Hybrid Intelligent Approaches for Smart Energy (eBook, PDF)

Practical Applications
Redaktion: A, John; Hamid, Yasir; Sanjeevikumar, P.; Mohan, Senthil Kumar

• Format: PDF

Produktbeschreibung

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.

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Produktdetails

• Verlag: John Wiley & Sons
• Seitenzahl: 336
• Erscheinungstermin: 19. September 2022
• Englisch
• ISBN-13: 9781119821861
• Artikelnr.: 66253452

Autorenporträt

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.

Inhaltsangabe

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 Pl