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Submit a ProposalEnergy |
Hybrid Intelligent Optimization Approaches for Smart Energy
 | A Practical Approach Edited by Senthilkumar Mohan, A. John, Sanjeevikumar Padmanaban, and Yasir Hamid Copyright: 2022 | Status: Published ISBN: 9781119821243 | Hardcover | 313 pages Price: $195 USD  |
One Line Description
Written and edited by a group of experts in the field, this is a comprehensive and up-to-date description of current energy optimization techniques, such as artificial intelligence techniques, machine learning, deep learning, and IoT techniques and their future trends.
Audience
Engineers and scientists across many fields, including petroleum and process engineers, chemical engineers, electrical engineers working with power systems, and students at the university and post-graduate level studying smart cities.
Engineers and scientists across many fields, including petroleum and process engineers, chemical engineers, electrical engineers working with power systems, and students at the university and post-graduate level studying smart cities.
Description
Energy optimization and consumption prediction are necessary to prevent energy waste, schedule energy usage, and reduce the cost. Today, smart computing technologies are slowly replacing the traditional computational methods in energy optimization, consumption, scheduling, and usage. Smart computing is an important core technology in today’s scientific and engineering environment. Smart computation techniques such as artificial intelligence, machine learning, deep learning and Internet of Things (IoT) are the key role players in emerging technologies across different applications, industries, and other areas. These newer, smart computation techniques are incorporated with traditional computation and scheduling methods to reduce power usage in areas such as distributed environment, healthcare, smart cities, agriculture and various functional areas.
The scope of this book is to bridge the gap between traditional power consumption methods and modern consumptions methods using smart computation methods. This book addresses the various limitations, issues and challenges of traditional energy consumption methods and provides solutions for various issues using modern smart computation technologies. These smart technologies play a significant role in power consumption, and they are cheaper compared to traditional technologies. The significant limitations of energy usage and optimizations are rectified using smart computations techniques, and the computation techniques are applied across a wide variety of industries and engineering areas. Valuable as reference for engineers, scientists, students, and other professionals across many areas, this is a must-have for any library.
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Energy optimization and consumption prediction are necessary to prevent energy waste, schedule energy usage, and reduce the cost. Today, smart computing technologies are slowly replacing the traditional computational methods in energy optimization, consumption, scheduling, and usage. Smart computing is an important core technology in today’s scientific and engineering environment. Smart computation techniques such as artificial intelligence, machine learning, deep learning and Internet of Things (IoT) are the key role players in emerging technologies across different applications, industries, and other areas. These newer, smart computation techniques are incorporated with traditional computation and scheduling methods to reduce power usage in areas such as distributed environment, healthcare, smart cities, agriculture and various functional areas.
The scope of this book is to bridge the gap between traditional power consumption methods and modern consumptions methods using smart computation methods. This book addresses the various limitations, issues and challenges of traditional energy consumption methods and provides solutions for various issues using modern smart computation technologies. These smart technologies play a significant role in power consumption, and they are cheaper compared to traditional technologies. The significant limitations of energy usage and optimizations are rectified using smart computations techniques, and the computation techniques are applied across a wide variety of industries and engineering areas. Valuable as reference for engineers, scientists, students, and other professionals across many areas, this is a must-have for any library.
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Author / Editor Details
Senthilkumar 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.
Senthilkumar 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.
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.
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.
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Table of Contents
List of Contributors
Preface
Acknowledgements
1. Review and Analysis of Machine Learning Based Techniques for Load Forecasting in Smart Grid System
Dr. Shihabudheen KV and Dr. Sheik Mohammed S
1.1 Introduction
1.2 Forecasting Methodology
1.3 AI-Based Prediction Methods
1.4 Single Prediction Methods
1.4.1 Linear Regression
1.4.2 Artificial Neural Networks (ANN)
1.4.3 Support Vector Regression (SVR)
1.4.4 Extreme Learning Machine
1.4.5 Neuro-Fuzzy Techniques
1.4.6 Deep Learning Techniques
1.5 Hybrid Prediction Methods
1.5.1 Combined AI-Based Prediction Techniques
1.5.2 Signal Decomposition Based Prediction Techniques
1.5.3 EMD Based Decomposition
1.5.4 Wavelet Based Decomposition
1.6 Results and Discussions
1.6.1 Description of Dataset
1.6.2 Performance Analysis of Single Prediction
Methods for Load Forecasting
1.6.3 Feature Selection
1.6.4 Optimal Parameter Selection
1.6.5 Prediction Results of Single Prediction Methods
1.7 Performance Analysis of Hybrid Prediction Methods for Load Forecasting
1.8 Comparative Analysis
1.9 Conclusion
References
2. Energy Optimized Techniques in Cloud and Fog Computing
N.M. Balamurugan, TKS Rathish babu, K Maithili and M. Adimoolam
2.1 Introduction
2.2 Fog Computing and Its Applications
2.3 Energy Optimization Techniques in Cloud Computing
2.4 Energy Optimization Techniques in Fog Computing
2.5 Summary and Conclusions
References
3. Energy-Efficient Cloud Computing Techniques for Next Generation: Ways of Establishing and Strategies for Future Developments
Praveen Mishra, M. Sivaram, M.Arvindhan, Dr. A. Daniel and Dr. Raju Ranjan
3.1 Introduction
3.2 A Layered Model of Cloud Computing
3.2.1 System of Architecture
3.3 Energy and Cloud Computing
3.3.1 Performance of Network
3.3.2 Reliability of Servers
3.3.3 Forward Challenges
3.3.4 Quality of Machinery
3.4 Saving Electricity Prices
3.4.1 Renewable Energy
3.4.2 Cloud Freedom
3.5 Energy-Efficient Cloud Usage
3.6 Energy-Aware Edge OS
3.7 Energy Efficient Edge Computing Based on Machine Learning
3.8 Energy Aware Computing Offloading
3.8.1 Energy Usage Calculation and Simulation
3.9 Comments and Directions for the Future
References
4. Energy Optimization Using Silicon Dioxide Composite and Analysis of Wire Electrical Discharge Machining Characteristics
Dr. M.S. Kumaravel, Dr. N. Alagumurthi and Dr. Mathiyalagan
4.1 Introduction
4.2 Materials and Methods
4.3 Results and Discussion
4.3.1 XRD Analysis
4.3.2 SEM Analysis
4.3.3 Grey Relational Analysis (GRA)
4.3.4 Main Effects Graph
4.3.5 Analysis of Variance (ANOVA)
4.3.6 Confirmatory Test
4.4 Conclusion
Acknowledgement
Conflict of Interest
References
5. Optimal Planning of Renewable DG and Reconfiguration of Distribution Network Considering Multiple Objectives Using PSO Technique for Different Scenarios
Balmukund Kumar and Aashish Kumar Bohre
5.1 Introduction
5.2 Literature Review for Recent Development in DG Planning and Network Reconfiguration
5.3 System Performance Parameters and Index
5.4 Proposed Method
5.4.1 Formulation of Multi-Objective Fitness Function
5.4.2 Backward-Forward-Sweep Load Flow Based on BIBC-BCBV Method
5.5 PSO Based Optimization
5.5 Test Systems
5.6 Results and Discussions
5.7 Conclusions
References
6. Investigation of Energy Optimization for Spectrum Sensing in Distributed Cooperative IoT Network Using Deep Learning Techniques
M. Pavithra, R. Rajmohan, T. Ananth Kumar, S. Usharani and P. Manju Bala
6.1 Introduction
6.2 IoT Architecture
6.3 Cognitive Spectrum Sensing for Distributed Shared Network
6.4 Intelligent Distributed Sensing
6.5 Heuristic Search Based Solutions
6.6 Selecting IoT Nodes Using Framework
6.7 Training With Reinforcement Learning
6.8 Model Validation
6.9 Performance Evaluations
6.10 Conclusion and Future Work
References
7. Road Network Energy Optimization Using IoT and Deep Learning
N. M. Balamurugan, N. Revathi and R. Gayathri 7.1 Introduction
7.2 Road Network
7.2.1 Types of Road
7.2.2 Road Structure Representation
7.2.3 Intelligent Road Lighting System
7.3 Road Anomaly Detection
7.4 Role of IoT in Road Network Energy Optimization
7.5 Deep Learning of Road Network Traffic
7.6 Road Safety and Security
7.7 Conclusion
References
8. Energy Optimization in Smart Homes and Buildings
S. Sathya, G. Karthi, A. Suresh Kumar and S. Prakash
8.1 Introduction
8.2 Study of Energy Management
8.3 Energy Optimization in Smart Home
8.3.1 Power Spent in Smart-Building
8.3.2 Hurdles of Execution in Energy Optimization
8.3.3 Barriers to Assure SH Technologies
8.4 Scope and Study Methodology
8.4.1 Power Cost of SH
8.5 Conclusion
References
9. Machine Learning Based Approach for Energy Management in the Smart City Revolution
Deepica S., S. Kalavathi, Angelin Blessy J. and D. Maria Manuel Vianny
9.1 Introduction
9.1.1 Smart City: What is the Need?
9.1.2 Development of Smart City
9.2 Need for Energy Optimization
9.3 Methods for Energy Effectiveness in Smart City
9.3.1 Smart Electricity Grids
9.3.2 Smart Transportation and Smart Traffic Management
9.3.3 Natural Ventilation Effect
9.4 Role of Machine Learning in Smart City Energy Optimization
9.4.1 Machine Learning: An Overview
9.5 Machine Learning Applications in Smart City
9.6 Conclusion
References
10. Design of an Energy Efficient IoT System for Poultry Farm Management
G. Rajakumar, G. Gnana Jenifer, T. Ananth Kumar and T. S. Arun Samuel
10.1 Introduction
10.2 Literature Survey
10.3 Proposed Methodology
10.3.1 Monitoring and Control Module
10.3.2 Monitoring Temperature
10.3.3 Monitoring Humidity
10.3.4 Monitoring Air Pollutants
10.3.5 Artificial Lightning
10.3.6 Monitoring Water Level
10.4 Hardware Components
10.4.1 Arduino UNO
10.4.2 Temperature Sensor
10.4.3 Humidity Sensor
10.4.4 Gas Sensor
10.4.5 Water Level Sensor
10.4.6 LDR Sensor
10.4.7 GSM (Global System for Mobile Communication) Modem
10.5 Results and Discussion
10.5.1 Hardware Module
10.5.2 Monitoring Temperature
10.5.3 Monitoring Gas Content
10.5.4 Monitoring Humidity
10.5.5 Artificial Lighting
10.5.6 Monitoring Water Level
10.5.7 Poultry Energy-Efficiency Tips
10.6 Conclusion
References
11. IoT Based Energy Optimization in Smart Farming Using AI
N. Padmapriya, Dr. T. Ananth Kumar, R. Aswini, R. Rajmohan, P. Kanimozhi and M. Pavithra
11.1 Introduction
11.2 IoT in Smart Farming
11.2.1 Benefits of Using IoT in Agriculture
11.2.2 The IoT-Based Smart Farming Cycle
11.3 AI in Smart Farming
11.3.1 Artificial Intelligence Revolutionises Agriculture
11.4 Energy Optimization in Smart Farming
11.4.1 Energy Optimization in Smart Farming Using IoT and AI
11.5 Experimental Results
11.5.1 Analysis of Network Throughput
11.5.2 Analysis of Network Latency
11.5.3 Analysis of Energy Consumption
11.5.4 Applications of IoT and AI in Smart Farming
11.6 Conclusion
References
12. Smart Energy Management Techniques in Industries 5.0
S. Usharani, P. Manju Bala, T. Ananth Kumar, R. Rajmohan and M. Pavithra
12.1 Introduction
12.2 Related Work
12.3 General Smart Grid Architecture
12.3.1 Energy Sub-Sectors
12.3.1.1 Smart Grid: State-of-the-Art Inside Energy Sector
12.3.2 EV and Power-to-Gas: State-of-the-Art within Biomass and Transport
12.3.3 Constructing Zero Net Energy (CZNE): State-of-the-Art Inside Field of Buildings
12.3.4 Manufacturing Industry: State-of-the-Art
12.3.5 Smart Energy Systems
12.4 Smart Control of Power
12.4.1 Smart Control Thermal System
12.4.2 Smart Control Cross-Sector
12.5 Subsector Solutions
12.6 Smart Energy Management Challenges in Smart Factories
12.7 Smart Energy Management Importance
12.8 System Design
12.9 Smart Energy Management for Smart Grids
12.10 Experimental Results
12.11 Conclusions
References
13. Energy Optimization Techniques in Telemedicine Using Soft Computing
R. Indrakumari
13.1 Introduction
13.2 Essential Features of Telemedicine
13.3 Issues Related to Telemedicine Networks
13.4 Telemedicine Contracts
13.5 Energy Efficiency: Policy and Technology Issue
13.5.1 Soft Computing
13.5.2 Fuzzy Logic
13.5.3 Artificial Intelligence
13.5.4 Genetic Algorithms
13.5.5 Expert System
13.5.6 Expert System Based on Fuzzy Logic Rules
13.6 Patient Condition Monitoring
13.7 Analysis of Physiological Signals and Data Processing
13.8 M-Health Monitoring System Architecture
13.9 Conclusions
References
14. Healthcare: Energy Optimization Techniques Using IoT and Machine Learning
G. Vallathan, Senthilkumar Meyyappan and T. Rajani
14.1 Introduction
14.2 Energy Optimization Process
14.3 Energy Optimization Techniques in Healthcare
14.4 Future Direction of Energy Optimizations
14.5 Conclusion
References
15. Case Study of Energy Optimization: Electric Vehicle Energy Consumption Minimization Using Genetic Algorithm
Pedram Asef
15.1 Introduction
15.2 Vehicle Modelling to Optimisation
15.2.1 Vehicle Mathematical Modelling
15.2.2 Vehicle Model Optimisation Process: Applied Genetic Algorithm
15.2.3 GA Optimisation Results and Discussion
15.3 Conclusion
References
About the Editors
Index
Back to Top
List of Contributors
Preface
Acknowledgements
1. Review and Analysis of Machine Learning Based Techniques for Load Forecasting in Smart Grid System
Dr. Shihabudheen KV and Dr. Sheik Mohammed S
1.1 Introduction
1.2 Forecasting Methodology
1.3 AI-Based Prediction Methods
1.4 Single Prediction Methods
1.4.1 Linear Regression
1.4.2 Artificial Neural Networks (ANN)
1.4.3 Support Vector Regression (SVR)
1.4.4 Extreme Learning Machine
1.4.5 Neuro-Fuzzy Techniques
1.4.6 Deep Learning Techniques
1.5 Hybrid Prediction Methods
1.5.1 Combined AI-Based Prediction Techniques
1.5.2 Signal Decomposition Based Prediction Techniques
1.5.3 EMD Based Decomposition
1.5.4 Wavelet Based Decomposition
1.6 Results and Discussions
1.6.1 Description of Dataset
1.6.2 Performance Analysis of Single Prediction
Methods for Load Forecasting
1.6.3 Feature Selection
1.6.4 Optimal Parameter Selection
1.6.5 Prediction Results of Single Prediction Methods
1.7 Performance Analysis of Hybrid Prediction Methods for Load Forecasting
1.8 Comparative Analysis
1.9 Conclusion
References
2. Energy Optimized Techniques in Cloud and Fog Computing
N.M. Balamurugan, TKS Rathish babu, K Maithili and M. Adimoolam
2.1 Introduction
2.2 Fog Computing and Its Applications
2.3 Energy Optimization Techniques in Cloud Computing
2.4 Energy Optimization Techniques in Fog Computing
2.5 Summary and Conclusions
References
3. Energy-Efficient Cloud Computing Techniques for Next Generation: Ways of Establishing and Strategies for Future Developments
Praveen Mishra, M. Sivaram, M.Arvindhan, Dr. A. Daniel and Dr. Raju Ranjan
3.1 Introduction
3.2 A Layered Model of Cloud Computing
3.2.1 System of Architecture
3.3 Energy and Cloud Computing
3.3.1 Performance of Network
3.3.2 Reliability of Servers
3.3.3 Forward Challenges
3.3.4 Quality of Machinery
3.4 Saving Electricity Prices
3.4.1 Renewable Energy
3.4.2 Cloud Freedom
3.5 Energy-Efficient Cloud Usage
3.6 Energy-Aware Edge OS
3.7 Energy Efficient Edge Computing Based on Machine Learning
3.8 Energy Aware Computing Offloading
3.8.1 Energy Usage Calculation and Simulation
3.9 Comments and Directions for the Future
References
4. Energy Optimization Using Silicon Dioxide Composite and Analysis of Wire Electrical Discharge Machining Characteristics
Dr. M.S. Kumaravel, Dr. N. Alagumurthi and Dr. Mathiyalagan
4.1 Introduction
4.2 Materials and Methods
4.3 Results and Discussion
4.3.1 XRD Analysis
4.3.2 SEM Analysis
4.3.3 Grey Relational Analysis (GRA)
4.3.4 Main Effects Graph
4.3.5 Analysis of Variance (ANOVA)
4.3.6 Confirmatory Test
4.4 Conclusion
Acknowledgement
Conflict of Interest
References
5. Optimal Planning of Renewable DG and Reconfiguration of Distribution Network Considering Multiple Objectives Using PSO Technique for Different Scenarios
Balmukund Kumar and Aashish Kumar Bohre
5.1 Introduction
5.2 Literature Review for Recent Development in DG Planning and Network Reconfiguration
5.3 System Performance Parameters and Index
5.4 Proposed Method
5.4.1 Formulation of Multi-Objective Fitness Function
5.4.2 Backward-Forward-Sweep Load Flow Based on BIBC-BCBV Method
5.5 PSO Based Optimization
5.5 Test Systems
5.6 Results and Discussions
5.7 Conclusions
References
6. Investigation of Energy Optimization for Spectrum Sensing in Distributed Cooperative IoT Network Using Deep Learning Techniques
M. Pavithra, R. Rajmohan, T. Ananth Kumar, S. Usharani and P. Manju Bala
6.1 Introduction
6.2 IoT Architecture
6.3 Cognitive Spectrum Sensing for Distributed Shared Network
6.4 Intelligent Distributed Sensing
6.5 Heuristic Search Based Solutions
6.6 Selecting IoT Nodes Using Framework
6.7 Training With Reinforcement Learning
6.8 Model Validation
6.9 Performance Evaluations
6.10 Conclusion and Future Work
References
7. Road Network Energy Optimization Using IoT and Deep Learning
N. M. Balamurugan, N. Revathi and R. Gayathri 7.1 Introduction
7.2 Road Network
7.2.1 Types of Road
7.2.2 Road Structure Representation
7.2.3 Intelligent Road Lighting System
7.3 Road Anomaly Detection
7.4 Role of IoT in Road Network Energy Optimization
7.5 Deep Learning of Road Network Traffic
7.6 Road Safety and Security
7.7 Conclusion
References
8. Energy Optimization in Smart Homes and Buildings
S. Sathya, G. Karthi, A. Suresh Kumar and S. Prakash
8.1 Introduction
8.2 Study of Energy Management
8.3 Energy Optimization in Smart Home
8.3.1 Power Spent in Smart-Building
8.3.2 Hurdles of Execution in Energy Optimization
8.3.3 Barriers to Assure SH Technologies
8.4 Scope and Study Methodology
8.4.1 Power Cost of SH
8.5 Conclusion
References
9. Machine Learning Based Approach for Energy Management in the Smart City Revolution
Deepica S., S. Kalavathi, Angelin Blessy J. and D. Maria Manuel Vianny
9.1 Introduction
9.1.1 Smart City: What is the Need?
9.1.2 Development of Smart City
9.2 Need for Energy Optimization
9.3 Methods for Energy Effectiveness in Smart City
9.3.1 Smart Electricity Grids
9.3.2 Smart Transportation and Smart Traffic Management
9.3.3 Natural Ventilation Effect
9.4 Role of Machine Learning in Smart City Energy Optimization
9.4.1 Machine Learning: An Overview
9.5 Machine Learning Applications in Smart City
9.6 Conclusion
References
10. Design of an Energy Efficient IoT System for Poultry Farm Management
G. Rajakumar, G. Gnana Jenifer, T. Ananth Kumar and T. S. Arun Samuel
10.1 Introduction
10.2 Literature Survey
10.3 Proposed Methodology
10.3.1 Monitoring and Control Module
10.3.2 Monitoring Temperature
10.3.3 Monitoring Humidity
10.3.4 Monitoring Air Pollutants
10.3.5 Artificial Lightning
10.3.6 Monitoring Water Level
10.4 Hardware Components
10.4.1 Arduino UNO
10.4.2 Temperature Sensor
10.4.3 Humidity Sensor
10.4.4 Gas Sensor
10.4.5 Water Level Sensor
10.4.6 LDR Sensor
10.4.7 GSM (Global System for Mobile Communication) Modem
10.5 Results and Discussion
10.5.1 Hardware Module
10.5.2 Monitoring Temperature
10.5.3 Monitoring Gas Content
10.5.4 Monitoring Humidity
10.5.5 Artificial Lighting
10.5.6 Monitoring Water Level
10.5.7 Poultry Energy-Efficiency Tips
10.6 Conclusion
References
11. IoT Based Energy Optimization in Smart Farming Using AI
N. Padmapriya, Dr. T. Ananth Kumar, R. Aswini, R. Rajmohan, P. Kanimozhi and M. Pavithra
11.1 Introduction
11.2 IoT in Smart Farming
11.2.1 Benefits of Using IoT in Agriculture
11.2.2 The IoT-Based Smart Farming Cycle
11.3 AI in Smart Farming
11.3.1 Artificial Intelligence Revolutionises Agriculture
11.4 Energy Optimization in Smart Farming
11.4.1 Energy Optimization in Smart Farming Using IoT and AI
11.5 Experimental Results
11.5.1 Analysis of Network Throughput
11.5.2 Analysis of Network Latency
11.5.3 Analysis of Energy Consumption
11.5.4 Applications of IoT and AI in Smart Farming
11.6 Conclusion
References
12. Smart Energy Management Techniques in Industries 5.0
S. Usharani, P. Manju Bala, T. Ananth Kumar, R. Rajmohan and M. Pavithra
12.1 Introduction
12.2 Related Work
12.3 General Smart Grid Architecture
12.3.1 Energy Sub-Sectors
12.3.1.1 Smart Grid: State-of-the-Art Inside Energy Sector
12.3.2 EV and Power-to-Gas: State-of-the-Art within Biomass and Transport
12.3.3 Constructing Zero Net Energy (CZNE): State-of-the-Art Inside Field of Buildings
12.3.4 Manufacturing Industry: State-of-the-Art
12.3.5 Smart Energy Systems
12.4 Smart Control of Power
12.4.1 Smart Control Thermal System
12.4.2 Smart Control Cross-Sector
12.5 Subsector Solutions
12.6 Smart Energy Management Challenges in Smart Factories
12.7 Smart Energy Management Importance
12.8 System Design
12.9 Smart Energy Management for Smart Grids
12.10 Experimental Results
12.11 Conclusions
References
13. Energy Optimization Techniques in Telemedicine Using Soft Computing
R. Indrakumari
13.1 Introduction
13.2 Essential Features of Telemedicine
13.3 Issues Related to Telemedicine Networks
13.4 Telemedicine Contracts
13.5 Energy Efficiency: Policy and Technology Issue
13.5.1 Soft Computing
13.5.2 Fuzzy Logic
13.5.3 Artificial Intelligence
13.5.4 Genetic Algorithms
13.5.5 Expert System
13.5.6 Expert System Based on Fuzzy Logic Rules
13.6 Patient Condition Monitoring
13.7 Analysis of Physiological Signals and Data Processing
13.8 M-Health Monitoring System Architecture
13.9 Conclusions
References
14. Healthcare: Energy Optimization Techniques Using IoT and Machine Learning
G. Vallathan, Senthilkumar Meyyappan and T. Rajani
14.1 Introduction
14.2 Energy Optimization Process
14.3 Energy Optimization Techniques in Healthcare
14.4 Future Direction of Energy Optimizations
14.5 Conclusion
References
15. Case Study of Energy Optimization: Electric Vehicle Energy Consumption Minimization Using Genetic Algorithm
Pedram Asef
15.1 Introduction
15.2 Vehicle Modelling to Optimisation
15.2.1 Vehicle Mathematical Modelling
15.2.2 Vehicle Model Optimisation Process: Applied Genetic Algorithm
15.2.3 GA Optimisation Results and Discussion
15.3 Conclusion
References
About the Editors
Index
Back to Top