Sustainable Management of Electronic Waste E-Book
173,99 €
Mehr erfahren.
- Herausgeber: John Wiley & Sons
- Kategorie: Wissenschaft und neue Technologien
- Sprache: Englisch
SUSTAINABLE MANAGEMENT OF ELECTRONIC WASTE Written and edited by a group of industry professionals, this new volume provides cutting-edge insights into how the sustainability of managing electronic waste can be achieved, for engineers, scientists, and students. As a result of the rapid advancement of technology and the globalization of the economy, waste electrical and electronic equipment (WEEE) management has become increasingly important. Manufacturers are especially concerned about the proper disposal of their waste, and researchers need to identify the obstacles and enablers that stand in the way of implementing a long-term WEEE management system in order to develop a long-term WEEE management system. Further, the literature did not adequately capture the perspectives of multiple stakeholders while also identifying the enablers required for the development of sustainable WEEE management policies, which was particularly important in developing countries. This volume fills a gap in the literature by considering the perspectives of multiple stakeholders to identify enablers of sustainable WEEE management in emerging economies which was previously unexplored. This book focuses on the most recent technological advancements for the twenty-first century, emphasizing the synergies that exist between computer science, bioinformatics, and other sciences. The research and development of artificial intelligence, machine learning, blockchain technologies, quantum computing with cryptography, nanotechnology, sensors based on biotechnology, Internet of Things devices, nature-inspired algorithms, computer vision techniques, computational biology, and other topics are covered in this book, along with their applications in the fields of science, engineering, physical science, and economics. Modern environmental techniques are among the most innovative innovations emerging as a result of the insatiable demand for health standards in the modern world.
Sie lesen das E-Book in den Legimi-Apps auf:
Seitenzahl: 667
Veröffentlichungsjahr: 2024
Ähnliche
Table of Contents
Cover
Table of Contents
Series Page
Title Page
Copyright Page
Foreword
Preface
1 Integration of Artificial Intelligence Techniques for Energy Management
1.1 Introduction
1.2 Summary of Artificial Intelligence Techniques
1.3 Reasons for Applying AI in EMS
1.4 ML in Renewable Energy
1.5 Integration of AI in Smart Grids
1.6 Parameter Selection and Optimization
1.7 Biological-Based Models for EMS
1.8 Future of ML in Energy
1.9 Opportunities, Limitations, and Challenges
1.10 Conclusion
References
2 Artificial Neural Network Process Optimization for Predicting the Thermal Properties of Biomass: Recent Advances and Future Challenges
Abbreviations
2.1 Introduction
2.2 AI Technology and Its Application on Renewable Energy
2.3 Bioenergy and ANN
2.4 ANN Model Development
2.5 Future Scope of ANN-Bioenergy
2.6 Conclusion
References
3 E-Waste Management and Bioethanol Production
3.1 Introduction
3.2 Review
3.3 Degradation of Lignocellulose
3.4 Bioprocessing of Lignocellulosic Materials
3.5 AI Technologies
3.6 Conclusions
References
4 A Novel-Based Smart Home Energy Management System for Energy Consumption Prediction Using a Machine Learning Algorithm
4.1 Introduction
4.2 Literature Review
4.3 Proposed Work
4.4 Results and Discussion
4.5 Conclusion
References
5 AI-Based Weather Forecasting System for Smart Agriculture System Using a Recurrent Neural Networks (RNN) Algorithm
5.1 Introduction
5.2 Literature Review
5.3 Proposed Work
5.4 Results and Discussion
5.5 Conclusion
References
6 Comprehensive Review of IoT-Based Green Energy Monitoring Systems
6.1 Introduction
6.2 IoT-Based Green Energy Monitoring Systems
6.3 Comparative Analysis
6.4 Conclusion
References
7 The Contribution of Renewable Energy with Artificial Intelligence to Accomplish Organizational Development Goals and Its Impacts
7.1 Introduction
7.2 AI Contributions in Conventional and Renewable Energy
7.3 AI-Based Technology in Renewable Energy
7.4 A Look at Some Challenges Faced by the Renewable Energy Industry
7.5 Higher Computational Power & Intelligent Robotics
7.6 The Impact of Digital Technology on Energy Company Results
7.7 Conclusion
7.8 Future Work
References
8 Current Trends in E-Waste Management
8.1 Introduction
8.2 What is E-Waste?
8.3 How is E-Waste Recycled and Why Do Problems Exist?
8.4 Global E-Waste Management Market Restraints
8.5 Global E-Waste Management Market Opportunities
8.6 Management of E-Waste in India
8.7 New Solutions for E-Waste Excess
8.8 Recent Trends in E-Waste Management
8.9 E-Waste Regulation in India
8.10 Recovery of Resources from E-Waste
8.11 Generation and Management of Mobile Phone Waste
8.12 Players of the Market
8.13 Recent Developments
8.14 Conclusion
References
9 Current E-Waste Management: An Exploratory Study on Managing E-Waste for Environmental Sustainability
9.1 Introduction
9.2 E-Waste Production
9.3 The Present Predicament
9.4 Conclusion
References
10 Challenges in E-Waste Management
10.1 Introduction
10.2 E-Waste: Meaning and Definition
10.3 Environmental Sustainability in E-Waste Management
10.4 Sustainable Management of E-Waste
10.5 Life Cycle of E-Waste
10.6 Terminology of E-Waste
10.7 Key Stakeholders in the E-Waste Management System
10.8 Status of E-Waste Management in India
10.9 Challenges in E-Waste Management
10.10 E-Waste Policy and Regulation
10.11 E-Waste Recycling
10.12 Life Cycle Assessment (LCA) Analysis of E-Waste
10.13 Existing Laws Relating to E-Waste
10.14 Management Options
10.15 Conclusion
References
11 Recycling of Electronic Wastes: Practices, Recycling Methodologies, and Statistics
11.1 Introduction
11.2 Recycling E-Waste
11.3 Smart Phones at the End of Their Life
11.4 Recycling of Printed Circuit Boards (PCB)
11.5 Solar Panel Recycling
11.6 How Has E-Waste Management in India Evolved Through the Years?
11.7 Conclusion
References
12 Sustainable Development Through the Life Cycle of Electronic Waste Management
12.1 Introduction
12.2 Impact on the Environment
12.3 Environmental Impact of Electronics Manufacturing
12.4 E-Waste Management Initiative
12.5 Issues with E-Waste in India
12.6 Impact of E-Waste Recycling in Developing Nations
12.7 Opportunities and Challenges in E-Waste Management in India
12.8 Recent Investigations on Electronic Waste Management
12.9 Conclusion
References
13 E-Waste Challenges & Solutions
13.1 Introduction
13.2 Related Works
13.3 E-Waste: A Preamble
13.4 Six Categories of E-Waste
13.5 Composition of Materials Found in Equipment
13.6 Recycling of WEEE
13.7 Procedures in the E-Waste Management
13.8 E-Waste (Management) Rules
13.9 Report from the Central Pollution Control Board
13.10 An Integrated Waste Management Systems Web Application
13.11 E-Waste Management Rules 2016 Amendments
13.12 Management of Battery Waste Rules, 2022
13.13 Conclusion
References
14 Global Challenges of E-Waste: Its Management and Future Scenarios
14.1 Introduction
14.2 Worldwide Production of E-Waste
14.3 Global Availability of E-Waste: Additional Information
14.4 Environmental Impact of E-Waste
14.5 Management of E-Waste
14.6 Concerns and Challenges
14.7 Future Scenarios of E-Waste
14.8 The Need for Scientific Acknowledgment and Research
14.9 Conclusion
References
15 Impact of E-Waste on Reproduction
15.1 Introduction
15.2 Literature Review
15.3 Discussion
15.4 Conclusion
References
16 Challenges in Scale-Up of Bio-Hydrometallurgical Treatment of Electronic Waste: From Laboratory-Based Research to Practical Industrial Applications
16.1 Introduction
16.2 Methodology
16.3 Results
16.4 Economic Feasibility
16.5 Conclusions
References
17 Current Advances in Recycling of Electronic Wastes
17.1 Introduction
17.2 E-Waste: A General Description and Classification and Issues on the Environment and Health
17.3 Conventional Approaches to E-Waste Recycling, Advantages and Disadvantages
17.4 Advances in Approaches for Improving E-Waste Recycling for Value-Added Materials and Biomaterials Generation
17.5 Conclusion
Acknowledgement
References
18 E-Waste: The Problem and the Solutions
18.1 Introduction
18.2 India’s Electronic Waste Crisis
18.3 Inadequate Infrastructure for Refurbishing E-Waste
18.4 Enhancing India’s E-Waste Management
18.5 Effects of E-Waste Recycling in Developing India Like Nations
18.6 Opportunity for Managing E-Waste in India
18.7 Management of Electronic Waste
References
19 Contribution of E-Waste Management in Green Computing
19.1 Introduction
19.2 Concept of Green Computing
19.3 A History of Green Computing
19.4 Benefits of Recycling in Green Computing
19.5 E-Waste Management Steps
19.6 E-Waste Recycling: An Approach Towards Green Computing
19.7 Harmful Effects of E-Waste
19.8 E-Waste and the Sustainable Development Goals of the 2030 Agenda
19.9 Significant International E-Waste Agreements
19.10 Conclusion
References
Index
Also of InterestAlso of Interest
End User License Agreement
List of Tables
Chapter 1
Table 1.1 Classification and comparison of AI, ML, and DL applications in ener...
Chapter 2
Table 2.1 Various applications of ANN in biomass characterization.
Table 2.2 Types of models in ANN.
Table 2.3 Types of algorithms used for training the data in ANN.
Chapter 3
Table 3.1 Types of lignocellulosics and their uses (Howard
et al,
2003; Haq
et
...
Table 3.2 Lignocellulose contents of common agricultural residues and waste (H...
Table 3.3 Pre-treatment processes of lignocellulosic materials (Taherzadeh
et
...
Table 3.4 Various raw materials for ethanol production.
Chapter 4
Table 4.1 Artificial intelligence vs. machine learning.
Table 4.2 Survey on existing works.
Table 4.3 Appliance consumption detail.
Chapter 5
Table 5.1 Difference between machine learning and deep learning.
Table 5.2 Summary of analysis based on factors that affected it.
Table 5.3 Statistical summary of RNN.
Table 5.4 Algorithm RNN to predict exact farming.
Chapter 6
Table 6.1 Characteristics of different IoT-based energy monitoring systems.
Table 6.2 Comparison of different monitoring methods.
Chapter 7
Table 7.1 Various algorithm designs for variable objects in AI technology.
Table 7.2 Comparison statement of artificial intelligence in pros and cons wit...
Chapter 9
Table 9.1 The structure of E-waste.
Chapter 10
Table 10.1 Year-wise production of E-waste in India.
Table 10.2 Various E-waste sources, their constituents, and health impacts [4-...
Table 10.3 Stakeholders & their responsibilities.
Chapter 13
Table 13.1 Process comparison for treating E-waste.
Chapter 14
Table 14.1 Worldwide amount of electronic waste generated from 2010 to 2019 (A...
Chapter 16
Table 16.1 Studies on bioflotation (Source: [12]).
Table 16.2 Comparative characteristics of pyro-and hydrometallurgy as metal ex...
Table 16.3 Bioleaching mechanisms (Source [27]).
Table 16.4 Bioleaching strategies (Source [8]).
Table 16.5 Physicochemical variables affecting the bioleaching process.
Table 16.6 Work summary.
Chapter 17
Table 17.1 E-waste recycling approaches with possible merits and demerits.
Table 17.2 Microbial regime involvement for E-waste recycling and recovery.
Chapter 18
Table 18.1 India’s top ten generating States of electronic trash.
Table 18.2 Top ten E-waste generating cities.
List of Illustrations
Chapter 1
Figure 1.1 Pictorial representation of machine learning approaches.
Chapter 2
Figure 2.1 Global energy production, utilization, and forecast (“Short-Term En...
Figure 2.2a Utilization of liquid fuels globally and its forecast.
Figure 2.2b Utilization of liquid fuels globally and its forecast.
Figure 2.3 Diagrammatic representation of ANN.
Figure 2.4 Stages involved in ANN model development.
Chapter 3
Figure 3.1 Conversion pathway for making ethanol from cellulosic biomass.
Chapter 4
Figure 4.1 Automation methods.
Figure 4.2 Artificial intelligence vs. machine learning.
Figure 4.3 Lifecycle of ML.
Figure 4.4 Proposed system.
Figure 4.5 Correlation coefficient of EBLRML.
Figure 4.6 Distribution of final grade based on energy consumption.
Figure 4.7 Energy level consumption prediction using EBLRML.
Figure 4.8 Appliance usage and consumption level difference based on electrici...
Figure 4.9 Smart home energy consumption prediction using EBLRML.
Chapter 5
Figure 5.1 Technologies involved in real-time management of agriculture.
Figure 5.2 Applications of RNN (Recurrent Neural Networks).
Figure 5.3 Usage of information technology.
Figure 5.4 Application of AI techniques in agriculture.
Figure 5.5 Proposed system for smart agriculture.
Figure 5.6 Prediction of smart agriculture data.
Figure 5.7 Correlation coefficient plot for smart agriculture management syste...
Chapter 7
Figure 7.1 Predicted assets in AI growth of redict in year wise.
Figure 7.2 Architectural model of artificial intelligence on various renewable...
Figure 7.3 AI applied to forecasting wind energy.
Figure 7.4 Different methods of intelligence prediction based on human models.
Figure 7.5 Smart energy match for power source in artificial intelligence.
Chapter 9
Figure 9.1 Equipment for electronics and electricity waste.
Figure 9.2 Chemical properties of electronics and electricity waste.
Figure 9.3 Impacts of recycling of E-waste.
Figure 9.4 Program of recycling of E-waste.
Figure 9.5 Process of E-waste management.
Chapter 10
Figure 10.1 Life cycle of EEE.
Chapter 11
Figure 11.1 Damaged, surplus, and outdated electronic equipment.
Figure 11.2 Drivers and barriers to return and recycling of mobile phones.
Figure 11.3 States generating E-waste.
Figure 11.4 Generation of E-waste every year (in metric tons).
Chapter 12
Figure 12.1 E-waste reproduction unit.
Figure 12.2 Waste electrical and electronic equipment.
Figure 12.3 MAYER alloys corporation.
Figure 12.4 E-waste recycling process.
Figure 12.5 Modern structure of e-waste recycling network.
Chapter 13
Figure 13.1 Composition of WEEE [11].
Chapter 16
Figure 16.1 Conceptual system process flow map for biohydrometallurgical recov...
Figure 16.2 Physical separation techniques in separation process.
Figure 16.3 Bioflotation by biofilm and EPS formation (Adapted from [12]).
Figure 16.4 Microbial mechanisms for metal recovery (Adapted from [52]).
Figure 16.5 Microbial fuel cell & microbial electrolysis cell models (Adapted ...
Chapter 17
Figure 17.1 General overview on classification of e-waste.
Chapter 18
Figure 18.1 Worldwide problem of electronic garbage.
Figure 18.2 Electronic waste management challenges in India.
Figure 18.3 E-waste recycling industry’s unorganized sector.
Chapter 19
Figure 19.1 Concept of green computing.
Figure 19.2 E-waste.
Figure 19.3 Recycling e-waste.
Figure 19.4 Green computing approaches.
Figure 19.5 Steps of e-waste management.
Figure 19.6 E-waste impact on the environment and human beings.
Figure 19.7 Sustainable development goals (SDGs).
Guide
Cover Page
Table of Contents
Series Page
Title Page
Copyright Page
Foreword
Preface
Begin Reading
Index
Also of Interest
WILEY END USER LICENSE AGREEMENT
Pages
ii
iii
iv
xvii
xviii
xix
xx
xxi
xxii
xxiii
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
Scrivener Publishing100 Cummings Center, Suite 541JBeverly, MA 01915-6106
Publishers at ScrivenerMartin Scrivener ([email protected])Phillip Carmical ([email protected])
Sustainable Management of Electronic Waste
Edited by
Abhishek Kumar
Pramod Singh Rathore
Ashutosh Kumar Dubey
Arun Lal Srivastav
T. Ananth Kumar
and
Vishal Dutt
This edition first published 2024 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© 2024 Scrivener Publishing LLCFor more information about Scrivener publications please visit www.scrivenerpublishing.com.
All rights reserved. No part of this publication may be reproduced, stored in a retrieval system, or transmitted, in any form or by any means, electronic, mechanical, photocopying, recording, or otherwise, except as permitted by law. Advice on how to obtain permission to reuse material from this title is available at http://www.wiley.com/go/permissions.
Wiley Global Headquarters111 River Street, Hoboken, NJ 07030, USA
For details of our global editorial offices, customer services, and more information about Wiley products visit us at www.wiley.com.
Limit of Liability/Disclaimer of WarrantyWhile the publisher and authors have used their best efforts in preparing this work, they make no representations or warranties with respect to the accuracy or completeness of the contents of this work and specifically disclaim all warranties, including without limitation any implied warranties of merchant-ability or fitness for a particular purpose. No warranty may be created or extended by sales representatives, written sales materials, or promotional statements for this work. The fact that an organization, website, or product is referred to in this work as a citation and/or potential source of further information does not mean that the publisher and authors endorse the information or services the organization, website, or product may provide or recommendations it may make. This work is sold with the understanding that the publisher is not engaged in rendering professional services. The advice and strategies contained herein may not be suitable for your situation. You should consult with a specialist where appropriate. Neither the publisher nor authors shall be liable for any loss of profit or any other commercial damages, including but not limited to special, incidental, consequential, or other damages. Further, readers should be aware that websites listed in this work may have changed or disappeared between when this work was written and when it is read.
Library of Congress Cataloging-in-Publication Data
ISBN 9781394166176
Front cover images supplied by Pixabay.comCover design by Russell Richardson
Foreword
Background
This book gives a better understanding of how the management of e-waste systems functions. This book provides comprehensive, state-of-the-art coverage of all the essential aspects of modeling and simulating physical and conceptual systems. Various real-life examples show how simulation is crucial in understanding real-world systems. Practical uses of AI and data mining techniques are presented to show successful applications of the modeling and simulation techniques given.
Objectives
The number of researchers studying e-waste and trends for e-Waste management is significant due to their attractiveness in the challenges they pose for researchers and their applications for the benefit of mankind. However, due to the overlapping of the two domains, which maintain their distinct culture, providing tools for this overlap to be appreciated by both communities has become more than necessary. This book provides one such opportunity, providing a platform of appreciation for both communities.
The book collects important research articles from experts in the field after a thorough screening process to provide a collective effort to enhance the contributions of the combined communities. It is based on a wealth of years of research from dedicated researchers.
About the Book
The book has a collection of research articles from different authors from all over the world. It truly represents the uniqueness of the research as a common platform from diverse backgrounds and cultures.
The book starts with a chapter on the contribution of renewable energy and artificial intelligence, a critical topic. The second chapter applies to current trends in e-waste management, a standard tool employed in AI. As seen previously, the traditional method used is machine learning. The third chapter deals with current e-waste management through an exploratory study on managing e-waste for environmental sustainability. The sixth, eighth, tenth, and eighteenth chapters deal with e-waste management issues and challenges. Chapter 5 deals with the paradigm of machine learning that includes deep learning and provides some useful case studies. Chapter 9 extends this paradigm and discusses big data, a common buzzword associated with health data. AI associated with diagnostics has provided smart tools leading to smart informatics; Chapter 10 deals with this as applied to health. Chapters 14 and 15 give a useful resource on the impact of e-waste on reproduction. Chapter 16 comprehensively challenges the scale-up of the bio-hydrometallurgical treatment of electronic waste from laboratory-based research to practical industrial application. Chapter 17 provides one of the essential benefits of current electronic waste recycling advances. Chapter 18 discusses e-waste's addition to our ever-growing hazardous solid waste. Finally Chapter 19 discusses e-waste management as crucial to the development of green computing.
Preface
This book is organized into nineteen chapters. In Chapter 1, a study explores the published works of AI with EMS, RES, and SG, their effects, and their reasons. AI technology has evolved quickly in the last several decades, and its applications have rapidly increased in modern industrial systems. Similarly, nature-inspired and biological systems denote an unlimited inspiration source for developing technical systems that lead human civilization's progress and shape our thinking style.
In Chapter 2, the author introduces the concept of the progress of artificial intelligence and blockchain technology unlocking prospects toward thermal marketing accuracy. The Artificial Neural Network (ANN) is crucial for improving biomass energy prediction research. This study emphasizes the steps in modeling and using ANN in forecasting biomass thermal values. Several research gaps in the present state of the investigation on ANN, in terms of biomass and guidance for additional research, are identified.
Chapter 3 will also go over growing environmental concerns over the use and depletion of non-renewable fuel sources, together with the increasing price of oil and instabilities in the oil markets, which have recently stimulated interest in producing sustainable energy sources in the form of biofuels derived from plants. Ethanol developed from lignocellulosic biomass has characteristic benefits of safety, economics, and being more environmentally friendly than fossil fuels. The lignocellulosic materials comprise 50% cellulose, 25% hemicellulose, and 25% lignin.
In Chapter 4, a novel approach for a Smart Home Energy Management System for Energy Consumption Prediction is proposed using an Enhanced Bayesian Linear Regression Machine Learning algorithm (EBLRML). The residual sum of squares shows the significant linear model difference from the existing application of ML techniques where the coefficient based on correlation is lower, and the energy consumption is calculated to achieve the best-fit model.
In Chapter 5, the author explains that using artificial intelligence today improves all the limitations outlined in the problem statements above. Machine learning algorithms have been used to predict crop cultivation, optimization metrics, irrigation levels, and so on, but very little has been done to predict weather and forecast agriculture. By using advanced sensors connected to a network, it is possible to identify bad weather that may threaten agriculture itself at an earlier stage. A live farm's features can improve crop exploitation earlier in the growing cycle.
In Chapter 6, the author explains that energy consumption has risen exponentially, putting pressure on the utilities to increase energy production. One of the significant concerns of the current day is that energy conservation and monitoring systems have been developed to optimize the increasing demand for energy and its consumption. Energy management systems help to decrease current consumption, prevent energy wastage, and enable the optimized utilization of available resources.
In Chapter 7, the author explains a significant impediment to the widespread adoption of these energy sources in their high integration costs. However, artificial intelligence (AI) explanations and data-intensive expertise are currently being deployed in various sectors of the electrical significance chain and, given the future smart grid's increasing complexity and data generation capacity, have the probability of adding substantial value to the system.
Chapter 8 provides detailed explanations of critical issues influencing India's entire e-waste value chain, including a lack of data inventorization, unlawful disposal, and treatment choices. As a result, this study focuses on strategic interventions that comply with existing legislation and are necessary for a long-term e-waste value chain, secure resources, social well-being, reduced environmental consequences, and overall sustainable development. Apart from these, other methods for recycling e-waste are also incorporated to maintain the environment's integrity.
Chapter 9 discusses the environmental concerns linked with discarded electronic equipment, sometimes known as “e-waste,” in detail. In addition, the development of e-waste both now and in the future, as well as any environmental difficulties that may come from its management and disposal approaches, are investigated, and the existing programs for the management of e-waste are discussed.
Chapter 10 discusses electronic devices, such as televisions, smart-phones, and refrigerators, that have limited useful lives and must therefore be replaced frequently, creating e-waste. E-waste has the fastest growth rate among municipal solid garbage, producing 20 to 50 million tons annually globally. As a result, several nations are currently managing enormous amounts of e-waste. An essential issue in handling e-waste is environmental health. Worldwide, governments struggle to increase public awareness and make significant steps to protect the environment from rapid degradation. Because of the reasons above, effective e-waste management is required constantly.
Chapter 11 discusses the goals pertaining to the disposal of devices nearing the end of their “useful life.” Increasing transboundary secondary resource movement and Asia's rapid economic growth will require both 3R initiatives (reduce, reuse, recycle) in every nation and effective management of the global material cycle. As a result, electrical and electronic garbage management, or “e-waste,” has gained significant attention. For material processes from the national and international environmental and resource preservation perspectives, digital goods are at an all-time high in the developing digital world, and it is difficult to envision our everyday lives without them. Although they are most significant throughout their lifetime, they could endanger the environment if burned or disposed of in landfills.
Chapter 12 will include information on how the overall amount of trash managed locally and worldwide may be decreased by improving the lifecycle management of electronics by reducing the source of materials consumed, enhancing reuse, restoration, extending product life, and recycling electronics. The EPA's waste management hierarchy aligns with the life cycle approach. The hierarchy underlines that reducing, reusing, and recycling is essential components of sustainable materials management and grades the different management options from greenest to least green.
Chapter 13 discusses that e-waste is a significant problem in the technological world. To put it simply, e-waste refers to any unwanted or obsolete electronic equipment. Obsolete technology is inevitably phased out, reducing amounts of WEEE garbage. Large appliances and electronics such as refrigerators, air conditioners, computers, and mobile phones are all broken. This trash has the potential to kill humans. Humans and ecosystems alike are negatively affected by improper waste management. Polluting the environment by incinerating, burying, or dumping electronic waste is unacceptable. Rare earth elements used in reusable electronics are reusable.
Chapter 14 highlights modern developments in e-waste production and streams, current recycling technologies, human health, and environmental impacts of recycled materials and processes. The background, challenges, and problems of e-waste disposal and its proper management are also discussed, along with the implications of the analysis.
Chapter 15 shows associations between exposure to e-waste and physical health outcomes, including thyroid function, reproductive health, lung function, growth, and changes to cell functioning. Several researchers have investigated the consequences of pregnancies in communities exposed to e-waste. In most investigations, there have been consistent effects of exposure with increases in spontaneous abortions, stillbirths, premature deliveries, lower birthweights, and birth durations, despite diverse exposure settings and toxins being examined.
Chapter 16 will assess peer-reviewed data gathered to establish the technology readiness level of biohydrometallurgy for material recovery from e-waste at a pilot scale, concluding that bioleaching at a commercial scale currently faces diverse operational challenges that hamper its scale-up and industrial implementation.
Chapter 17 presents diverse e-waste recycling technologies, including conventional (physical and chemical treatments) and modern (biological or microbial treatments) approaches to combat environmental pollution and community health hazards. However, conventional and modern techniques suffer from multiple lacunae related to the efficacies of e-waste recycling techniques. To this end, the current literature review deals with a general outline of e-waste generation and categorization with special emphasis on e-waste recycling processes in great detail.
Chapter 18 discusses e-waste's addition to our ever-growing hazardous solid waste. Electronics and electrical equipment are part of e-waste. Many countries, especially developing countries like India, are facing infinite challenges in the management of e-waste which imported illegally or generated internally. India is also one of the countries fighting for e-waste management.
Chapter 19 discusses e-waste management as crucial to the development of green computing. We can lessen technology's adverse environmental effects and save resources by properly disposing of and recycling electronic equipment. However, for the rising issue of e-waste to be solved sustainably, governments and businesses must collaborate to develop effective e-waste management regulations and initiatives.
Dr. Abhishek Kumar
Associate ProfessorDepartment of Computer ScienceSMIEEE, Chandigarh University, India
Pramod Singh Rathore
Assistant ProfessorDepartment of Computer and Communication Engineering,Manipal University Jaipur, India
Dr. Ashutosh Kumar Dubey
Associate Professor
Department of Computer Science and EngineeringChitkara University School of Engineering and Technology,Chitkara University, Himachal Pradesh, IndiaSMIEEE, SMACM
Dr. Arun Lal Srivastav
Associate ProfessorChitkara University School of Engineering and Technology,Chitkara University, Himachal Pradesh, India
Dr. T. Ananth Kumar
Associate ProfessorComputer Science and EngineeringIFET College of Engineering,Tamilnadu, India
Vishal Dutt
Department of Computer Science and EngineeringChandigarh University, Mohali, Punjab, India
1Integration of Artificial Intelligence Techniques for Energy Management
Bhanu Chander* and Kumaravelan Gopalakrishnan
Department of Computer Science and Engineering, Pondicherry University, Karaikal Campus, Puducherry, India
Abstract
Artificial intelligence (AI) is a scientific application of knowledge used to build intellectual devices, specifically intelligent computer programs. Innovations in AI-based techniques are extensively applied in Energy Management Systems (EMS), Renewable Energy Systems (RES), and Smart Grids (SG) and are a cutting-edge frontier in power electronics and power engineering with powerful tools for design, control, fault diagnosis, and simulation. In particular, this study explores the published works of AI with EMS, RES, and SG, as well as their effects and reasons. AI technology has quickly evolved in the last several decades and its applications have rapidly increased in modern industrial systems. Similarly, nature-inspired and biological systems denote an unlimited inspiration source for developing technical systems that lead human civilization’s progress and shape our thinking style.
Nowadays, using renewable energy to condense climate revolution and global warming has become a growing trend and various AI-based prediction techniques have been developed to improve the prediction ability of renewable energy. An intelligent grid must predict the amount of power, integrate renewable sources, and manage an intelligent grid with optimal sizes, all of which are challenging tasks. Optimization techniques in ML also increase the maximum power output of a particular source and minimize computational costs. Parameter selection is another huge task in ML since it influences the performances of ML models. Hence, appropriate optimization and parameter selection techniques in EMS are discussed. This chapter will summarize AI techniques in EMS and RES with real-time applications. Integration of AI with SG, optimization, and parameter selection in AI-based techniques to improve the energy system are elaborated. Finally, the chapter concludes with novel research limitations and future outlooks.
Keywords: Artificial Intelligence (AI.), energy management system (EMS), smart grid, renewable energy
1.1 Introduction
At present, the abstraction of energy from renewable sources has increased. Technological advances made in sun, geothermal, water, wind, and many other natural renewable sources have gained notoriety as reasonable energy sources. Conservative energy sources such as coal, gas, and a mixture of crude oils, with their negative impression on the environment and strong connection with the respective country’s economy, have become political and financial weapons to create pressure [1-3]. So, there is a need for a replaceable and higher ratio of renewable energy sources. Harnessing energy from renewable sources has gained significant attention from the research community because of its advantages over conservative energy sources and its range for single-homes to large-scale power plants. However, renewable power plants are not fully controlled or planned due to the inherent features and power generation dependent on the ecological boundaries. For example, consider a power grid where the generation of power is created by renewable sources where capacity management alterations could impact grid physical health and quality of life [2-3]. Moreover, renewable energy sources continue to expand depending on the available sources, so it is necessary to determine grid localization, features, outlines, and optimal sizes. Management of an intelligent grid covering renewable energy plants is hard to integrate with other sources.
Energy sustainability and security are considered one of the most important challenges faced by the world. Specifically, it will be considered a leader for economic volatility. The limited fossil-based fuel sources and their adverse burning effects have boosted interest in developing sustainable energy sources. Solar thermal, wind, geothermal, biomass, tidal, and solar photovoltaic power are some of the renewable energy markets that are rapidly gaining attention. Around the world, major countries are hunting to expand their future works and share of renewables [2-5]. In conservative energy sources, the generation process and manufacturing rely on the energy demand from users; power constancy depends on demand and supply. If the demand and supply increase power-grid work quality, overall results degrade and the chances of power failure increase in some regions. If the demand and supply are less than needed, energy will be lost and the chances for futile costs will reduce. So, more research is needed to produce sufficient power at the right time and smooth, safe grid running for higher economic aid. Appropriate research suggests that sufficient capacity per the requirement minimizes energy wastage [3-6]. Most renewable power sources are incredibly flexible on ecological fluctuations.
Over the past few decades, Artificial Intelligence (AI) approaches such as statistics, mathematics, data mining, machine learning, deep learning, artificial neural networks, and optimization methods have been useful in various domains to solve data-driven problems. AI is a scientific application of knowledge to build intelligent devices, specifically intelligent computer programs. AI is generally explained as extraordinary intelligence demonstrated through learning, reasoning, and problem-solving [4-8]. With the development of various modern technologies, the world is likely to establish systems incorporated with knowledgeable human features such as the capacity to think, reason, simplify, differentiate, find meaning, and learn from previous knowledge to correct errors. AI techniques like decision-making systems, fuzzy logic, neural networks, and nature-inspired model deployment advance the progression limits in power electronics and engineering. The approaches mentioned above provide impressive designs, controls, simulation tools, error detection, and optimization in various energy-based systems. Nowadays, AI is one of the hot research topics and its applications have rapidly increased in modern manufacturing systems.
Machine Learning (ML) generally attempts to learn the relationship between the provided data (input) and the output data by employing mathematical complications. Once the ML models are trained to fit the training dataset, end-users get satisfied values and feed them to an expert system for forecasting results. Here, data pre-processing plays a crucial role in improving ML performance resourcefully. ML has three fundamental schemes: supervised, unsupervised, and reinforcement. In supervised learning, a model gains an advantage from labeled data during training, then produces the best possible results. In unsupervised learning, models inevitably classify input data points into clusters through certain principles and standards for training data that have not been categorized in advance [5-8]. For example, take clustering as an example, the amount of clusters mostly relies on the clustering principles employed. Reinforcement learning (RL) models learn from interactions with the outer atmosphere to attain feedback to capitalize on the predictable benefits. There are countless learning principles, rules, and theoretical mechanisms defined by researchers based on their application area. With rapid progression in hardware, software, and mathematical advances, a new learning method is booming: DL, which is considered a sub-field of ML. Apart from the learning ML models mentioned, DL can grasp characteristic nonlinear features and high-level invariant data formations. Thus, DL has been helpful in numerous research arenas to find adequate performances.
It is a known fact that renewable energy resources such as wind, heat, and solar light are extremely inconstant. The subsequent variations in generation capacity can cause variability in the power grid since the production of the grid plant is distinct with issues such as intensity of solar radioactivity, wind speed, and other related aspects such as solar power only being offered in the daytime. Therefore, we need to focus on power generation when the resources exist and a solution to store renewable sources for later use [6-9]. For example, sources like wind, water, and solar are incredibly hazardous and expensive to store. In addition, if the dimensions of creating natural resources are inadequate to meet demand, gas and fuel-based power plants are used to overcome the shortage issues. Many of the difficulties mentioned above push the inclusion of AI-based approaches for energy management and optimized consumption. The research community suggested numerous AI approaches depending on the requirements and characteristics of renewable energy sources.
1.2 Summary of Artificial Intelligence Techniques
The term Artificial Intelligence (AI) is well-defined as a set of high-tech structures which accurately execute numerous tasks generally associated with human beings. Like a human brain, AI approaches adapt their behaviour without direct reprogramming. In some cases, AI will reach or surpass human intelligence depending on how it accomplishes humanlike levels of cognitive functions, learning, perception, and interaction. Here, intelligence indicates the capability to absorb, study, then learn from another environment and put on that data to conditions that have, until that specific point in time, not happened. Similarly, a machine can sense information from its surrounding atmosphere and automatically decide on probable future data built on past data [5-9]. Hence, AI can be fixed as the procedure for representing absorbed intellect. However, the story behind AI started in medieval times. Between 360-300 BC, a famous researcher, Aristotle, stated the fundamental governing principles of the human brain with conceptual logic. After that, from the 15th century, numerous researchers expanded brain logic concepts.
A breakthrough was reached in 1943 when two famous research scientists, Warren McCulloch and Walter Pitts, projected an artificial neuron (neural) model. Authors who studied their research concluded that appropriately constructed neurons could learn and then make predictions. After that, in 1949, Donald Hebb came up with new rules on inter-neuron modifications and connectivity which inspired Alan Turing to design Turing Test to measure machine intelligence. Finally, in 1956, the term artificial intelligence was coined by John McCarthy. From the 1990’s, the world saw the growth and importance of intelligent neural networks, which mimics the concepts of human work. With the rapid progression in various hardware, software, and miniature sensing devices, building AI-based real-life applications became possible [3-5, 7-10]. At the end of the 19th-century, AI-based statistical learning models were employed for decision making. Here, fuzzy logic and rule-based models were highly used. From the 19th century until now, AI-based systems successfully adopted numerous real-time applications, from washing machines to controlling high-speed bullet trains. At present, AI itself is incorporated into many streams of human and industrial infrastructure.
1.2.1 Machine Learning
Machine Learning (ML) is a sub-part of AI that produces computational and flexible methods which allow the evolution of superior machine performance (See Figure 1.1). This means ML designs boundaries that allow humans to analyse the behaviour of AI schemes for practical actions. Deep learning is another concept of AI, which is described as a sub-part of ML. ML generally produces the best results with processed data, however, getting pre-processed data from real-time appliances is impossible. Here, DL with deep neural networks with many hidden layers extracts the valuable data layer by layer, then produces efficient data for decision making.
Decision Tree (DT): The decision tree is one of the prominent classification techniques in ML. The classification results in decision trees that are separated into groups of choices based on the input topographies. It builds a tree-like structure; the procedure starts from the base feature and then develops like the tree. Here, the tree’s structure is built from a base feature by distributing the source root node of the tree into sub-branch nodes. The tree partition was decided based on principles determined by the properties of the set and the target classification. The decision tree model is employed in several areas of intelligent energy management, demand, and supply of smart grids for the prediction of anomalies detection, planning and energy management, risk detection, and optimization.
Figure 1.1 Pictorial representation of machine learning approaches.
Random Forest (RS): Random Forest models are identified as an extension of decision trees that are mainly applied for categorization and regression and to progress the prediction precision of decision trees. Bootstrap collection is one of RS’s available variants, which decreases the variance of a DT by estimating a quantity from a data sample. RS algorithms work by forming a bootstrap sample of the training set, then use it to train a decision tree. Topographies are arbitrarily chosen at each node of the DT. The probability of incorrect classification is applied to select the optimal choice and the procedure continues until an efficient tree is formed. RS variant models are highly employed in structuring energy schemes to forecast hourly energy ingesting.
Wavelet Neural Network (WNN) combines wavelet analysis and neural structures. In this model, wavelet concepts considered from the generalization of windowed Fourier transform are effectively employed for predicting time series data in renewable energy sources to optimize the cost and battery.
Naïve Bayes: Bayesian theorem-based naïve Bayes calculates the likelihood of a guess (presumption) when fed with a piece of prior knowledge. The variants of the naïve model are broadly applied to solve building energy problems like analysis of building energy efficiency, weather predictions, prediction of photolytic and photovoltaic energy, and forecasts of energy usage on an hourly basis.
Artificial Neural Networks (ANNs): ANN was developed based on the human neuron structure and is broadly applied for nonlinear modeling processes. ANN is broadly applied to decision making, regression problems, natural language processing, dimension reduction, prediction, computer vision, intrusion, and anomaly detection. In the energy sector, ANN is applied to predict electricity consumption, battery optimization, cooling, and load sharing, which assists interior energy consumption by analyzing indoor climate and real-time energy monitoring.
Multiple Linear Regression (MLR): MLR is a statistical linear regression model widely applied for predicting smart buildings’ heating and cooling processes and non-linear energy demand functions. The principal objective of MLP-based models is to detect functions from the analysis of training data such as weather conditions, the impact of solar radiation, and humidity.
Logistic Regression (LR): Logistic Regression was designed with the statistical logistic function acknowledged as the sigmoid function. LR is mainly employed for classification events requiring likelihood occurrence forecasts. LR in the energy sector estimates weather predictions, fault detection, energy consumption, etc.
Genetic Algorithm (GA): Genetic algorithms work extensively for a heuristic search approach employed extensively in complex models because of their capacity to deal with non-linear topographies. In addition, GA models efficiently solve constrained and unconstrained optimization issues. Hence, GA variants are extensively useful in the scheduling of housing power loads to diminish the total energy cost in a dynamic pricing system.
Fuzzy Logic (FL): FL is a formula where the truth values of variables might be any actual number between 0 and 1 or comprehensively. Its primary function is to handle the perception of partial truth, where the truth value may choose between values that are totally true or false. Because of its simple design and complexity, FL is adopted in many more appliances than other models. FL in the energy sector is used in prediction, optimization, fault detection, data anomaly, and intrusion detection.
Particle Swarm Optimization (PSO): PSO is a nature-inspired metaheuristic optimization approached design built on the cooperation of birds and fish. PSO employs particles of swarms crisscrossing in a multidimensional exploration to find the optimum position. Every particle has a probable solution influenced by the involvement of its neighbour particles. PSO is employed for the optimal solution in hybrid ML approaches for energy estimation, optimization, supply, and demand.
K-Nearest Neighbour (KNN): The KNN model is trained with available datasets that then estimate the label of a new example based on the tags of its closet neighbours in the training set. KNN helps build energy management, demand, and supply, as well as monitors energy consumption in a particular time region.
Principal Component Analysis (PCA): PCA is an unsupervised ML model used to reduce dimensionality by plotting a particular function from higher-dimensional into lower-dimensional space. PCA methods in the energy sector are used to predict carbon dioxide emission, progress the housing load disaggregation of energy, and analyse power ingesting.
Hybrid Models (HM): HM models are a collection or combination of multiple ML algorithms. HM is mostly applied for data pre-processing and solving optimization issues. Day-based power predictions and generation, energy consumption, reduced energy model complexities, and power data cleaning are some areas where HM is applied in the energy sector.
1.2.2 Deep Learning (DL) Techniques
DL is considered as a sub-section of ML and is defined as a representation learning model that learns through numerous hidden layers and levels of representation that obtain results when each transforms the representation at one level. Shallow models consist of 1-to-3 levels of non-linear operations, whereas DL consists of more than 4 levels. DL-based techniques are broadly applied for classification issues and their usage in the past few years in various research fields has grown. Some reasons force us to employ DL in various research fields, for example DL with numerous hidden layers can handle large datasets, improve model performance, and have feature extraction capabilities. DL models with numerous hidden layers easily cope with large datasets and extract high-rated features to make accurate predictions. In addition, unlike orthodox ANNs, DL models can hold and store more information within the neurons.
Autoencoder (AE): An auto-encoder (AE) is a representation-based neural network with many unseen hidden layers. AE involves two functions: encoding and decoding. AE aims to absorb a representation of input data through training and then reconstruct the output. For example, if A is the input dataset, the encoder designs the input data to a representation or illustration of hidden layer h = f(x), and the decoder gives a hidden representation or illustration to construct the output g(h). Naturally, AE copies are helpful for dimensionality reduction/feature finding for training features in big datasets.
Recurrent Neural Networks (RNN): RNN is a classification-based neural network specifically applied in classification data. It uses a response/feedback loop which is associated to their previous calculations. RNN stores the feedback/response information in its memory database and uses it for previous outputs over time. In time series-based RNN models, the value calculated at a particular time (t) is affected by the standards impressed in the previous steps (t-1). RNN can study the model and time-based behaviours are revealed inside the time-series data. Then, the response values are used to recall the preceding steps. RNN and its variants like gated recurrent unit (GRU) and Long-short-term-memory (LSTM) are successfully applied to predict building energy, weather predictions, and power generation.
Convolutional Neural Networks (CNN): CNN models are experts in processing incoming data features with grid-shaped structures. These kinds of models are mainly applied for analyzing large-quantity complex datasets. A single CNN model contains four major parts: (a) a convolutional layer builds the feature maps of the input data; (b) next, a pooling layer is employed to decrease the dimensionality of complicated features; (c) a flattering-based technique is applied to adjust the data into a column vector; and (d) finally, a completely allied hidden layer estimates the loss function.
Deep Belief Networks (DBN): DBM consists of algorithms based on probability and unsupervised learning algorithms to produce the outputs. Table 1.1 described the classification and comparison of AI, ML, and DL applications in energy sector. The restricted Boltzmann machines (RBM) are a fundamental characteristic of the DBN. The RBM is a light two-layer neural net used to study likelihood circulations over its input data space so that its formation can display needed resources. The 1st layer of the RBM is called the visible/input layer and the 2nd is the hidden layer. DBN is broadly applied for data size decrease, feature learning, regression, classification, and collaborative filtering.
Table 1.1 Classification and comparison of AI, ML, and DL applications in energy sector.
Model
General application domain
Application domain in energy sector
Advantages
Disadvantages
Decision Trees
Classification
Building energy management, energy storage planning, refining operative efficiency
Practically accurate, reasonable speed, scalable
Complex, low user-friendliness
Linear Discriminative Analysis (LDA)
Classification
Energy prediction and power- data analysis, demand response management
Good speed and precision
Low accessibility
ANN
Modelling, prediction, and curve-fitting of non-linear progressions
HVAC energy consumption modelling, demand response management, failure probability modelling, smart grid management, sector coupling
High precision, intelligent speed, decent for noisy data
Extremely complex, low availability
SVM
Data arrangement, high-accuracy predictions
Building energy consumption and prediction, dynamic energy management, security and theft detection
High precision, scalable
Highly complex, low accessibility, low speed
Random Forest
Event estimating, data organization
Energy consumption forecasting, power-data analysis
Practically accurate, diminishes over-fitting
Reasonably compound, low accessibility, little speed
Deep Learning
Data prediction, pattern modelling
Energy efficient system design and modelling, dynamic energy management, security and theft detection
Reasonably user-friendly, high precision, moderate speed, best classifier interactions
Highly complex
Mixture Discriminative Analysis (MDA)
Dependability analysis, classification
Demand response management, preventive equipment analysis
Highly user-friendly, simple structure, high speed, feature dependencies
Low accessibility
WNN
Time series event calculation
HRES operating cost optimization, wind and solar power prediction
High accuracy, scalable
Low speed, low user-friendliness, reasonably complex
Fuzzy Logic
Control claims
Power point tracking, control and monitoring, outage prediction, preventive equipment analysis
Almost user-friendly, nearly accurate, high speed
Reasonably complex
Least Absolute Shrinkage and Selection Operator (LASSO)
Dependability analysis, classification
Power-data analysis, dynamic energy management, security and theft detection
Highly user-friendly, simple structure, high speed, feature dependencies
Low speed, low user-friendliness, reasonably complex
Multi-Dimensional Scaling (MDS)
High-accuracy predictions, dependability analysis, classification
Demand response management, preventive equipment analysis
Extremely user-friendly, modest structure, high speed, feature dependencies
Low accuracy
Hybrids
High-accuracy forecasts
Load prediction, energy generation foretelling, refining operational efficiency
High speed, generality, accuracy
Practically complex
Regression
Forecast of the possibility of occurrence
Energy ingesting and predicting, power-data analysis
Highly user-friendly, simple structure, high speed, feature dependencies
Little precision
Auto-encoder
Data representation, error calculations
Outage prediction, preventive equipment analysis
High speed, high accuracy, good generality
Reasonably composite
Convolution Neural Network (CNN)
Classification, high-accuracy predictions
Outage prediction, preventive equipment analysis, demand response management
High speed, high accuracy, high speed, good generality
Highly complex, low availability
Long-Short- Term-Memory (LSTM)
Classification, time-series analysis
Demand response management, outage prediction, preventive equipment analysis, failure probability modelling, smart grid management, sector coupling
Highly user-friendly, simple structure, high speed, feature dependencies
Highly complex, low availability
Recurrent Neural Network (RNN)
High-accuracy predictions, time-series analysis
Outage prediction, preventive equipment analysis, failure probability modelling, smart grid management, sector coupling
Highly user-friendly, simple structure, high speed, feature dependencies
Highly complex, low availability
Deep Belief Network (DBN)
High-accuracy predictions
Demand response management, dynamic energy management, security and theft detection
High speed, high accuracy, high speed, good generality
Highly complex, low availability
Stacked Generalization (Blending)
High-accuracy predictions, optimization
Power-data analysis, demand response management
Extremely user-friendly, modest structure, high speed, feature dependencies
Low accessibility
Genetic Algorithm
Problematic optimization
Optimum load scheduling, smart grid management, sector coupling
High precision, used in hybrid mode
Low-slung speed
Multivariate Regression Analysis
Classification, clustering, data reduction
Power-data analysis, dynamic energy management, security and theft detection
Highly user-friendly, simple structure, feature dependencies
Low availability and low speed
PSO
Problem optimization
Operational cost optimization and energy forecast
High accuracy, used in hybrid mode
Low speed, low convergence rate, fall in local optimum
KNN
Classification, forecast
Building energy ingesting study
High speed, user-friendly
Low accuracy
Bagging
Problem optimization, prediction
Demand response management
High speed, accuracy, good generality
Low accuracy
Naïve Bayes
Calculating the probability of occurrence
Building energy efficiency study, energy generation forecast
High speed, user-friendly
Low accuracy
K-Medoids
Classification, clustering
Preventive equipment analysis, sector coupling, demand response management
Practically accurate, reasonable speed, scalable
Low convergence rate, fall in local optimum
Principal Component Analysis
Data reduction
Failure probability monitoring, power-data analysis
Highly user-friendly, Simple structure, High speed, feature dependencies
Low convergence rate, fall in local optimum
Self-Organising Map (SOM)
Classification, clustering, data reduction
Operating cost optimization and energy scheduling, smart grid management
High speed, high accuracy, high speed, good generality
Low speed
Least Angle Regression (LARS)
Data reduction, classification
Outage prediction, demand response management, smart grid management
Highly user-friendly, Modest structure, High speed, feature dependencies
Low speed
Hierarchical Clustering
Classification, clustering, data reduction
Dynamic energy management, improving operational efficiency, power consumption analysis
Practically accurate, Reasonable speed, Scalable
Low accuracy
1.3 Reasons for Applying AI in EMS
Here, we mentioned the articles that inspired the integration of AI and ML approaches in the energy sector. Mondal et al.[9] designed a game theory-based energy management approach for smart grids and simulation results show models that maximize advantages in cost and Negeri supply. Elseid et al.[10] proposed energy management in a smart grid, which automatically optimizes energy demand. Leonori et al.[11] fabricated an adaptive neural fuzzy interference scheme by employing Echo-state networks as a series analyst. The authors tried to minimize energy exchange with the smart grid and experimental results show that the model achieved over 30 percent prediction for a particular time. De Santis et al.[12] presented an interconnected fuzzy logic-based Mamdani scheme for energy supervision in a smart grid. The authors focused on decision-making based on energy management and storage tasks. Venaygamoorthy et al.[13] reduced carbon emissions using evolutionary adaptive dynamic programming and a neural learning scheme. Model performance analyzed battery life and renewable energy load. Ma et al.[14] fabricated game-theory-based leaders and followers for accurate energy management, maximizing the benefits of active consumers and ensuring the optimal distribution in the smartgrid. Arcos-Aviles et al.[15] designed a low-complexity-based fuzzy logic controller for intelligent buildings and residential grids. Aldaouab et al.[16] proposed a genetic scheme-based optimization for commercial smart grids and primarily used micro-turbines and diesel generators. Liu et al.[17] presented the Stackelberg model for energy management. Nnamdi and Xiaohua [18] designed an incentive-based demand-supply model for energy management in a smart grid for analyzing connectional grid operations. The experimental results demonstrate that the proposed model maximizes the grid operations and minimizes the furl transaction costs.
1.4 ML in Renewable Energy
Due to the excessive usage of fossil fuels to increase economic growth, it has been documented that extreme consumption of fossil fuels will not only fast-track the decrease of power sources but also negatively impact the environment. Most of the effects on human health threaten global climate change. Hence, renewable energy, which can quickly be recovered or reproduced with its characteristics and low environmental pollution, has attracted attention from research communities. With the development of renewable energy systems, most of the current energy problems like consistency of energy source and solving regional energy supply need to be addressed. High-rate accuracy of energy monitoring can progress the efficacy of the energy organization. Hence, power prediction plays an energetic role in energy organization approaches. Numerous studies prove that AI-based approaches have been applied for renewable-energy forecasts. In addition, hybrid-AI-ML technologies increase the prediction accuracy of renewable energy with periodical intervals like months, weeks, days, hours, and minutes. Prediction accuracy and proficiency is characteristically operated to estimate the performance of AI-ML-DL models in renewable-energy predictions.
Nowadays, technologies like AI and ML provide a valuable contribution, opportunities for energy management and cooperation, and a chance for investors to implement effective, creative methods for more significant assets and compassionate energy conversion. ML is a kind of technology that can categorize through a set of rules and data in such a way that it studies and expands its methods through improved knowledge/practice. Industrial experts and data specialists state that AI will emerge in the future of energy management. Numerous AI-based approaches are already theoretically considerable for potential changes in the energy sector. Investments in the AI-based energy sector will surpass over 800 billion US dollars by the end of 2025. It is difficult to overlook the opportunities for development ML provides. Many industrial companies are now integrating AI and ML into their company tactics to catch the promise it offers.
The reports mentioned that the energy sector denotes around 2% of capital for AI in Europe and it specifies that there is still much room for development. The AI and ML industries are household to many start-ups with plenty to offer, and companies and industries are primed to take primary steps towards future AI technology. China invested nearly 400 billion US dollars in the AI landscape and 200 million in emerging technologies like energy and smart grid software. The UK government invested 130 million in 2020 and the US invested 50 million in funding for the energy department for AI and ML-based energy techniques. From the above information, there are plenty of investment opportunities which are expected to increase soon. The number of appliances for ML and AI in the energy management sector is practically limitless. Now, it is more a matter of discovering which requests will be the most beneficial, maintainable, and money-making. This will be predominantly accurate for solar and wind power, historically hampered by meteorological conditions and patterns that are hard to forecast and have numerous variable quantities to study.
1.4.1 ML for Renewable Energy Applications
Solar Energy:
