolam2.1 Introduction 282.2 Fog Computing and Its Applications 332.3 Energy Optimization Techniques in Cloud Computing 382.4 Energy Optimization Techniques in Fog Computing 422.5 Summary and Conclusions 44References 453 Energy-Efficient Cloud Computing Techniques for Next Generation: Ways of Establishing and Strategies for Future Developments 49Praveen Mishra, M. Sivaram, M. Arvindhan, A. Daniel and Raju Ranjan3.1 Introduction 503.2 A Layered Model of Cloud Computing 523.2.1 System of Architecture 533.3 Energy and Cloud Computing 543.3.1 Performance of Network 553.3.2 Reliability of Servers 553.3.3 Forward Challenges 553.3.4 Quality of Machinery 563.4 Saving Electricity Prices 563.4.1 Renewable Energy 573.4.2 Cloud Freedom 573.5 Energy-Efficient Cloud Usage 583.6 Energy-Aware Edge OS 583.7 Energy Efficient Edge Computing Based on Machine Learning 593.8 Energy Aware Computing Offloading 613.8.1 Energy Usage Calculation and Simulation 633.9 Comments and Directions for the Future 63References 644 Energy Optimization Using Silicon Dioxide Composite and Analysis of Wire Electrical Discharge Machining Characteristics 67M.S. Kumaravel, N. Alagumurthi and P. Mathiyalagan4.1 Introduction 674.2 Materials and Methods 694.3 Results and Discussion 724.3.1 XRD Analysis 724.3.2 SEM Analysis 734.3.3 Grey Relational Analysis (GRA) 734.3.4 Main Effects Graph 764.3.5 Analysis of Variance (ANOVA) 774.3.6 Confirmatory Test 784.4 Conclusion 80Acknowledgement 80References 805 Optimal Planning of Renewable DG and Reconfiguration of Distribution Network Considering Multiple Objectives Using PSO Technique for Different Scenarios 83Balmukund Kumar and Aashish Kumar Bohre5.1 Introduction 845.2 Literature Review for Recent Development in DG Planning and Network Reconfiguration 845.3 System Performance Parameters and Index 875.4 Proposed Method 885.4.1 Formulation of Multi-Objective Fitness Function 885.4.2 Backward-Forward-Sweep Load Flow Based on BIBC-BCBV Method 895.5 PSO Based Optimization 905.6 Test Systems 925.7 Results and Discussions 925.8 Conclusions 101References 1026 Investigation of Energy Optimization for Spectrum Sensing in Distributed Cooperative IoT Network Using Deep Learning Techniques 107M. Pavithra, R. Rajmohan, T. Ananth Kumar, S. Usharani and P. Manju Bala6.1 Introduction 1086.2 IoT Architecture 1116.3 Cognitive Spectrum Sensing for Distributed Shared Network 1136.4 Intelligent Distributed Sensing 1156.5 Heuristic Search Based Solutions 1176.6 Selecting IoT Nodes Using Framework 1186.7 Training With Reinforcement Learning 1196.8 Model Validation 1206.9 Performance Evaluations 1236.10 Conclusion and Future Work 125References 1267 Road Network Energy Optimization Using IoT and Deep Learning 129N. M. Balamurugan, N. Revathi and R. Gayathri7.1 Introduction 1297.2 Road Network 1327.2.1 Types of Road 1327.2.2 Road Structure Representation 1347.2.3 Intelligent Road Lighting System 1357.3 Road Anomaly Detection 1397.4 Role of IoT in Road Network Energy Optimization 1417.5 Deep Learning of Road Network Traffic 1427.6 Road Safety and Security 1427.7 Conclusion 144References 1448 Energy Optimization in Smart Homes and Buildings 147S. Sathya, G. Karthi, A. Suresh Kumar and S. Prakash8.1 Introduction 1488.2 Study of Energy Management 1508.3 Energy Optimization in Smart Home 1508.3.1 Power Spent in Smart-Building 1538.3.2 Hurdles of Execution in Energy Optimization 1568.3.3 Barriers to Assure SH Technologies 1568.4 Scope and Study Methodology 1578.4.1 Power Cost of SH 1588.5 Conclusion 159References 1599 Machine Learning Based Approach for Energy Management in the Smart City Revolution 161Deepica S., S. Kalavathi, Angelin Blessy J. and D. Maria Manuel Vianny9.1 Introduction 1629.1.1 Smart City: What is the Need? 1629.1.2 Development of Smart City 1639.2 Need for Energy Optimization 1669.3 Methods for Energy Effectiveness in Smart City 1669.3.1 Smart Electricity Grids 1669.3.2 Smart Transportation and Smart Traffic Management 1699.3.3 Natural Ventilation Effect 1729.4 Role of Machine Learning in Smart City Energy Optimization 1739.4.1 Machine Learning: An Overview 1739.5 Machine Learning Applications in Smart City 1759.6 Conclusion 177References 17810 Design of an Energy Efficient IoT System for Poultry Farm Management 181G. Rajakumar, G. Gnana Jenifer, T. Ananth Kumar and T. S. Arun Samuel10.1 Introduction 18210.2 Literature Survey 18310.3 Proposed Methodology 18710.3.1 Monitoring and Control Module 18810.3.2 Monitoring Temperature 18810.3.3 Monitoring Humidity 18910.3.4 Monitoring Air Pollutants 18910.3.5 Artificial Lightning 19010.3.6 Monitoring Water Level 19010.4 Hardware Components 19010.4.1 Arduino UNO 19010.4.2 Temperature Sensor 19010.4.3 Humidity Sensor 19110.4.4 Gas Sensor 19210.4.5 Water Level Sensor 19210.4.6 LDR Sensor 19310.4.7 GSM (Global System for Mobile Communication) Modem 19410.5 Results and Discussion 19510.5.1 Hardware Module 19510.5.2 Monitoring Temperature 19610.5.3 Monitoring Gas Content 19810.5.4 Monitoring Humidity 19810.5.5 Artificial Lighting 19810.5.6 Monitoring Water Level 19810.5.7 Poultry Energy-Efficiency Tips 19910.6 Conclusion 201References 20311 IoT Based Energy Optimization in Smart Farming Using AI 205N. Padmapriya, T. Ananth Kumar, R. Aswini, R. Rajmohan, P. Kanimozhi and M. Pavithra11.1 Introduction 20611.2 IoT in Smart Farming 20811.2.1 Benefits of Using IoT in Agriculture 20811.2.2 The IoT-Based Smart Farming Cycle 20911.3 AI in Smart Farming 21011.3.1 Artificial Intelligence Revolutionises Agriculture 21011.4 Energy Optimization in Smart Farming 21111.4.1 Energy Optimization in Smart Farming Using IoT and AI 21211.5 Experimental Results 21511.5.1 Analysis of Network Throughput 21611.5.2 Analysis of Network Latency 21711.5.3 Analysis of Energy Consumption 21811.5.4 Applications of IoT and AI in Smart Farming 21911.6 Conclusion 220References 22112 Smart Energy Management Techniques in Industries 5.0 225S. Usharani, P. Manju Bala, T. Ananth Kumar, R. Rajmohan and M. Pavithra12.1 Introduction 22612.2 Related Work 22712.3 General Smart Grid Architecture 22912.3.1 Energy Sub-Sectors 23012.3.1.1 Smart Grid: State-of-the-Art Inside Energy Sector 23012.3.2 EV and Power-to-Gas: State-of-the-Art within Biomass and Transport 23112.3.3 Constructing Zero Net Energy (CZNE): State-of-the-Art Inside Field of Buildings 23312.3.4 Manufacturing Industry: State-of-the-Art 23412.3.5 Smart Energy Systems 23512.4 Smart Control of Power 23612.4.1 Smart Control Thermal System 23612.4.2 Smart Control Cross-Sector 23712.5 Subsector Solutions 23812.6 Smart Energy Management Challenges in Smart Factories 23912.7 Smart Energy Management Importance 24012.8 System Design 24112.9 Smart Energy Management for Smart Grids 24112.10 Experimental Results 24712.11 Conclusions 250References 25113 Energy Optimization Techniques in Telemedicine Using Soft Computing 253R. Indrakumari13.1 Introduction 25313.2 Essential Features of Telemedicine 25513.3 Issues Related to Telemedicine Networks 25613.4 Telemedicine Contracts 25713.5 Energy Efficiency: Policy and Technology Issue 25813.5.1 Soft Computing 25813.5.2 Fuzzy Logic 26013.5.3 Artificial Intelligence 26013.5.4 Genetic Algorithms 26313.5.5 Expert System 26313.5.6 Expert System Based on Fuzzy Logic Rules 26413.6 Patient Condition Monitoring 26613.7 Analysis of Physiological Signals and Data Processing 27113.8 M-Health Monitoring System Architecture 27213.9 Conclusions 275References 27614 Healthcare: Energy Optimization Techniques Using IoT and Machine Learning 279G. Vallathan, Senthilkumar Meyyappan and T. Rajani14.1 Introduction 28014.2 Energy Optimization Process 28114.3 Energy Optimization Techniques in Healthcare 28314.3.1 Energy Optimization in Building 28314.3.2 Machine Learning for Energy Optimization 28414.3.3 Reinforcement Learning for Energy Optimization 28614.3.4 Energy Optimization of Sustainable Internet of Things (IoT) 28714.4 Future Direction of Energy Optimizations 28814.5 Conclusion 289References 28915 Case Study of Energy Optimization: Electric Vehicle Energy Consumption Minimization Using Genetic Algorithm 291Pedram Asef15.1 Introduction 29215.2 Vehicle Modelling to Optimisation 29515.2.1 Vehicle Mathematical Modelling 29515.2.2 Vehicle Model Optimisation Process: Applied Genetic Algorithm 29815.2.3 GA Optimisation Results and Discussion 30115.3 Conclusion 305References 305About the Editors 307Index 309

John A., PhD, is an assistant professor at Galgotias University, Greater Noida, India, and he received his PhD in computer science and engineering from Manonmaniam Sundaranar University, Tirunelveli, India. He has presented papers in various national and international conferences and has published papers in scientific journals.Senthil Kumar Mohan, PhD, is an associate professor in the Department of Software and System Engineering at the School of Information Technology and Engineering, Vellore Institute of Technology, Vellore, India. He received his PhD in engineering and technology from Vellore Institute of Technology, and he has contributed to many research articles in various technical journals and conferences.Sanjeevikumar Padmanaban, PhD, is a faculty member with the Department of Energy Technology, Aalborg University, Esbjerg, Denmark. He has almost ten years of teaching, research and industrial experience and is an associate editor on a number of international scientific refereed journals. He has published more than 300 research papers and has won numerous awards for his research and teaching.Yasir Hamid, PhD, is an assistant professor in the Department of Information Security Engineering Technology at Abu Dhabi Polytechnic. He earned his PhD in 2019 from Pondicherry University in Computer Science and Engineering. Before joining ADPOLY, he was an assistant professor in the Department of Computer Science, Islamic University of Science and Technology, India. He is an editorial board member on many scientific and technical journals.

• List of Contributors xiiiPreface xvAcknowledgements xix  1 Review and Analysis of Machine Learning Based Techniques for Load Forecasting in Smart Grid System 1Shihabudheen KV and Sheik Mohammed S1.1 Introduction 21.2 Forecasting Methodology 41.3 AI-Based Prediction Methods 51.3.1 Single Prediction Methods 51.3.1.1 Linear Regression 51.3.1.2 Artificial Neural Networks (ANN) 71.3.1.3 Support Vector Regression (SVR) 81.3.1.4 Extreme Learning Machine 91.3.1.5 Neuro-Fuzzy Techniques 101.3.1.6 Deep Learning Techniques 111.3.2 Hybrid Prediction Methods 121.3.2.1 Combined AI-Based Prediction Techniques 121.3.2.2 Signal Decomposition Based Prediction Techniques 131.3.2.3 EMD Based Decomposition 141.3.2.4 Wavelet Based Decomposition 141.4 Results and Discussions 151.4.1 Description of Dataset 151.4.2 Performance Analysis of Single Prediction Methods for Load Forecasting 161.4.2.1 Feature Selection 161.4.2.2 Optimal Parameter Selection 171.4.2.3 Prediction Results of Single Prediction Methods 171.4.3 Performance Analysis of Hybrid Prediction Methods for Load Forecasting 171.4.4 Comparative Analysis 211.5 Conclusion 22References 232 Energy Optimized Techniques in Cloud and Fog Computing 27N.M. Balamurugan, TKS Rathish babu, K Maithili and M. Adimoolam2.1 Introduction 282.2 Fog Computing and Its Applications 332.3 Energy Optimization Techniques in Cloud Computing 382.4 Energy Optimization Techniques in Fog Computing 422.5 Summary and Conclusions 44References 453 Energy-Efficient Cloud Computing Techniques for Next Generation: Ways of Establishing and Strategies for Future Developments 49Praveen Mishra, M. Sivaram, M. Arvindhan, A. Daniel and Raju Ranjan3.1 Introduction 503.2 A Layered Model of Cloud Computing 523.2.1 System of Architecture 533.3 Energy and Cloud Computing 543.3.1 Performance of Network 553.3.2 Reliability of Servers 553.3.3 Forward Challenges 553.3.4 Quality of Machinery 563.4 Saving Electricity Prices 563.4.1 Renewable Energy 573.4.2 Cloud Freedom 573.5 Energy-Efficient Cloud Usage 583.6 Energy-Aware Edge OS 583.7 Energy Efficient Edge Computing Based on Machine Learning 593.8 Energy Aware Computing Offloading 613.8.1 Energy Usage Calculation and Simulation 633.9 Comments and Directions for the Future 63References 644 Energy Optimization Using Silicon Dioxide Composite and Analysis of Wire Electrical Discharge Machining Characteristics 67M.S. Kumaravel, N. Alagumurthi and P. Mathiyalagan4.1 Introduction 674.2 Materials and Methods 694.3 Results and Discussion 724.3.1 XRD Analysis 724.3.2 SEM Analysis 734.3.3 Grey Relational Analysis (GRA) 734.3.4 Main Effects Graph 764.3.5 Analysis of Variance (ANOVA) 774.3.6 Confirmatory Test 784.4 Conclusion 80Acknowledgement 80References 805 Optimal Planning of Renewable DG and Reconfiguration of Distribution Network Considering Multiple Objectives Using PSO Technique for Different Scenarios 83Balmukund Kumar and Aashish Kumar Bohre5.1 Introduction 845.2 Literature Review for Recent Development in DG Planning and Network Reconfiguration 845.3 System Performance Parameters and Index 875.4 Proposed Method 885.4.1 Formulation of Multi-Objective Fitness Function 885.4.2 Backward-Forward-Sweep Load Flow Based on BIBC-BCBV Method 895.5 PSO Based Optimization 905.6 Test Systems 925.7 Results and Discussions 925.8 Conclusions 101References 1026 Investigation of Energy Optimization for Spectrum Sensing in Distributed Cooperative IoT Network Using Deep Learning Techniques 107M. Pavithra, R. Rajmohan, T. Ananth Kumar, S. Usharani and P. Manju Bala6.1 Introduction 1086.2 IoT Architecture 1116.3 Cognitive Spectrum Sensing for Distributed Shared Network 1136.4 Intelligent Distributed Sensing 1156.5 Heuristic Search Based Solutions 1176.6 Selecting IoT Nodes Using Framework 1186.7 Training With Reinforcement Learning 1196.8 Model Validation 1206.9 Performance Evaluations 1236.10 Conclusion and Future Work 125References 1267 Road Network Energy Optimization Using IoT and Deep Learning 129N. M. Balamurugan, N. Revathi and R. Gayathri7.1 Introduction 1297.2 Road Network 1327.2.1 Types of Road 1327.2.2 Road Structure Representation 1347.2.3 Intelligent Road Lighting System 1357.3 Road Anomaly Detection 1397.4 Role of IoT in Road Network Energy Optimization 1417.5 Deep Learning of Road Network Traffic 1427.6 Road Safety and Security 1427.7 Conclusion 144References 1448 Energy Optimization in Smart Homes and Buildings 147S. Sathya, G. Karthi, A. Suresh Kumar and S. Prakash8.1 Introduction 1488.2 Study of Energy Management 1508.3 Energy Optimization in Smart Home 1508.3.1 Power Spent in Smart-Building 1538.3.2 Hurdles of Execution in Energy Optimization 1568.3.3 Barriers to Assure SH Technologies 1568.4 Scope and Study Methodology 1578.4.1 Power Cost of SH 1588.5 Conclusion 159References 1599 Machine Learning Based Approach for Energy Management in the Smart City Revolution 161Deepica S., S. Kalavathi, Angelin Blessy J. and D. Maria Manuel Vianny9.1 Introduction 1629.1.1 Smart City: What is the Need? 1629.1.2 Development of Smart City 1639.2 Need for Energy Optimization 1669.3 Methods for Energy Effectiveness in Smart City 1669.3.1 Smart Electricity Grids 1669.3.2 Smart Transportation and Smart Traffic Management 1699.3.3 Natural Ventilation Effect 1729.4 Role of Machine Learning in Smart City Energy Optimization 1739.4.1 Machine Learning: An Overview 1739.5 Machine Learning Applications in Smart City 1759.6 Conclusion 177References 17810 Design of an Energy Efficient IoT System for Poultry Farm Management 181G. Rajakumar, G. Gnana Jenifer, T. Ananth Kumar and T. S. Arun Samuel10.1 Introduction 18210.2 Literature Survey 18310.3 Proposed Methodology 18710.3.1 Monitoring and Control Module 18810.3.2 Monitoring Temperature 18810.3.3 Monitoring Humidity 18910.3.4 Monitoring Air Pollutants 18910.3.5 Artificial Lightning 19010.3.6 Monitoring Water Level 19010.4 Hardware Components 19010.4.1 Arduino UNO 19010.4.2 Temperature Sensor 19010.4.3 Humidity Sensor 19110.4.4 Gas Sensor 19210.4.5 Water Level Sensor 19210.4.6 LDR Sensor 19310.4.7 GSM (Global System for Mobile Communication) Modem 19410.5 Results and Discussion 19510.5.1 Hardware Module 19510.5.2 Monitoring Temperature 19610.5.3 Monitoring Gas Content 19810.5.4 Monitoring Humidity 19810.5.5 Artificial Lighting 19810.5.6 Monitoring Water Level 19810.5.7 Poultry Energy-Efficiency Tips 19910.6 Conclusion 201References 20311 IoT Based Energy Optimization in Smart Farming Using AI 205N. Padmapriya, T. Ananth Kumar, R. Aswini, R. Rajmohan, P. Kanimozhi and M. Pavithra11.1 Introduction 20611.2 IoT in Smart Farming 20811.2.1 Benefits of Using IoT in Agriculture 20811.2.2 The IoT-Based Smart Farming Cycle 20911.3 AI in Smart Farming 21011.3.1 Artificial Intelligence Revolutionises Agriculture 21011.4 Energy Optimization in Smart Farming 21111.4.1 Energy Optimization in Smart Farming Using IoT and AI 21211.5 Experimental Results 21511.5.1 Analysis of Network Throughput 21611.5.2 Analysis of Network Latency 21711.5.3 Analysis of Energy Consumption 21811.5.4 Applications of IoT and AI in Smart Farming 21911.6 Conclusion 220References 22112 Smart Energy Management Techniques in Industries 5.0 225S. Usharani, P. Manju Bala, T. Ananth Kumar, R. Rajmohan and M. Pavithra12.1 Introduction 22612.2 Related Work 22712.3 General Smart Grid Architecture 22912.3.1 Energy Sub-Sectors 23012.3.1.1 Smart Grid: State-of-the-Art Inside Energy Sector 23012.3.2 EV and Power-to-Gas: State-of-the-Art within Biomass and Transport 23112.3.3 Constructing Zero Net Energy (CZNE): State-of-the-Art Inside Field of Buildings 23312.3.4 Manufacturing Industry: State-of-the-Art 23412.3.5 Smart Energy Systems 23512.4 Smart Control of Power 23612.4.1 Smart Control Thermal System 23612.4.2 Smart Control Cross-Sector 23712.5 Subsector Solutions 23812.6 Smart Energy Management Challenges in Smart Factories 23912.7 Smart Energy Management Importance 24012.8 System Design 24112.9 Smart Energy Management for Smart Grids 24112.10 Experimental Results 24712.11 Conclusions 250References 25113 Energy Optimization Techniques in Telemedicine Using Soft Computing 253R. Indrakumari13.1 Introduction 25313.2 Essential Features of Telemedicine 25513.3 Issues Related to Telemedicine Networks 25613.4 Telemedicine Contracts 25713.5 Energy Efficiency: Policy and Technology Issue 25813.5.1 Soft Computing 25813.5.2 Fuzzy Logic 26013.5.3 Artificial Intelligence 26013.5.4 Genetic Algorithms 26313.5.5 Expert System 26313.5.6 Expert System Based on Fuzzy Logic Rules 26413.6 Patient Condition Monitoring 26613.7 Analysis of Physiological Signals and Data Processing 27113.8 M-Health Monitoring System Architecture 27213.9 Conclusions 275References 27614 Healthcare: Energy Optimization Techniques Using IoT and Machine Learning 279G. Vallathan, Senthilkumar Meyyappan and T. Rajani14.1 Introduction 28014.2 Energy Optimization Process 28114.3 Energy Optimization Techniques in Healthcare 28314.3.1 Energy Optimization in Building 28314.3.2 Machine Learning for Energy Optimization 28414.3.3 Reinforcement Learning for Energy Optimization 28614.3.4 Energy Optimization of Sustainable Internet of Things (IoT) 28714.4 Future Direction of Energy Optimizations 28814.5 Conclusion 289References 28915 Case Study of Energy Optimization: Electric Vehicle Energy Consumption Minimization Using Genetic Algorithm 291Pedram Asef15.1 Introduction 29215.2 Vehicle Modelling to Optimisation 29515.2.1 Vehicle Mathematical Modelling 29515.2.2 Vehicle Model Optimisation Process: Applied Genetic Algorithm 29815.2.3 GA Optimisation Results and Discussion 30115.3 Conclusion 305References 305About the Editors 307Index 309