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Hybrid Intelligent Approaches for Smart Energy: Practical Applications

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HYBRID INTELLIGENT APPROACHES FOR SMART ENERGY

Green technologies and cleaner energy are two of the most important topics facing our world today, and the march toward efficient energy systems, smart cities, and other green technologies, has been, and continues to be, a long and intricate one. Books like this one keep the veteran engineer and student, alike, up to date on current trends in the technology and offer a reference for the industry for its practical applications.

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.

Author(s): Senthil Kumar Mohan, John A., Sanjeevikumar Padmanaban, Yasir Hamid
Series: Next Generation Computing and Communication Engineering
Publisher: Wiley-Scrivener
Year: 2022

Language: English
Pages: 337
City: Beverly

Cover
Title Page
Copyright Page
Contents
List of Contributors
Preface
Acknowledgements
Chapter 1 Review and Analysis of Machine Learning Based Techniques for Load Forecasting in Smart Grid System
1.1 Introduction
1.2 Forecasting Methodology
1.3 AI-Based Prediction Methods
1.3.1 Single Prediction Methods
1.3.1.1 Linear Regression
1.3.1.2 Artificial Neural Networks (ANN)
1.3.1.3 Support Vector Regression (SVR)
1.3.1.4 Extreme Learning Machine
1.3.1.5 Neuro-Fuzzy Techniques
1.3.1.6 Deep Learning Techniques
1.3.2 Hybrid Prediction Methods
1.3.2.1 Combined AI-Based Prediction Techniques
1.3.2.2 Signal Decomposition Based Prediction Techniques
1.3.2.3 EMD Based Decomposition
1.3.2.4 Wavelet Based Decomposition
1.4 Results and Discussions
1.4.1 Description of Dataset
1.4.2 Performance Analysis of Single Prediction Methods for Load Forecasting
1.4.2.1 Feature Selection
1.4.2.2 Optimal Parameter Selection
1.4.2.3 Prediction Results of Single Prediction Methods
1.4.3 Performance Analysis of Hybrid Prediction Methods for Load Forecasting
1.4.4 Comparative Analysis
1.5 Conclusion
References
Chapter 2 Energy Optimized Techniques in Cloud and Fog Computing
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
Chapter 3 Energy-Efficient Cloud Computing Techniques for Next Generation: Ways of Establishing and Strategies for Future Developments
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
Chapter 4 Energy Optimization Using Silicon Dioxide Composite and Analysis of Wire Electrical Discharge Machining Characteristics
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
References
Chapter 5 Optimal Planning of Renewable DG and Reconfiguration of Distribution Network Considering Multiple Objectives Using PSO Technique for Different Scenarios
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
5.6 Test Systems
5.7 Results and Discussions
5.8 Conclusions
References
Chapter 6 Investigation of Energy Optimization for Spectrum Sensing in Distributed Cooperative IoT Network Using Deep Learning Techniques
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
Chapter 7 Road Network Energy Optimization Using IoT and Deep Learning
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
Chapter 8 Energy Optimization in Smart Homes and Buildings
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
Chapter 9 Machine Learning Based Approach for Energy Management in the Smart City Revolution
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
Chapter 10 Design of an Energy Efficient IoT System for Poultry Farm Management
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
Chapter 11 IoT Based Energy Optimization in Smart Farming Using AI
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
Chapter 12 Smart Energy Management Techniques in Industries 5.0
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
Chapter 13 Energy Optimization Techniques in Telemedicine Using Soft Computing
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
Chapter 14 Healthcare: Energy Optimization Techniques Using IoT and Machine Learning
14.1 Introduction
14.2 Energy Optimization Process
14.3 Energy Optimization Techniques in Healthcare
14.3.1 Energy Optimization in Building
14.3.2 Machine Learning for Energy Optimization
14.3.3 Reinforcement Learning for Energy Optimization
14.3.4 Energy Optimization of Sustainable Internet of Things (IoT)
14.4 Future Direction of Energy Optimizations
14.5 Conclusion
References
Chapter 15 Case Study of Energy Optimization: Electric Vehicle Energy Consumption Minimization Using Genetic Algorithm
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
EULA