Sustainable Smart Homes and Buildings with Internet of Things E-Book

• Herausgeber: John Wiley & Sons
• Kategorie: Geisteswissenschaft
• Sprache: Englisch

Seitenzahl: 470

Veröffentlichungsjahr: 2024

Beschreibung

Written and edited by a team of experts in the field, this exciting new volume explores the real-world applications and methods for using Internet of Things (IoT) to make homes and buildings smart and sustainable and to continue working toward a “greener” world.

Sustainable Smart Homes and Buildings with Internet of Things (IoT) is a book that explores the integration of renewable energy sources and IoT technology in the design and management of smart homes and buildings. The book covers various topics related to the subject, including energy efficiency, real-time monitoring, control and optimization of renewable energy sources, smart grid integration, energy storage systems, and microgrids.

The book explains how IoT technology can be used to collect data from various sensors and devices installed in smart homes and buildings to create a real-time monitoring and control system for renewable energy sources, which can help optimize energy usage and reduce waste. It also discusses the challenges and opportunities associated with the integration of renewable energy sources in smart homes and buildings, and how these challenges can be addressed through the use of IoT technology.

The book is intended for architects, engineers, building managers, energy professionals, and researchers interested in the design and management of sustainable smart homes and buildings. It provides practical insights, case studies, and examples that illustrate the benefits of using renewable energy sources and IoT technology to create energy-efficient, environmentally friendly, and comfortable living spaces.

Leseprobe

Table of Contents

Cover

Table of Contents

Series Page

Title Page

Copyright Page

Preface

1 Development of a Framework to Integrate Smart Home and Energy Operation Systems to Manage Energy Efficiency Through AI

1.1 Introduction

1.2 Research Idea Definitions

1.3 Algorithms for Intelligent Models

1.4 Analyzing and Implementing

1.5 Conclusion

Bibliography

2 Development of a Hybrid System to Make the Decision and Optimization of Renewable Energy Sources

2.1 Introduction

2.2 Related Work

2.3 Methods of Modelling

2.4 Methodology

2.5 Discussion and Result

2.6 Conclusion

Bibliography

3 IoT-Based Renewable Energy Management Systems in Apartment

3.1 Introduction

3.2 Smart House Design Using Internet of Things

3.3 Problem Statement

3.4 The Proposed Methodology

3.5 A Mathematical Framework

3.6 Optimize Design

3.7 Discussion and Results

3.8 Conclusion

References

4 Framework of IoT-Based Meta Firewall System to Plan the Renewable Energy Consumption in Smart Homes or Buildings

4.1 Introduction

4.2 Problem Formulation and System Model

4.3 Meta-Control Firewall Plus (IMCF+)

4.4 Architecture of the IMCF+ System

4.5 Trial Methods and Assessment

4.6 Conclusion

Bibliography

5 Manage and Optimization of Renewable Energy Consumption Efficiency for Smart Homes

5.1 Introduction

5.2 Proposed Method

5.3 Results

5.4 Discussion

5.5 Conclusion

References

6 Cost and Renewable Energy Management by IoT-Oriented Smart Home Based on Smart Grid Demand Response

6.1 Introduction

6.2 Methodology

6.3 System Design

6.4 Results

6.5 Conclusion

Bibliography

7 IoT-Based Smart Green Building Energy Management System

7.1 Introduction

7.2 Methodology

7.3 Results of Construction

7.4 Working Model

7.5 Results of Testing

7.6 Conclusion

References

8 The Framework of IoT-Based Paradigms to Renewable Power Utilization and Distribution by Microgrid

8.1 Introduction

8.2 Related Work

8.3 Intelligent Power System Design

8.4 Daily External Energy Requirements

8.5 Conclusion

Bibliography

9 Machine Learning-Based Swarm Optimization for Residential Demand-Based Electricity

9.1 Introduction

9.2 Relevant Works

9.3 The Motivation

9.4 Energy Optimization Proposal

9.5 Discussions and Results

9.6 Conclusion

References

10 Integration of Intelligent System and Big Data Environment to Find the Energy Utilization in Smart Public Buildings

10.1 Introduction

10.2 Methods and Materials

10.3 Results

10.4 Discussions

10.5 Conclusion

Bibliography

11 Multi-Objective Optimization Process to Analyze the Renewable Energy Storage and Distribution System from the Grid

11.1 Introduction

11.2 Review of Literature

11.3 Work Proposal

11.4 Results and Discussion

11.5 Conclusion

References

12 Deep Learning and Multi-Horizontal Solar Energy Forecasting of Different Weather Conditions in Smart Cities

12.1 Introduction

12.2 Description of Data

12.3 Information Preparation

12.4 Procedures and Assessment

12.5 Results

12.6 Conclusion

Bibliography

13 Machine Learning Models are Used to Analyze the Effectiveness of Daily Residential Area Energy Consumption

13.1 Introduction

13.2 Intelligent Energy Systems for the House

13.3 Advanced Plan for Demand Response

13.4 Results

13.5 Conclusion

Bibliography

14 Integration of AI and IoT Used to Manage and Secure the Renewable Energy Management in the Environment

14.1 Introduction

14.2 Smart IoT Device Setting Out and Energy-Saving Equipment

14.3 Key AI-Based Energy-Efficient Network Issues

14.4 AI-Based Managing Framework for Multidimensional Smart IoT Devices

14.5 Research Futures

14.6 Conclusion

References

15 Hybrid Genetic Optimization and Particle Swarm Optimization for Enhanced Electricity Demand Forecasting Using Artificial Neural Networks

15.1 Introduction

15.2 Electricity Sector

15.3 Methodology

15.4 ANN-GA-PSO Methods

15.5 Results

15.6 Conclusion

References

16 Harmonizing Renewable Energy, IoT, and Economic Prosperity: A Multifaceted Analysis

16.1 Introduction

16.2 Literature Survey

16.3 Proposed Methodology

16.4 Conclusion

References

17 An Optimized Demand for Cost and Environment Benefits Towards Smart Residentials Using IOT and Machine Learning

17.1 Introduction

17.2 Literature Review

17.3 Key Considerations for Implementing Machine-Based Learning Algorithms in Smart-Based Systems

Conclusion

References

18 IoT-Enabled RBFNN MPPT Algorithm for High Gain SEPIC Converter in Grid-Tied Rooftop PV Applications

18.1 Introduction

18.2 Related Works

18.3 Proposed System

18.4 Results and Discussion

18.5 Conclusion

References

Index

Also of Interest

End User License Agreement

List of Tables

Chapter 2

Table 2.1 Literature works.

Table 2.2 Analyzing the suggested model of similar models.

Chapter 3

Table 3.1 The classification and variables of domestic appliances.

Chapter 4

Table 4.1 Carbon dioxide (CO

2

) emissions from power production, as provided by...

Table 4.2 Return flight CO

2

emissions.

Table 4.3 Assessing the household system for FE, FΓ, and FCE.

Table 4.4 Personal FCE (comfort error) among residents.

Table 4.5 University campus system evaluation: FE, FΓ, and FCE.

Chapter 6

Table 6.1 Technical specs for NodeMCU.

Table 6.2 Technical specs for arduino pro mini.

Chapter 7

Table 7.1 DPM data spreadsheet variables.

Table 7.2 Each smart room’s appliances and power consumption.

Chapter 8

Table 8.1 The specifications pertaining to standard household appliances.

Table 8.2 Price of energy.

Chapter 9

Table 9.1 Four levels of time-of-use.

Chapter 10

Table 10.1 Machine learning outcomes.

Table 10.2 Predictors used by the best ML models.

Chapter 11

Table 11.1 RES-GRID requirements.

Chapter 12

Table 12.1 Sheet for local weather sensors.

Table 12.2 Technical details of a pyrheliometer.

Table 12.3 Details chosen at random from the CWB.

Table 12.4 Results of CWB data hyperparameter adjustment.

Table 12.5 Historical LWS hyperparameter tweaking results.

Table 12.6 Hybrid data hyperparameter tweaking.

Chapter 13

Table 13.1 Control rating for non-shiftable applications.

Table 13.2 Priority shiftable appliance power rating.

Chapter 15

Table 15.1 Network data of the ANN.

Table 15.2 ANN-GA-PSO model summary.

Chapter 16

Table 16.1 Comparative analysis of existing models.

Chapter 18

Table 18.1 Parameters specifications.

List of Illustrations

Chapter 1

Figure 1.1 Sensor for each network area.

Figure 1.2 Situation types according to detector.

Figure 1.3 Smartphone detector.

Figure 1.4 Smart appliances for each area network.

Figure 1.5 Service structure for IE2S.

Figure 1.6 Smart appliances incorporate IAT and IST.

Figure 1.7 IAT algorithm.

Figure 1.8 IE2S algorithm.

Figure 1.9 IST algorithm.

Figure 1.10 Arduino board on the smart home’s sensor board.

Figure 1.11 Seoul city weather service chart.

Figure 1.12 1000 times learning process.

Figure 1.13 Executing test on learned program results.

Figure 1.14 Final findings chart.

Chapter 2

Figure 2.1 A schematic showing the hybrid power system under study. Photovolta...

Figure 2.2 Block schematic of a data and control monitoring system. Often abbr...

Figure 2.3 Simulated model in simulink software.

Figure 2.4 Proposed approach flowchart.

Figure 2.5 An integrated flowchart of MATLAB and the Arduino IDE for code gene...

Figure 2.6 Intergrating WIFI module.

Figure 2.7 MATLAB photovoltaic voltage.

Figure 2.8 MATLAB photovoltaic current view.

Figure 2.9 MATLAB photovoltaic current view.

Figure 2.10 MATLAB display of battery voltage.

Figure 2.11 MATLAB display of battery current.

Figure 2.12 MATLAB display of SOC.

Figure 2.13 MATLAB display of battery power.

Figure 2.14 Voltage from a steady wind, as shown in MATLAB.

Chapter 3

Figure 3.1 IOT-smart house operation.

Figure 3.2 Home ZigBee network.

Figure 3.3 IoT layers for the suggested technique.

Figure 3.4 Mathematics model description.

Figure 3.5 The mechanism of demand response shift.

Figure 3.6 The daily workload allocation for each kind of load.

Figure 3.7 The total daily load curve before the implementation of the suggest...

Figure 3.8 Grid hourly pricing.

Figure 3.9 Mansoura daily sun radiation prediction.

Chapter 4

Figure 4.1 Monthly consumption patterns.

Figure 4.2 System overview for IMCF+.

Figure 4.3 Integration of IMCF+ software library with openHAB home automation ...

Figure 4.4 Light-level and temperature histograms for the apartment dataset an...

Figure 4.5 Evaluation of performance includes comfort error (FCE), energy cons...

Figure 4.6 k-opt Evaluation: evaluation of comfort error (FCE), CO

2

emission (...

Figure 4.7 Study on energy conservation: evaluation of comfort error (FCE), CO...

Chapter 5

Figure 5.1 CPS architecture.

Figure 5.2 Comparing the RMSE and R2 values of several models.

Figure 5.3 Suggested appliance energy prediction.

Figure 5.4 Proposed energy optimization method.

Figure 5.5 Feature importance values.

Figure 5.6 RMSE results for February, March, and April.

Chapter 6

Figure 6.1 Pin layout of NodeMCU.

Figure 6.2 Pinout of a pro mini arduino.

Figure 6.3 Current sensor ACS712-30A.

Figure 6.4 One-phase voltage sensor, model ZMPT101B.

Figure 6.5 The structure of a structural equation modelling (SEM) system.

Figure 6.6 The module for power measurement and control.

Figure 6.7 Phase of testing for the power measuring and control module.

Figure 6.8 Procedures for voltage calibration of a power measuring and control...

Figure 6.9 Connecting NodeMCU with NRF24L01 RF module.

Figure 6.10 The average daily energy use before the implementation of the SEM ...

Figure 6.11 The average monthly energy use before installing an SEM system.

Figure 6.12 Time-of-use energy pricing.

Figure 6.13 The cost of monthly energy use before the implementation of the SE...

Figure 6.14 Using the SEM system, the average daily energy usage.

Figure 6.15 The amount of energy used by the SEM system every month.

Chapter 7

Figure 7.1 Proposed smart building EM system block diagram.

Figure 7.2 Power consumption measurement.

Figure 7.3 System’s flowchart.

Figure 7.4 Creation of DPM historical data flowchart.

Figure 7.5 Block diagram of KNN power consumption predictions.

Figure 7.6 Block diagram of face recognition-based room power management syste...

Figure 7.7 IoT dashboard update block diagram.

Figure 7.8 Proposed system block diagram.

Figure 7.9 Confusion grid for facial recognition classifier.

Figure 7.10 Target function for training model.

Figure 7.11 LSTM method current A prediction.

Figure 7.12 KNN method prediction.

Figure 7.13 RF method prediction.

Chapter 8

Figure 8.1 Smart grid’s primary architectural elements.

Figure 8.2 The Internet of Things network architecture.

Figure 8.3 The specifications related to conventional home appliances.

Figure 8.4 The specifications pertaining to timed appliances.

Figure 8.5 Power distribution without microgrid.

Figure 8.6 Power distribution with microgrid.

Figure 8.7 Daily external energy needs.

Chapter 9

Figure 9.1 EM systems.

Figure 9.2 Genetic algorithm architecture.

Figure 9.3 HEM architecture module.

Figure 9.4 Appliances’ scheduling patterns divide the energy users.

Figure 9.5 BPSO algorithm.

Figure 9.6 Varied energy consumption.

Figure 9.7 DR program classifications.

Figure 9.8 Prices of ToU.

Figure 9.9 (a), (b) Traditional unscheduled load energy usage and cost user.

Figure 9.10 (a), (b) Intelligent user profile for energy cost and consumption.

Figure 9.11 (a), (b) Comparison between smart and traditional users.

Figure 9.12 Simulation results.

Chapter 10

Figure 10.1 DNN model.

Figure 10.2 MERIDA intelligent system.

Chapter 11

Figure 11.1 Schematic of the utility grid (UG) and MGs.

Figure 11.2 Operational mode classifications.

Figure 11.3 Neural networks.

Figure 11.4 Structure for neural network.

Figure 11.5 MATLAB simulink.

Figure 11.6 Rate of change. (a) RES and (b) grid-connected modes.

Figure 11.7 Predicting error (%). (a) RES method and (b) grid-coupled method.

Figure 11.8 ALO optimization method.

Chapter 12

Figure 12.1 The framework for gathering information.

Figure 12.2 Roof photovoltaic system with 32 panels and a 10-kW output.

Figure 12.3 The sky weather shot from 2–4 March 2021 with the historic photovo...

Figure 12.4 Information preparation structure.

Figure 12.5 The ANN model’s PV power projection.

Figure 12.6 The outcomes of the LSTM model’s PV power prediction.

Figure 12.7 The GRU model’s predictions for PV power.

Figure 12.8 The GRU model’s predictions for PV power.

Chapter 13

Figure 13.1 Smart HEMS design.

Figure 13.2 Power use in the home on a daily basis.

Figure 13.3 Proposed demand response technique flowchart.

Figure 13.4 A MATLAB-based HEMS model.

Figure 13.5 The user-interface window for non-shiftable equipment.

Figure 13.6 The user-interface window that allows users to shift appliances.

Figure 13.7 Signals for both average and real-time prices.

Figure 13.8 Energy used by the various smart home gadgets.

Figure 13.9 Different operating modes.

Figure 13.10 Power usage profile once demand response method is put into place...

Chapter 14

Figure 14.1 Smart IoT devices setting out.

Figure 14.2 Solar and windy power PV.

Figure 14.3 (a) Future networks architecture.

Figure 14.3 (b) AI architecture.

Figure 14.4 Original training data.

Figure 14.5 Results for classification of training data.

Figure 14.6 Original dataset.

Figure 14.7 Results of clustering.

Figure 14.8 Price function diagram.

Chapter 15

Figure 15.1 Discrepancy between generating capacity and power consumption.

Figure 15.2 Results for AGPQ and ANN-G-P methods.

Figure 15.3 Discrepancies between linear models.

Figure 15.4 Comparison of the MAPE values across several methods.

Figure 15.5 Comparison between the AGPQ (quadratic) and ANN-G-P (linear).

Figure 15.6 Comparations of the predictions of A-G-P-Q with the real need usin...

Figure 15.7 Projected power consumption for scenario 1 and scenario 2.

Figure 15.8 Correlation between gross domestic product (GDP) and the electrici...

Chapter 16

Figure 16.1 Designing of smart home using BERT embeddings.

Figure 16.2 Smart home using grid system.

Figure 16.3 Energy utilization and visualization models.

Figure 16.4 TBATS model for prediction.

Figure 16.5 Prediction using TEAC and ANN.

Figure 16.6 Working of hybrid systems with low economy maintenance.

Figure 16.7 IoT for renewable energy generation.

Chapter 17

Figure 17.1 Distribution of machine learning algorithm in smart city [26].

Chapter 18

Figure 18.1 Proposed block diagram.

Figure 18.2 Equivalent circuit of PV system.

Figure 18.3 Conventional SEPIC converter circuit.

Figure 18.4 High gain SEPIC converter.

Figure 18.5 RBFNN structure.

Figure 18.6 Waveform of PV panels. (a) Temperature. (b) Voltage. (c) Irradiati...

Figure 18.7 Converter output. (a) Voltage. (b) Current.

Figure 18.8 Output of the grid. (a) Voltage. (b) Current.

Figure 18.9 Power waveform. (a) Real. (b) Reactive.

Figure 18.10 PV panel waveform. (a) Temperature. (b) Voltage. (c) Irradiation....

Figure 18.11 Output of the converter. (a) Voltage. (b) Current.

Figure 18.12 Grid output. (a) Voltage. (b) Current.

Figure 18.13 Power waveform. (a) Real power. (b) Reactive power.

Figure 18.14 IoT output.

Figure 18.15 THD simulation.

Figure 18.16 THD comparison.

Figure 18.17 Tracking efficiency comparison.

Guide

Cover Page

Table of Contents

Series Page

Title Page

Copyright Page

Preface

Begin Reading

Index

Also of Interest

WILEY END USER LICENSE AGREEMENT

Pages

ii

iii

iv

xv

xvi

xvii

xviii

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

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

66

67

68

69

70

71

72

73

74

75

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

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

144

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

221

222

223

224

225

226

227

228

229

230

231

232

233

235

236

237

238

239

240

241

242

243

244

245

246

247

248

249

250

251

252

253

254

255

256

257

258

259

260

261

262

263

264

265

266

267

268

269

271

272

273

274

275

276

277

278

279

280

281

282

283

284

285

286

287

288

289

291

292

293

294

295

296

297

298

299

300

301

302

303

304

305

306

307

309

310

311

312

313

314

315

316

317

318

319

320

321

322

323

324

325

327

328

329

330

331

332

333

334

335

336

Scrivener Publishing100 Cummings Center, Suite 541JBeverly, MA 01915-6106

Publishers at ScrivenerMartin Scrivener ([email protected])Phillip Carmical ([email protected])

Sustainable Smart Homes and Buildings with Internet of Things

Edited by

Pramod Singh Rathore

Abhishek Kumar

Surbhi Bhatia

Arwa Mashat

and

Thippa Reddy Gadekallu

This edition first published 2025 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© 2025 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 merchantability 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 978-1-394-23148-5

Front cover images supplied by Adobe FireflyCover design by Russell Richardson

Preface

The convergence of sustainable building practices and advanced Internet of Things (IoT) technologies marks a significant shift in the architecture and construction industries. As global environmental challenges intensify, the need for innovative solutions that reduce energy consumption, enhance resource efficiency, and improve living standards has never been more urgent. This book, Sustainable Smart Homes and Buildings with IoT, aims to address these challenges by exploring the integration of IoT technologies in creating environmentally friendly and intelligent living spaces. The concept of smart homes and buildings goes beyond merely connecting devices and systems. It encompasses the creation of living environments that are responsive, adaptive, and efficient. By leveraging IoT, these smart spaces can monitor and manage energy usage, optimize lighting and climate control, ensure security, and even enhance the health and well-being of occupants. The potential for IoT to revolutionize building sustainability is immense, offering new ways to achieve energy efficiency, reduce carbon footprints, and promote sustainable living.

A unique feature of this volume is its emphasis on case studies and real-world implementations. By presenting practical examples and success stories, we aim to bridge the gap between theoretical knowledge and practical application. These case studies highlight the challenges faced and the innovative solutions developed, offering valuable insights for researchers, practitioners, and policymakers. In addition to technological and practical aspects, the book also addresses the ethical and regulatory considerations of integrating IoT in sustainable building practices. As with any technological advancement, the deployment of IoT in homes and buildings raises important questions about data privacy, security, and ethical use. This volume provides a balanced discussion on these topics, ensuring a holistic understanding of the field.

CHAPTER SUMMARIES:

Chapter 1: It focuses on the diverse smart home and Internet of things (IoT)-related products are fast evolving to provide customers with enhanced convenience and ease. However, the current crop of intelligent houses needs to be improved by a lack of operating systems that can adequately connect many components of the smart home ecosystem.

Chapter 2: This chapter introduces a novel use of the Internet of things-based supervisory control and data acquisition (SCADA) system for keeping tabs on a hybrid system that includes solar, wind, and battery energy storage technologies.

Chapter 3: This chapter uses IoT and Harmony to optimize smart home energy management. SEO method: the proposed method aims to minimize energy consumption and enhance energy efficiency. Photovoltaic (PV) and wind renewable energy systems supply residential households with electrical power.

Chapter 4: This chapter suggests a novel architecture called the Internet of things Meta-Control Firewall Plus (IMCF+) to close this gap and strike a reasonable balance between comfort, energy use, and carbon dioxide emissions. Green planner (GP) is an innovative artificial intelligence (AI)-inspired algorithm built inside the IMCF+ framework that plans energy usage using several different amortizations schemes.

Chapter 5: Here author optimizes energy savings and consumer convenience by considering the pressure of the air, dew point, and wind velocity. Design a hybrid strategy using GWO and particle swarm optimization (PSO) to evaluate the optimization. This study allows for proactive energy optimization using appliance prediction.

Chapter 6: This chapter presents the design of a smart energy management (SEM) system that utilizes Node MCU and Android platforms. The SEM system is specifically developed as an integral component of a smart home application. This system enables real-time monitoring of home energy use, along with the capability to capture data about device operating times and energy consumption statistics.

Chapter 7: This chapter provides support for the use of real-time IoT for energy management in eco-friendly smart structures. Taking readings of energy use, making forecasts of future energy use, and recognizing people’s faces are the three cornerstones of the proposed system. Predictions were made using a method called short-term load forecasting (STLF) that is based on the K-nearest neighbor (KNN) algorithm.

Chapter 8: The goal of this chapter is to suggest an architecture for making the most of renewable energy sources. The suggested architecture collects the power consumption profile of heterogeneous devices using Internet of things principles.

Chapter 9: To minimize power costs for the user, the suggested system effectively optimizes the electrical consumption of various home appliances in price surroundings that are constantly changing.

Chapter 10: This chapter aims to address the challenge of integrating big data platforms and machine learning algorithms into an intelligent system for this purpose to forecast how much energy various Croatian government buildings will consume, prediction models were constructed using deep learning neural networks, Rpart regression tree models, and random forests using variable reduction techniques.

Chapter 11: This chapter proposes a novel optimization approach to DC microgrid power management (MG) for day-ahead control and trading. Power damage in energy-efficient conservation schemes and consumption release measures like oxides of nitrate, sulfur dioxide, and carbon dioxides are all potential savings targets in the context of the multiple objective optimization dispatch (MOOD) problem.

Chapter 12: This chapter proposes a preprocessing data framework to address the issue of weather data loss, quantity, and matching, all of which impact the model training outcomes. The models used include an artificial neural network (ANN), a long short-term memory (LSTM), and a gated recurrent unit (GRU), all based on deep learning algorithms.

Chapter 13: This chapter aims to help homeowners lower their energy bills by creating a demand response plan the future algorithm seeks to reduce peak demand by shifting regulated loads’ operation from expensive peak hours to cheaper off-peak times, all while taking into account the customer’s preferences.

Chapter 14: This chapter suggests a smart energy information management solution. Noteworthy is that the suggested approach achieves safe and efficient energy use in a smart environment using artificial intelligence (AI) technology and secure. cryptography primitives. The suggested plan is to make smart environments’ energy use more efficient across all dimensions. With the suggested approach, smart IoT device energy management at the granularity may be achieved by considering and implementing strategies along three distinct dimensions.

Chapter 15: This research presents a novel approach to predicting power demand by leveraging a hybrid growth method integrating particle swarm optimization (PSO) and genetic algorithm (GA) accompanying artificial neural networks (ANN).

Chapter 16: This chapter explores the multifaceted impact of renewable energy adoption and IoT technology on the economics of smart homes and buildings. Renewable energy technologies, such as solar panels and wind turbines, are transforming residential and commercial structures into energy self-sufficient entities.

Chapter 17: In this chapter, author shall define machine-based learning algorithms and provide a general overview of smart-based systems. We will examine these algorithms’ advantages, difficulties, and practical uses.

Chapter 18: In this chapter author explains the Photovoltaic (PV) systems that are being used more often to produce clean electricity. However, because of their low efficiency, scientists have tried to find ways to make them more effective and efficient. A high gain single-ended primary inductor converter (SEPIC) converter is used in this proposed system to increase the output DC voltage to the desired level and to maximize the utilization of PV array with continuous current flow.

Dr. Pramod Singh Rathore

Assistant Professor,Manipal University Jaipur,Rajasthan, India

Dr. Abhishek Kumar

Associate Professor,SMIEEE, Chandigarh University, India

Dr. Surbhi Bhatia

School of Science,Engineering and Environment,University of Salford,United KingdomUniversity Centre for Research and Development,Chandigarh University, Mohali, Punjab

Dr. Arwa Mashat

Information System department in the Faculty of Computingand Information Technology, King Abdulaziz University,Rabigh, Saudi Arabia

Dr. Thippa Reddy Gadekallu

Chief Engineer,Zhongda Group,Haiyan County, Jiaxing City,Zhejiang Province, ChinaAdjunct Professor,Division of Research and Development,Lovely Professional University, Phagwara, India

1Development of a Framework to Integrate Smart Home and Energy Operation Systems to Manage Energy Efficiency Through AI

Sasikala P.1*, S. Sivakumar2, Murali Kalipindi3 and Makhan Kumbhkar4

1Department of Computer Science, Government Science College (Nrupathunga University), Bangalore, Karnataka, India

2Department of Electrical and Electronics Engineering, VelTech Rangarajan Dr. Sagunthala R and D Institute of Science and Technology, Avadi, Chennai, Tamil Nadu, India

3Department of Artificial Intelligence and Machine Learning, Vijaya Institute of Technology for Women (Affiliated to JNTUK), Enikepadu, Andhra Pradesh, India

4Department of Computer Applications, ICAR-Indian Institute of Soybean Research, Indore, India

Abstract

Diverse smart home and Internet of things (IoT)-related products are fast evolving to provide customers with enhanced convenience and ease. However, the current crop of intelligent houses needs to be improved by a lack of operating systems that can adequately connect many components of the smart home ecosystem. This is true since these devices are manufactured using self-service modules and run on proprietary IoT platforms built by the manufacturer. Without a unified OS, managing many components of a smart home may quickly become a bureaucratic nightmare. Moreover, this gives rise to issues such as an overwhelming amount of traffic on the intelligent network for home and inefficiency in energy use. To address these challenges, it is essential to establish a comprehensive management system that facilitates seamless connectivity among IoT devices. To effectively manage the IoT, they suggest implementing three sophisticated models as application services inside an IoT platform designed for smart homes. The three methods under consideration are intelligence awareness targeting as a service (IAT), intelligence energy consumption as a service (IE2S), and intelligence service total access system (TAS) (IST). The IoT oversees the management of the “things” stage. Intelligent Adaptive Technology (IAT) employs advanced machine learning techniques to effectively develop a comprehensive understanding of the contextual information associated with the data values produced by various sensors. This enables the system to gather data on the prevailing environmental conditions efficiently. The IE2 system is a server designed for the IoT platform. Its primary responsibility is to handle and analyze the information gathered by the IAT. The server employs Mobius, an open-source platform by international standards, and a TensorFlow engine for information learning. The IE2S analyses customer use patterns to automate service delivery. Intelligence service TAS manages and provides care for the service period. Three clever models enable IoT devices in smart homes to collaborate actively. Innovative methods may reduce network congestion and energy waste by minimizing needless jobs and adjusting energy use to IoT usage trends in smart homes.

Keywords: Internet of things, smart home, IAT, IE2S, machine learning

1.1 Introduction

Internet of things has recently advanced to the point where it can autonomously recognize and assess its surroundings and apply this understanding to new settings. Because of advancements in the performance of computers, wireless network processing, efficient algorithms, and so on, the speed and precision with which IoT receives and processes instructions has increased dramatically. As more and more algorithms are developed, the IoT is expanding exponentially.

Internet of things applications in smart homes are many. In contrast to the traditional house, which consists mainly of a structure and specific furnishings for living, the smart home prioritizes the user’s happiness and comfort. Beyond its primary function as a place to sleep, eat, and shower, a “smart home” is an expansive collection of IoT apps that enable individuals to regulate their daily cycles, seek delight, and take advantage of novel services.

Numerous studies highlight issues with congestion in networks and power utilization despite the expansion of the IoT industry and related technologies. Currently, the more available technology to mitigate the effects of the widespread adoption of smart home services on energy and data networks is required. As the IoT continues to grow, so does the need for electricity, leading to a surge in the number of studies focusing on renewable energy sources. However, because of the time and resources needed for this new energy generation, it is a risky, long-term undertaking. To address these issues, need network processing that is both autonomous and efficient, as well as a means of controlling the IoT that minimizes wasted energy use by learning from users’ habits.

A smart manager is needed for controlling and managing the data from IoT users, which is crucial for finding solutions to the issues above. For this article, “intelligent manager” is not a reference to an IoT platform that only links IoT devices; instead, it refers to a manager who offers services that create a personalized environment for the customer in keeping with the smart home’s stated goal and utilizes network technology to reduce energy consumption.

The cognitive services provided by IoT platforms nowadays mainly consist of processing massive amounts of data and performing complex mathematical computations much more rapidly and correctly than a human can. Utilizing a set of rules that learn from and analyze user data, a smart home’s smart manager cannot only coordinate the operation of several strategies at once but also tailor its services to each resident. In a smart home, this allows for the anticipation and readiness for a wide range of scenarios and settings. Network and energy consumption may be reduced by appropriately operating and controlling IoT applications in advance, based on analysis of relevant data. Additionally, automated services are provided in a comfortable and pleasant setting by anticipating the user’s sentiments, ideas, and requirements.

This research takes into account prior work to learn about cutting-edge tech and industry trends across several IoT platforms and to identify the need for offering innovative home-specific IoT energy-saving services. This study presents a model for service with the necessary artificial intelligence to improve network and energy efficiency, to the research above.

1.2 Research Idea Definitions

1.2.1 A Service for Intelligence Awareness

A model that processes data from intelligent situational awareness systems at the item level is called intelligence awareness target as a service (IAT). Information assurance technology (IAT) sifts through data produced by objects, collecting just the information that is required in advance. This allows data to be processed in a manner suitable for the circumstance since needless processing is avoided and the essential actions and procedures are carried out.

To anticipate and respond to potential problems, IAT classifies users’ actions and routines into four broad categories. According to this, IAT is conscious of the circumstances, and the necessary data is processed.

Internet of things is broken down in this research into its parts: sensors, mobile devices, and home electronics.

These three gadgets (sensors, smartphones, and smart appliances) are among those that generate and process the most data in a smart home, and they are also among the easiest to access and gather data from about the environment and its inhabitants. The IAT, in broad terms, is split into stationary and non-stationary components. The sensor IAT is standard in stationary IAT, whereas the smartphone IAT is more common in mobile IAT.

Meanwhile, the concept of an IAT that controls a smart appliance may be articulated similarly. Being both an IAT and an IST (intelligence service target as a type of service) sets it apart from similar technologies. This study expands the scope of the IAT discussion to include smart appliances because of their unique characteristics.

1.2.2 IAT Sensor

The sensor IAT model is intended to be used by stationary items to gather primary sensor data. To assess the interior setting and provide concrete numerical values and ideas, a collection of sensors is necessary.

The fixed sensor boards may be seen in each room of the intelligent house, as shown in Figure 1.1. The smart boards have absolute sensors connected to them, and this data may be used to assess the present state of the intelligent house. Sound, motion and rotational sensors are employed. These sensors are used to learn about the state of each smart home’s local area network and to compile data for analysis.

Figure 1.1 Sensor for each network area.

Figure 1.2 Situation types according to detector.

Figure 1.2 illustrates how the ITA may be aware of the interior context in advance by classifying user actions and lifestyle habits into four broad categories. The first scenario involves an action being taken by a user while inside. In the second scenario, the user is at home, relaxing. The third scenario is a user who is sleeping in a home. In the fourth scenario, there is no one home. These grouped scenarios comprehend the user’s whereabouts and movement patterns.

The sensor’s checking shown in Figure 1.2 is straightforward and does not need a massive quantity of data transfer to identify whether or not anything has been detected. As a result, IAT can anticipate potential problems, estimate their severity, and only send data to the server if necessary. Because the Internet is not being put to use while no data is being sent, less power is used.

1.2.3 IAT Smartphone

In contrast to sensor IAT, mobile devices like smartphones are constantly in motion. Several types of sensors are seen in Figure 1.3 inside a smartphone. It’s the most crucial tool for getting information from the user’s immediate vicinity. By doing so, it gathers information about the user that may be used for diagnosis. The global positioning system (GPS) sensor, gyroscope detector, proximity detector, barometer sensor, and light sensor were all used in this investigation. These sensors provide a complete comprehension of the user’s surroundings, including their location, motion, and current lighting conditions. As part of the training set, this information is sent to the server, where it is categorized using weights.

Figure 1.3 Smartphone detector.

1.2.4 IAT Smart Appliance

The current level detector IAT and smartphone IAT are simpler models than the smart appliance IAT. Both IoT and IST at the service and thing levels are supported. The things-level job of smart appliance IAT is data collection, and the services-level role is service provision. The first function is to gather information, which may be broken down into two categories: monitoring the surrounding environment using inexpensive sensors and studying individual users’ habits. First, as illustrated in Figure 1.4, many smart appliances gather primary environmental data by their intended use. This data may take several forms.

To integrate the data, the IAT in the smart appliance processes it and sends it to the server. The interior temperature is recorded by a device like an air conditioner and sent to a central hub. The second element is user data collecting, and in this case, records of how intelligent appliances are used are obtained for analysis. The information gathered is sent to a central server, which is processed using machine learning to reveal patterns in user behavior. This is essential to identify and provide appropriate treatment for these habits.

Figure 1.4 Smart appliances for each area network.

The server’s second function is to be served automatically. In an air conditioner, a server-level decision might result in optimal air conditioning service being supplied. Digital home gadget IAT is responsible for gathering information and the automated service. In this chapter, a model for the next generation of smart home devices integrates an IAT model for data collection with an IST model for service provision. Since it is concerned with the service levels at which such services are provided, it also serves as an IST model. In the chapter’s last section, an IST model is presented.

1.2.5 Service-Based Intelligence Energy Efficiency

The IoT platform and server is intelligent energy efficacy as a service (IE2S). The intelligent integration and management of IE2S can be shown in Figure 1.5 at both the components level as well as the service level.

As the things level, IE2S receives information from IAT that helps it anticipate events. An ongoing process of data collection and analysis using a neural network learning algorithm ensures that no transmitted information is lost. Through machine learning, IE2S can execute two tasks: circumstance recognition and user consumption pattern discovery.

The primary purpose is scenario recognition inside the smart home, and for this, IE2S continually gathers data transmitted by IAT in real time, analyzing it. To choose the best course of action, it not only learns about the circumstance at hand but also examines many types of environmental data. As a secondary purpose, it analyses how the user interacts with IoT devices in their smart home. Learning is informed by either data provided by the person controlling the IoT or data already captured. The smart home surroundings constantly learn to give the user the desired scenarios.

Services provided by IE2S are tailored to each user based on an analysis of their data. This is accomplished by exchanging service command information between IE2S and IST. The IST system defines the actions to be taken to provide services that are well-suited to the characteristics of the device. Then, the actions are taken in compliance with these characteristics. Energy and network consumption reduction in a smart home is to analyze the user’s use habits inside the intelligent house and eliminate superfluous chores. Automation of smart homes to save energy is possible if fewer services than required are supplied.

Figure 1.5 Service structure for IE2S.

1.2.6 Service Idea for Intelligence Target

TAS intelligence’s (IST) job is to serve users through a wide range of intelligent home products. To deliver the necessary services to the user, IST utilizes the IE2S learning data. Smart appliances incorporate IAT and IST, as illustrated in Figure 1.6, since they may gather data and provide services with a single device. There are three components of the IST shown in Figure 1.6: an air conditioner, a light, and an electric blanket. The most popular intelligent appliances in the home were chosen to illustrate the vast amounts of data and many services at play. The air conditioner sends the current room’s high temperature to the server, and in IE2S, the system simultaneously learns from the customer’s habits and information to determine the optimal temperature. Therefore, air conditioning may be used to keep inside at a comfortable temperature. The lamp’s brightness and operational state are optimized by monitoring the user’s routines and adjusting accordingly based on the time of day and whether or not someone is present in the room. As the user changes temperature, the electric blanket records and analyses this data to determine the ideal setting for the user’s body and the room.

Furthermore, in addition to its intelligent capabilities, the IST can regulate and execute essential functions of devices. Additionally, it offers a range of application services to users via smartphone apps. Nevertheless, the scope of this research is limited to intelligent services alone.

1.3 Algorithms for Intelligent Models

1.3.1 Algorithm for IAT

The IAT technique is designed for low-effort data transmission between the object level and the server. To do as little as possible, it learns about the scenario in advance and collects relevant information. All relevant information is collected and uploaded to the server. As a result, the server needs to process the most crucial data, and less time is spent on collecting and storing data that is optional.

Figure 1.6 Smart appliances incorporate IAT and IST.

As shown in Figure 1.7, the IAT algorithm accepts as input three distinct forms of data: sensor data, smartphone data, and data explained using the IAT concept. Sensor and smart home appliance/mobile device data input, here, information gleaned from a user-operated smart appliance or mobile device is designated as status data since it may be used to understand the user’s preferences better.

To begin, the present state of the intelligent house is divided into four categories based on the sensor data entered. Figure 1.2 also depicts these four categories. The sensors and devices in this scenario are set only to activate when there’s a human present in the house. When engaged, a sensor eliminates readings outside a predetermined tolerance range before sending the remaining data to a central server.

Figure 1.7 IAT algorithm.

Second, input from a smart home appliance or mobile device is sent to the server as “desired” information. As the user interacts with the smart device, the values of this information are adapted to meet their specific requirements. This allows it to infer the user’s desired state and the one they want to transition to.

1.3.2 Algorithm of IE2S

As can be seen in Figure 1.8, IE2S acts as a server, collecting, processing, and learning from information sent by IAT. As input, it takes in information filtered and customized by the user using IAT. As seen in the figure, it communicates the present state to the learning information and the preferred state to which the user wants data to conclude the data. When a gap exists between the current and target states, IE2S uses deep learning to forecast the best value. The prediction value is improved by using a synthetic TensorFlow engine for learning. As a result, IE2S may generate a perfect state via forecasts even if the customer’s desired information input was never received.

1.3.3 Algorithm for IST

As seen in Figure 1.9, IST responds to directives from IE2S to either preserve or alter the state of connected devices. IST is a program that swiftly carries out IE2S’s commands. To facilitate communication between the IE2S and the devices, the IST acts as a bridge, always ready to accept orders. Finally, it reports to IE2S any changes in device status and constantly polls for updated data.

Figure 1.8 IE2S algorithm.

Figure 1.9 IST algorithm.

1.4 Analyzing and Implementing

1.4.1 Sensory Things

The three sensors from Figure 1.10 are connected to an Arduino board on the smart home’s sensor board. All of the sensor action codes required by IAT are included in a single modular library. This is helpful for the integrated management of the many sensors. The data is read from the sensors by the Arduino board via serial connection. The Arduino board talks to the server via Wi-Fi using the MQTT protocol. It has real-time knowledge of the IoT scenario and reports necessary information to the server.

The sensor on the board gathers data about its surroundings. The situation may be constantly monitored using condition statements (if-else) generated by the Awareningloop function. Despite being a loop, it has a low processing cost, while no events happen because of the event structure. If an algorithm is conscious of a circumstance, it may filter out irrelevant information before sending the necessary data to the server by transmitting it beyond the normal range.

Figure 1.10 Arduino board on the smart home’s sensor board.

1.4.2 Server

The server operates on the Mobius globally recognized platform and the IE2S algorithm. The information sent from IAT is stored in a MySQL database on the Mobius server. Using the Jupyter Notebook and the Anaconda software package, the TensorFlow engine learns this information.

As a basic example of this kind of implementation, a system was created that monitors the user’s interior temperatures and equates them to the customer’s preferred values to figure out what the ideal temperature should be and then give it.

The Seoul city weather service is shown in Figure 1.11 below. The average temperatures for each month are shown, and these are the reference temperatures utilized in educational settings. The intelligent air conditioner considers the “user desire temp” parameters. It was speculated, based on the recorded temperature differentials at which the customer activated the air conditioner, that the user had a favored temperature pattern. Each piece of data is then digitized and cleaned up to build a learning-ready dataset. TensorFlow then uses its newly acquired knowledge to determine an accurate interior temperature.

The TensorFlow-powered learning code inputs for x (monthly average temperatures) and y (user preferences) are used to train the model. Connections are continually calculated, and operators that work well with these connections are discovered, all in the service of transforming X into Y. Inputting a new value for X causes the learned operators to produce a new output value. A training rate between 0.3 and 1000 iterations during the learning phase was used in the test.

Figure 1.11 Seoul city weather service chart.

Figure 1.12 depicts the procedure of repeating the learning process one thousand times. As can be seen, the initial price is 513 and decreases to the final price of 1.06. Figure 1.13 displays the outcomes of the exam conducted with the aid of the learning software. First, arbitrary values of x are assigned to the monthly temperature data. The learning algorithm can verify the prediction values. The values generated are very close to those chosen by the user.

A chart depicting the final findings is shown in Figure 1.14. The blue line represents the typical temperature monthly, while the black line indicates the desired setting for the user. Learning the connection between these two data sets yields an estimate, shown as an orange line. When contrasted with the blue line, the black and red lines in this graph seem pretty similar. The error may be decreased even more by using additional data for training.

Figure 1.12 1000 times learning process.

Figure 1.13 Executing test on learned program results.

Figure 1.14 Final findings chart.

1.5 Conclusion

Technology for the smart home and the IoT is advancing fast, and many new intelligent gadgets are being created to make people’s lives easier and more convenient. A need for operating systems for integrating the many smart home gadgets is a significant limitation of currently available smart houses. This is because the manufacturers of these devices rely on self-service modules and utilize their own proprietary IoT platforms to manufacture their products. Without a unified operating system, keeping track of all the moving parts in a smart home becomes a significant burden.

Many issues, including ineffective systems of operation, excessive network traffic, and energy waste, have arisen due to the massive and quick expansion of technology linked to the IoT and the smart home. A combined supervision system that links IoT devices is needed to solve these issues. They offer three innovative models as application services for an IoT platform to manage IoT effectively in a smart home. IAT, IE2S, and IST are the three models to consider. Using these three frameworks, IoT devices may gain situational awareness via intelligent learning from the data they collect. Based on the current state of affairs, IoT is activated, and IAT gathers the appropriate data. Currently, IoT is only turned on when necessary. The IE2S acts as the server (IoT platform), processing and learning from the information gathered by IAT with the help of a TensorFlow engine. Through observation and analysis of user behavior, it learns to make accurate predictions. Deep learning was used to achieve highly comparable forecast temperature results in the experiment conducted in this research, which compared the information regarding the average temperature for monthly with that of the customer’s desired temperature.

Monthly average temperature data that was made entirely up nonetheless resulted in forecast numbers that were quite close to the user’s intended temperature data. The quality of predictions improves in direct proportion to the quantity of user data collected, so additional study into improving prediction quality via increased data collection is warranted. For system stability, further study into the intelligent algorithm’s physical structure and characteristics is needed, as is the collection of accurate data and an increase in the accuracy of its predictions.

In addition, the network itself has to be analyzed and tested. IoT gadgets in a smart home can communicate and work together if their designers pay attention to these three forms of intelligence. Services for automating energy saving in the house were offered in this research. As more research on intelligent models is done, structurally fresh mediums for the IoT for smart home systems will provide intelligent, energy-efficient living environments for healthy lives.

Bibliography

Lee, D.

et al.

, Artificial intelligence implementation framework development for building energy saving.

Int. J. Energy Res.

, 44, 14, 11908–11929, 2020.

Reinisch, C.

et al.

, Thinkhome energy efficiency in future smart homes.

EURASIP J. Embedded Syst.

, 2011, 1–185, 2011.

Rochd, A.

et al.

, Design and implementation of an AI-based & IoT-enabled Home

Energy Management System: A case study in Benguerir—Morocco.

Energy Rep.

, 7, 699–7195, 2021.

Farzaneh, H.

et al.

, Artificial intelligence evolution in smart buildings for energy efficiency.

Appl. Sci.

, 11, 2, 763, 2021.

Sepasgozar, S.

et al.

, A systematic content review of artificial intelligence and the Internet of things applications in smart home.

Appl. Sci.

, 10, 9, 3074, 2020.

Zhou, B.

et al.

, Smart home energy management systems: Concept, configurations, and scheduling strategies.

Renew. Sustain. Energy Rev.

, 61, 30–405, 2016.

Zaidan, A.A. and Zaidan, B.B., A review on intelligent process for smart home applications based on IoT: coherent taxonomy, motivation, open challenges, and recommendations.

Artif. Intell. Rev.

, 53, 1, 141–165, 2020.

Pavan, KV.S., Deepthi, K., Saravanan, G., Kumar, T.R., Vinay, AV., Improvement of Delamination spread Model to gauge a dynamic disappointment of interlaminar in covered Composite Materials &to forecast of Material Debasement.

PalArch’s J. Archaeol. Egypt/Egyptol.

, 17, 9, 6551–6562, 2020. Retrieved from

https://archives.palarch.nl/index.php/jae/article/view/5214

.

Guo, X.

et al.

, Review on the application of artificial intelligence in smart homes.

Smart Cities

, 2, 3, 402–420, 2019.

Shareef, H.

et al.

, Review on home energy management system considering demand responses, smart technologies, and intelligent controllers.

IEEE Access

, 6, 24498–245095, 2018.

Wu, Y.

et al.

, A comprehensive overview of the framework for developing sustainable energy internet: From things-based energy network to services-based management system.

Renew. Sustain. Energy Rev.

, 150, 1114095, 2021.

Dhotre, V.A., Mohammad, H., Pathak, P.K., Shrivastava, A., Kumar, T.R., Big data analytics using MapReduce for education system.

Linguist. Antverp.

, 25, 3130–3138, 2021.

Aliero, M.S.

et al.

, Smart Home Energy Management Systems in the Internet of Things networks for green cities demands and services.

Environ. Technol. Innov.

, 22, 1014435, 2021.

Mehmood, M.U.

et al.

, A review of the applications of artificial intelligence and big data to buildings for energy-efficiency and a comfortable indoor living environment.

Energy Build.

, 202, 1093835, 2019.

Li, W.

et al.

, Motivations, barriers and risks of smart home adoption: From a systematic literature review to conceptual framework.

Energy Res. Social Sci.

, 80, 1022115, 2021.

Almusaed, A., Ibrahim, Y., Asaad, A., Enhancing Smart Home Design with AI Models: A Case Study of Living Spaces Implementation Review.

Energies

, 16, 6, 2636, 2023.

A. Alqahtani,

et al.

, Enquiry chat bot and artificial intelligent guidance system and its working method. U.S. Patent Application No. 18/175,764.

Rathore, P.S. and Chaudhary, A., Route planning via facilities with time dependent.

2013 International Conference on Information Systems and Computer Networks

, Mathura, India, pp. 5–8, 2013, doi: 10.1109/ICISCON.2013.6524163.

Merabet, G.H.

et al.

, Intelligent building control systems for thermal comfort and energy-efficiency: A systematic review of artificial intelligence-assisted techniques.

Renew. Sustain. Energy Rev.

, 144, 110969, 2021.

Ringel, M., Roufaida, L., Djamel, D., Multiple benefits through smart home energy management solutions—A simulation-based case study of a single-family-house in Algeria and Germany.

Energies

, 12, 8, 1537, 2019.

Bhargava, N., Rathore, P.S., Bhowmick, A., A Methodology for the Energy Aware Clustering and Aggregate Node Rotation with Sink Relocation in MANET, in:

Emerging Trends in Expert Applications and Security. ICETEAS 2023. Lecture Notes in Networks and Systems

, vol. 682, V.S. Rathore, V. Piuri, R. Babo, M.C. Ferreira, (Eds.), Springer, Singapore, 2023,

https://doi.org/10.1007/978-981-99-1946-8_13

.

Bhatia, S., Alsuwailam, R.I., Roy, D.G., Mashat, A., Improved Multimedia Object Processing for the Internet of Vehicles.

Sensors

, 22, 4133, 2022.

https://doi.org/10.3390/s22114133

.

Marinakis, V., Big data for energy management and energy-efficient buildings.

Energies

, 13, 7, 1555, 2020.

Note

*

Corresponding author

:

[email protected]