Hybrid Intelligent Approaches for Smart Energy E-Book
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- Herausgeber: John Wiley & Sons
- Kategorie: Wissenschaft und neue Technologien
- Serie: Next Generation Computing and Communication Engineering
- Sprache: Englisch
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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Veröffentlichungsjahr: 2022
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Table of Contents
Cover
Series Page
Title Page
Copyright Page
List of Contributors
Preface
Acknowledgements
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
1.4 Results and Discussions
1.5 Conclusion
References
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
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
3.3 Energy and Cloud Computing
3.4 Saving Electricity Prices
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
3.9 Comments and Directions for the Future
References
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
4.4 Conclusion
Acknowledgement
References
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
5.5 PSO Based Optimization
5.6 Test Systems
5.7 Results and Discussions
5.8 Conclusions
References
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
7 Road Network Energy Optimization Using IoT and Deep Learning
7.1 Introduction
7.2 Road Network
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
8.1 Introduction
8.2 Study of Energy Management
8.3 Energy Optimization in Smart Home
8.4 Scope and Study Methodology
8.5 Conclusion
References
9 Machine Learning Based Approach for Energy Management in the Smart City Revolution
9.1 Introduction
9.2 Need for Energy Optimization
9.3 Methods for Energy Effectiveness in Smart City
9.4 Role of Machine Learning in Smart City Energy Optimization
9.5 Machine Learning Applications in Smart City
9.6 Conclusion
References
10 Design of an Energy Efficient IoT System for Poultry Farm Management
10.1 Introduction
10.2 Literature Survey
10.3 Proposed Methodology
10.4 Hardware Components
10.5 Results and Discussion
10.6 Conclusion
References
11 IoT Based Energy Optimization in Smart Farming Using AI
11.1 Introduction
11.2 IoT in Smart Farming
11.3 AI in Smart Farming
11.4 Energy Optimization in Smart Farming
11.5 Experimental Results
11.6 Conclusion
References
12 Smart Energy Management Techniques in Industries 5.0
12.1 Introduction
12.2 Related Work
12.3 General Smart Grid Architecture
12.4 Smart Control of Power
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
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
13.6 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
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
15.1 Introduction
15.2 Vehicle Modelling to Optimisation
15.3 Conclusion
References
About the Editors
Index
Also of Interest
End User License Agreement
List of Tables
Chapter 1
Table 1.1 Optimum parameters selected for single prediction methods.
Table 1.2 Performance evaluation of different prediction methods.
Chapter 2
Table 2.1 Various cloud computing based application services.
Table 2.2 Various fog computing based application services.
Table 2.3 List of software based energy optimization techniques.
Table 2.4 List of problems and energy optimization technique.
Table 2.5 List of problems solved with network-based energy optimization techn...
Table 2.6 Energy optimization techniques for solving massive energy problem.
Chapter 4
Table 4.1 Mechanical property of Al/SiO
2
composites.
Table 4.2 Process parameters and their levels.
Table 4.3 Experimental layout using L9 OA and performance results.
Table 4.4 Normalized and coefficient with grade.
Table 4.5 ANOVA shown in and percentage contribution that influences grey rela...
Table 4.6 Response for means.
Table 4.7 Results of confirmation experiment for GRG.
Chapter 5
Table 5.1 IEEE-69-bus radial system losses for all different scenarios.
Table 5.2 Proposed system REL index for all scenarios.
Table 5.3 Fitness function and indices value of proposed system for all scenar...
Table 5.4 Comparative results for proposed and existing work.
Chapter 7
Table 7.1 Types of road based on material.
Table 7.2 Types of road based on traffic volume.
Chapter 8
Table 8.1 Commercial building energy-savings from SH technologies.
Chapter 11
Table 11.1 Simulation parameters.
Chapter 13
Table 13.1 Indicator values.
Table 13.2 Evaluation of early manifestation of disease.
Table 13.3 Record of detected anomalous indicators in test log.
Table 13.4 Determining degree of risk.
Table 13.5 Determining Risk of disease at current values of temperature and pu...
Table 13.6 Set of protocols of ZG standard.
Chapter 14
Table 14.1 Energy wastage and consumption in hospital and home.
Chapter 15
Table 15.1 Environmental-related parameters set for simulation settings.
Table 15.2 Light truck specification data.
Table 15.3 Energy consumption minimization problems for a delivery electric tr...
Table 15.4 Uncertainty definition of controllable variables
Table 15.5 Uncertainty definition of controllable variables.
Table 15.6 Optimisation variables lower/upper ranges.
List of Illustrations
Chapter 1
Figure 1.1 Structure of smart grid [1].
Figure 1.2 Overall steps for load time series forecasting methodology.
Figure 1.3 Classification of AI-based prediction.
Figure 1.4 Feed forward neural network with single hidden layer.
Figure 1.5 Support vector regression architecture.
Figure 1.6 RNN architecture.
Figure 1.7 LSTM network.
Figure 1.8 Signal decomposition based load-forecasting strategy.
Figure 1.9 Dataset collected for months of January, April, July, and September...
Figure 1.10 PACF graph of dataset for January, April, July, and September.
Figure 1.11 (a) Prediction results of single prediction methods for January. (...
Figure 1.12 (a) EMD decompositions of dataset (January). (b) EMD decomposition...
Figure 1.13 (a) Prediction results using hybrid prediction method for dataset ...
Figure 1.14 (a) RMSE of selected predictions techniques for different datasets...
Chapter 2
Figure 2.1 General architecture of cloud computing.
Figure 2.2 Why cloud computing service is important in near future.
Figure 2.3 Common fog computing architecture with networking.
Figure 2.4 General layered architecture for fog computing.
Figure 2.5 Types of energy optimization techniques.
Chapter 3
Figure 3.1 Energy efficiency in perspective of cloud federation.
Figure 3.2 Cloud computing architecture.
Figure 3.3 Energy losing and wasting scenarios in cloud computing.
Figure 3.4 Business model of cloud computing.
Figure 3.5 Taxonomy of energy efficiency on storage.
Figure 3.6 Energy aware edge in operating system.
Figure 3.7 IoT connecting edge with other devices.
Figure 3.8 Offloading standard distribution task.
Chapter 4
Figure 4.1 Experimental setup of stir casting.
Figure 4.2 AlMMC.
Figure 4.3 Microstructure of Al/SiO
2
.
Figure 4.4 WEDM machine (EDM DK7740).
Figure 4.5 XRD pattern of SiO
2
.
Figure 4.6 SEM image of AMMC.
Figure 4.7 Main effects plots for S/N ratio.
Chapter 5
Figure 5.1 Process flowchart of particle swarm optimization technique.
Figure 5.2 Modified IEEE-69-bus radial network including 5 tie line switches.
Figure 5.3 69-bus radial system voltage profile for scenarios 1, 2, 3, and 5.
Figure 5.4 Proposed system voltage profile for scenarios 1, 2, 4, and 6.
Figure 5.5 Proposed system voltage profile for all scenarios.
Figure 5.6 Proposed system APL for scenarios 1, 2, 3, and 5.
Figure 5.7 Proposed system APL for scenarios 1, 2, 4, and 6.
Figure 5.8 Proposed system APL for all scenarios.
Figure 5.9 Proposed system RPL for scenarios 1, 2, 3, and 5.
Figure 5.10 Proposed system RPL for scenarios 1, 2, 4, and 6.
Figure 5.11 Proposed system RPL for all scenarios.
Figure 5.12 Proposed system losses for all scenarios.
Figure 5.13 Proposed system reliability index for different scenarios.
Figure 5.14 Proposed system REL for all scenarios.
Figure 5.15 Fitness function and indices value of proposed system for all scen...
Chapter 6
Figure 6.1 Spectrum sensing applications in IoT.
Figure 6.2 Sensor node arrangement in IoT.
Figure 6.3 SOA architecture for smart building monitoring.
Figure 6.4 Distributed model for cognitive spectrum sensing in IoT.
Figure 6.5 Sensor selection process.
Figure 6.6 Proposed DRL structure.
Figure 6.7 Moisture detection.
Figure 6.8 Stress detection.
Figure 6.9 Heat detection.
Figure 6.10 DRC vs GGA vs GGH.
Figure 6.11 Comparison of processing time.
Chapter 7
Figure 7.1 Overview of intelligent road transportation system.
Figure 7.2 Graph representation of road network from real road map.
Figure 7.3 Section of atal road tunnel in India of 9.02 km long.
Figure 7.4 Entrance of La Linea road tunnel in Columbia of 8.65 km long.
Figure 7.5 Solar street lighting.
Figure 7.6 Road tunnel lighting.
Figure 7.7 Road damage detection
Chapter 8
Figure 8.1 Image of a smart home.
Figure 8.2 (a) Quantity of Indian commercial buildings by size. (b) Floor area...
Chapter 9
Figure 9.1 Smart city evolution timeline.
Figure 9.2 Steps for smart transportation system work flow.
Figure 9.3 (a) Design flow of conventional engineering. (b) Baseline machine l...
Figure 9.4 Machine learning methodology.
Figure 9.5 Applications based on smart city.
Chapter 10
Figure 10.1 System architecture of smart poultry farm.
Figure 10.2 Temperature sensor (LM35) board.
Figure 10.3 Humidity sensor (HR 202) board.
Figure 10.4 Gas sensor (MQ7) board.
Figure 10.5 Floating switch.
Figure 10.6 LDR sensor board.
Figure 10.7 GSM (SIM800 Quad) board.
Figure 10.8 Hardware module.
Figure 10.9 Temperatures between 29°C to 31°C.
Figure 10.10 Temperatures above 31
0
C.
Figure 10.11 Temperatures below 28
0
C.
Figure 10.12 Exhaust fan is switched ON.
Figure 10.13 Humidity output.
Figure 10.14 LDR output.
Figure 10.15 Water level monitoring output.
Chapter 11
Figure 11.1 IoT and AI based smart farming.
Figure 11.2 Analysis of network throughput.
Figure 11.3 Analysis of network latency.
Figure 11.4 Analysis of energy consumption.
Chapter 12
Figure 12.1 Smart grid architecture.
Figure 12.2 Inducement for flexible demand.
Figure 12.3 Inducement for constant revenue.
Figure 12.4 Inducement for contingent revenue.
Chapter 13
Figure 13.1 Problem solving approach.
Figure 13.2 Architecture of fuzzy logic system.
Figure 13.3 ANN algorithms.
Figure 13.4 Genetic algorithm.
Figure 13.5 Expert system.
Figure 13.6 Semantic terms of indicators for set of membership functions.
Figure 13.7 Division of initial risk level variable (Y) into six groups of fuz...
Figure 13.8 Information flow.
Chapter 14
Figure 14.1 Model for event management in energy system [1].
Figure 14.2 Surrogate model with optimization algorithm [1].
Figure 14.3 Reinforcement learning structure for energy optimization.
Figure 14.4 Supply chain components of energy optimization [3].
Chapter 15
Figure 15.1 Longitudinal force distribution of the electric vehicle.
Figure 15.2 Light truck standard, LDV_PVU, drive cycle for delivery applicatio...
Figure 15.3 GA optimisation flowchart for longitudinal dynamics of a vehicle.
Figure 15.4 The global sensitivity analysis over the most influenceable contro...
Figure 15.5 The global sensitivity analysis over the most influenceable contro...
Figure 15.6 The total energy consumption
f
1
(objective) before and after the o...
Figure 15.7 The torque-speed-efficiency map before (in colour) and after optim...
Guide
Cover Page
Series Page
Title Page
Copyright Page
List of Contributors
Preface
Acknowledgements
Table of Contents
Begin Reading
About the Editors
Index
Also of Interest
WILEY END USER LICENSE AGREEMENT
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Scrivener Publishing100 Cummings Center, Suite 541JBeverly, MA 01915-6106
Publishers at ScrivenerMartin Scrivener ([email protected])Phillip Carmical ([email protected])
Hybrid Intelligent Approaches for Smart Energy
Practical Applications
Edited by
John A. Senthil Kumar MohanSanjeevikumar Padmanaban
and
Yasir Hamid
This edition first published 2022 by John Wiley & Sons, Inc., 111 River Street, Hoboken, NJ 07030, USA and Scrivener Publishing LLC, 100 Cummings Center, Suite 541J, Beverly, MA 01915, USA© 2022 Scrivener Publishing LLCFor more information about Scrivener publications please visit www.scrivenerpublishing.com.
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Library of Congress Cataloging-in-Publication Data
ISBN 9781119821243
Cover image: Electrical Tower, Patrick Daxenbichler | Internet of Things, Monthira Yodtiwong | Dreamstime.comCover design by Kris Hackerott
List of Contributors
Shihabudheen KV
Sheik Mohammed S
N.M. Balamurugan
TKS Rathish babu
K Maithili
M. Adimoolam
Praveen Mishra
M. Sivaram
A. Daniel
Raju Ranjan
M.S. Kumaravel
N. Alagumurthi
Mathiyalagan
Balmukund Kumar
Aashish Kumar Bohre
M. Pavithra
R. Rajmohan
T. Ananth Kumar
S. Usharani
P. Manju Bala
N. Revathi
R. Gayathri
S. Sathya
G. Karthi
A. Suresh Kumar
S. Prakash
Deepica S.
S. Kalavathi
Angelin Blessy J.
D. Maria Manuel Vianny
G. Rajakumar
G. Gnana Jenifer
T. S. Arun Samuel
N. Padmapriya
R. Aswini
P. Kanimozhi
R. Indrakumari
G. Vallathan
Senthilkumar Meyyappan
T. Rajani
Pedram Asef
Preface
This book aims to primarily address issues surrounding the optimization, consumption, and management of energy resources with the use of hybrid intelligence techniques. The consumption and optimization of energy play a crucial role in sustaining the development goals of modern society. The need to save energy while reducing its overall cost cannot be emphasized enough. In recent times, smart computing technologies have slowly but inevitably replaced the traditional computational methods in energy optimization and consumption and its optimal scheduling and usage. Smart computing has permeated almost all areas of technological innovation today. Smart computation techniques such as artificial intelligence, machine learning, deep learning, and IoT have become indispensable to designing and building applications that span diverse areas like distributed environments, healthcare, smart cities, agriculture, and a host of other functional areas. Hence, it is no surprise that in power usage, these smart computation techniques have been incorporated along with traditional computation and scheduling methods to bring about optimal reductions. This book is predominantly focused on emerging concepts and algorithmic approaches in machine learning and artificial intelligence to bring about enhancements in soft computing techniques in energy optimization. This site will contribute to research work undertaken by researchers, academicians, data scientists, and technology developers alike.
This book comprises of fifteen chapters, each chapter elaborating upon various aspects and techniques related to optimizing and managing energy. Chapter 1 elaborates upon the review and analysis of machine learning-based techniques for load forecasting. A load-forecasting algorithm for time-series loads using AI techniques with supervised methods is presented and discussed. This includes a comparative assessment of load forecasting based on supervised artificial intelligent algorithms such as Artificial Neural Networks (ANN), Support Vector Machine (SVM), and Extreme Learning Machine (ELM), which is performed on smart meter data. The results are presented in this chapter, along with a performance analysis of the selected algorithms.
Chapter 2 elaborates on various energy optimization techniques, examines how energy can be optimized, and what techniques are available and used in cloud and fog computing. Chapter 3 is focused on the energy efficiencies of various next-generation cloud computing techniques. This chapter delves deeper into the benefits and trade-offs arising from the adoption of various energy conservation measures. It outlines the challenges and recommendations to be considered in the future. Chapter 4 presents a method of energy optimization using a Silicon dioxide composite and an analysis of wire electrical discharge machining characteristics. Here, a stir casting method to produce the uniform distribution of SiO2 particles in an Aluminum matrix composite (AMC) is expanded upon, along with confirmation by microstructural analysis during which the XRD pattern reveals that the SiO2 indeed has a monoclinic crystalline structure.
Chapter 5 presents a discussion on optimal planning of renewable DG and the reconfiguration of the distribution network that considers multiple objectives using the PSO technique under different scenarios. This chapter discusses the methods for improving voltage and reliability and reducing power losses (active and reactive) and proposes an IEEE-69-bus test distribution network with separate scenarios for optimal sizing-siting of multi-renewable DGs with the reconfiguration of the particle swarm optimization (PSO) technique. Chapter 6 propounds upon energy optimization for spectrum sensing in Distributed Cooperative IoT Networks using deep learning techniques. This chapter further investigates applying deep learning methods to the Internet of Things (IoT) applications while focusing on energy optimization mechanisms.
Chapter 7 expands upon the energy optimization for a road network using IoT and deep learning methods. In this chapter, an overview of the components of an intelligent road transportation system is first presented. It is then followed by a discussion on the potential for leveraging the advancements in deep learning and Internet-of-Things (IoT) in modeling the short-term traffic states for the prediction of traffic flow and energy optimization in using fuel and electricity consumption and road lighting. Chapter 8 explores the means of energy optimization in smart homes and buildings. In this chapter, information about communication technologies that enable energy optimization is presented. Such optimizations in smart homes and buildings are proposed using energy management and optimal scheduling by utilizing the Internet of Things and smart grids. This chapter further touches upon methodologies considered and used in the study and presents recommendations for the future direction of the work.
Chapter 9 focuses on machine learning-based approaches in energy management for spurring the smart city revolution. This chapter proposes a machine algorithm for optimizing the energy sources in a smart city. Such an approach can benefit future smart city plans and help adequately deal with existing energy resources. Chapter 10 similarly explores the design of an energy efficient IoT system in the management of poultry farms. It proposes a technique for enabling efficient poultry farming that will cause increased production and profits. It propounds the use of solar technologies for generating electricity, which, apart from being used in households, have also been successfully implemented in commercial farms to make use of energy-saving batteries and the opportunity to sell the power back to the grid.
Chapter 11 considers IoT-based energy optimization methods that use artificial intelligence for enabling smart farming. This chapter outlines how IoT can contribute to the improvement of agricultural productivity for reaching new sustainability heights. The first part of the chapter introduces the many ways of using IoT in smart farming and its manifold benefits. The second part elaborates on revolutionizing the farming process by using Artificial Intelligence. The third part emphasizes energy optimization in agriculture, in general, using IoT and Artificial Intelligence. Chapter 12 proposes the use of smart energy management techniques in industries. It elaborates upon the concept of energy consumption and control to effectively regulate energy demands using smart grids in manufacturing industries. It also offers a description and details of the statistical method that can be tailored to the specific needs of manufacturing customers for building energy-efficient power systems.
Chapter 13 reviews energy optimization techniques using soft computing in telemedicine. This chapter proposes an expert system based on fuzzy rules that can be used to calculate patient risk levels. This is followed by discussing various energy optimization techniques using the Internet of Things, machine learning, and deep learning techniques in Chapter 14. These techniques are used in different applications and for various optimization. The Internet of Things is connected to multiple technologies such as Cyber-Physical Systems, Big Data Management, Cloud Management, etc. Using IoT connectivity continuously to monitor energy usage and optimization in various devices and energy consumption can be reduced in several industrial and manufacturing fields, leading to many benefits, including environmental benefits such as reducing CO2 emissions.
Acknowledgements
We are deeply indebted to Almighty God for giving us this opportunity and it is only possible with the presence of God.
I want to thank the Almighty for giving me enough mental strength and belief in completing this work successfully. I thank my friends and family members for their help and support. I express my sincere thanks to the management of Galgotias University, Greater Noida, Uttar Pradesh, India. I wish to express my deep sense of gratitude and thanks to Wiley-Scrivener, publisher, for their valuable suggestions and encouragement.
John A. PhD
I sincerely thank my VIT management for continuous support and encouragement. I thank my family members and friends for their timely support and help. I extend my thanks to Wiley-Scrivener, publisher, for excellent support and guidance.
Senthilkuamr Mohan, PhD
I express my sincere thanks to the management of CTiF Global Capsule, Department of Business Development and Technology, Aarhus University, Birk Centerpark 15, Herning 7400, Denmark. Also, I would like to thank the Wiley-Scrivener Press for allowing me to edit this book.
P. Sanjeevikumar, PhD
I express my sincere thanks to the management of Abu Dhabi Polytechnic. Also, I would like to thank the Wiley-Scrivener Press for allowing me to edit this book.
Yasir Hamid, PhD
