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Sustainable Smart Homes and Buildings with Internet of Things

Edited by Pramod Singh Rathore, Abhishek Kumar, Surbhi Bhatia, Arwa Mashat, and Thippa Reddy Gadekallu
Copyright: 2025   |   Expected Pub Date:2024//
ISBN: 9781394231485  |  Hardcover  |  
348 pages
Price: $225 USD
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One Line Description
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.

Audience
Engineers, undergraduate, postgraduate, and research students and faculty, researchers, and other industry professionals

Description
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.

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Author / Editor Details
Pramod Singh Rathore, MTech, is pursuing his doctorate in computer science from the University of Engineering and Management (UEM), Kolkata, India and is an assistant professor in the Department of Computer and Communication Engineering, Manipal University Jaipur. With over 11 years of academic teaching experience, he has published more than 55 papers in scientific journals, books, and conferences. He has co-authored and edited numerous books, including books from Scrivener Publishing.

Abhishek Kumar, PhD, is a post-doctoral fellow in the Ingenium Research Group, Universidad De Castilla-La Mancha, Ciudad Real, and Ciudad Real, Spain. He has over nine years of academic experience and has published over 100 papers in scientific journals, books, and conferences. He has authored or coauthored six books published and edited 25 books, including books from Scrivener Publishing, and he has been the series editor for a number of book series.

Surbhi Bhatia Khan, PhD, is working in the School of Science, Engineering, and Environment, University of Salford, Manchester, United Kingdom and the University Centre for Research and Development, Chandigarh University, Mohali, Punjab, India. She has over 11 years of academic and teaching experience and has published more than 100 papers in scientific journals. She has 12 patents to her credit and has written three books and edited ten books.

Arwa Mashat, PhD, is an assistant professor in the Faculty of Computing and Information Technology in King Abdulaziz University Rabigh branch. She earned her PhD in instructional design and technology from Old Dominion University, Virginia, USA and has written two books.

Thippa Reddy Gadekallu is currently with the Zhongda Group as Chief Engineer and in the School of Information Technology and Engineering, Vellore Institute of Technology, Vellore, India, as well as with the Department of Electrical and Computer Engineering, Lebanese American University, Byblos, Lebanon.

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Table of Contents
Preface
1. Development of a Framework to Integrate Smart Home and Energy Operation Systems to Manage Energy Efficiency Through AI
Sasikala P., S. Sivakumar, Murali Kalipindi and Makhan Kumbhkar
1.1 Introduction
1.2 Research Idea Definitions
1.2.1 A Service for Intelligence Awareness
1.2.2 IAT Sensor
1.2.3 IAT Smartphone
1.2.4 IAT Smart Appliance
1.2.5 Service-Based Intelligence Energy Efficiency
1.2.6 Service Idea for Intelligence Target
1.3 Algorithms for Intelligent Models
1.3.1 Algorithm for IAT
1.3.2 Algorithm of IE2S
1.3.3 Algorithm for IST
1.4 Analyzing and Implementing
1.4.1 Sensory Things
1.4.2 Server
1.5 Conclusion
Bibliography
2. Development of a Hybrid System to Make the Decision and Optimization of Renewable Energy Sources
M. Jayakrishna, S. Sivakumar, Nalam Chandra Sekhar and Yabesh Abraham Durairaj Isravel
2.1 Introduction
2.2 Related Work
2.3 Methods of Modelling
2.3.1 Designing a Hybrid Energy Infrastructure
2.3.2 Modelling Web-Based SCADA Systems
2.4 Methodology
2.4.1 A Simulated Model
2.4.1.1 Model Experiment
2.5 Discussion and Result
2.6 Conclusion
Bibliography
3. IoT-Based Renewable Energy Management Systems in Apartment
Thulasi Bikku, S. Sivakumar, Sudha Arogya Mary Chinthamani and Pramoda Patro
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
3.5.1 Grid Model for Electricity
3.5.2 Energy-Use Model
3.5.3 Pricing Energy
3.5.4 Demand-Reply Paradigm
3.6 Optimize Design
3.6.1 Objectives and Restrictions
3.7 Discussion and Results
3.7.1 The Provided Data
3.8 Conclusion
References
4. Framework of IoT-Based Meta Firewall System to Plan the Renewable Energy Consumption in Smart Homes or Buildings
Mandeep Kaur Ghumman, A. Vinay Bhushan, Chetan Khemraj Lanjewar and Abhishek Choubey
4.1 Introduction
4.1.1 Green Home
4.1.2 Green Dorms
4.2 Problem Formulation and System Model
4.2.1 System Design
4.2.2 The Research Goal
4.2.2.1 Comfort Error
4.2.2.2 Consumption of Energy
4.2.2.3 CO2 Emissions
4.2.3 Baseline Methods
4.3 Meta-Control Firewall Plus (IMCF+)
4.3.1 Operation Summary
4.3.2 Procedure for Amortization
4.3.3 Algorithm for Green Plan (GP)
4.3.4 Analysis of Performance
4.4 Architecture of the IMCF+ System
4.4.1 A System Architecture
4.4.2 Graphical User Interface
4.5 Trial Methods and Assessment
4.5.1 Methods
4.5.1.1 Datasets
4.5.2 Evaluations of IMCF+
4.5.2.1 Evaluation of Households
4.5.2.2 Evaluation of University Campus
4.5.2.3 Hotel Apartment Evaluation
4.5.3 Series of Micro-Benchmarks
4.5.3.1 Series-1: Evaluation of Performance
4.5.3.2 Series-2: K-Opt Assess
4.5.3.3 Series-2: Evaluation of Initialization
4.5.3.4 Series-3: Studying Energy Conservation
4.6 Conclusion
Bibliography
5. Manage and Optimization of Renewable Energy Consumption Efficiency for Smart Homes
Thulasi Bikku, V.O. Kavitha, Chetan Khemraj Lanjewar and Abhishek Choubey
5.1 Introduction
5.2 Proposed Method
5.2.1 Preprocessing
5.2.2 Forecasting
5.2.3 Optimization
5.3 Results
5.3.1 Testing Environment
5.3.2 Dataset
5.3.3 Assessment
5.3.3.1 Preprocessing
5.3.3.2 Forecast
5.3.3.3 Optimization
5.4 Discussion
5.5 Conclusion
Bibliography
6. Cost and Renewable Energy Management by IoT-Oriented Smart Home Based on Smart Grid Demand Response
Omprakash B., Jatinkumar Patel, Dhanaselvam J. and Shruti Bhargava Choubey
6.1 Introduction
6.2 Methodology
6.2.1 Edge MCU
6.2.2 Pro Mini Arduino
6.2.3 Measurement of Current and Voltage
6.2.4 Blynk, A Creator of Interfaces for iOS and Android Platforms
6.3 System Design
6.4 Results
6.5 Conclusion
Bibliography
7. IoT-Based Smart Green Building Energy Management System
Rahama Salman, Ghada Elkady, Mukta Sandhu and Sandeep Gupta
7.1 Introduction
7.2 Methodology
7.3 Results of Construction
7.3.1 Hypotheses
7.3.2 DPM Data Creation
7.3.3 Room Power Management (Face Recognition), Power-Cut Feature
7.4 Working Model
7.4.1 Short-Term Load Forecasting (RT-STLF): Five Primary Blocks Make Up the RT-STLF
7.4.2 Manage Room Power
7.4.3 IoT Data Update
7.5 Results of Testing
7.5.1 Face Recognition (Classification) Accuracy
7.5.2 Forecasting Methodologies Comparison
7.6 Conclusion
References
8. The Framework of IoT-Based Paradigms to Renewable Power Utilization and Distribution by Microgrid
Kannan Kaliappan, Basi Reddy A., D. Muthukumaran, Gopinath S., T. Aditya Sai Srinivas and R. Senthamil Selvan
8.1 Introduction
8.2 Related Work
8.3 Intelligent Power System Design
8.3.1 Connected Devices Network
8.3.1.1 Methods of Processing and Computing
8.3.1.2 Capacity for Storage
8.3.1.3 Optimizing Energy Use in Microgrids
8.4 Daily External Energy Requirements
8.4.1 Factory Robots
8.4.2 The Topic of Discussion Pertains to Domestic or Home Robots
8.4.3 Robotic Doctors
8.5 Conclusion
Bibliography
9. Machine Learning-Based Swarm Optimization for Residential Demand-Based Electricity
Yalamanchili Salini, Kiran Sree Pokkuluri, D. Deepa and Mary Joseph
9.1 Introduction
9.2 Relevant Works
9.3 The Motivation
9.4 Energy Optimization Proposal
9.4.1 Appliance Scheduling Problem Formulation
9.4.2 Problem of Optimization
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
Sushil Bhardwaj, Bharath Sampath, Latifjon Kosimov and Shakhlokhon Kosimova
10.1 Introduction
10.2 Methods and Materials
10.2.1 Data
10.2.2 Methods
10.2.2.1 Data Collection/Preprocessing Methods
10.2.2.2 Predictive Modelling Techniques
10.3 Results
10.3.1 Energy Consumption Results Using ML Systems
10.3.2 Design of an Intelligent Energy Management System Architecture
10.4 Discussions
10.4.1 Theory Contributions
10.4.2 Practice Implications
10.4.3 Research Limitations and Direction
10.5 Conclusion
Bibliography
11. Multi-Objective Optimization Process to Analyze the Renewable Energy Storage and Distribution System from the Grid
Dinesh G., Manisha G., Dina Allam and Ghada Elkady
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
Pradosh Kumar Sharma, M. V. Kesava Kumar, Mohd Wazih Ahmad and Radhika M.
12.1 Introduction
12.2 Description of Data
12.2.1 Information About Photovoltaic Production
12.2.2 Weather Information from the CWB
12.2.3 AccuWeather Reports
12.2.4 Local Weather Position/Pyrheliometer
12.3 Information Preparation
12.3.1 Classifying Data
12.3.2 Encryption of Data
12.4 Procedures and Assessment
12.4.1 Artificial Neural Network
12.4.2 Long Short-Term Memory
12.4.3 Gated Recurrent Unit
12.5 Results
12.5.1 Findings from Hyperparameter Tuning
12.5.2 Different Weather Data Groups’ Forecast Performance
12.6 Conclusion
Bibliography
13. Machine Learning Models are Used to Analyze the Effectiveness of Daily Residential Area Energy Consumption
Kapil Aggarwal, D. M. Kalai Selvi, Vijay Kumar Rayabharapu and K. S. Chakradhar
13.1 Introduction
13.2 Intelligent Energy Systems for the House
13.2.1 Tracking
13.2.2 Management
13.2.3 Leadership
13.2.4 Recording
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
R. Swathi, M. Prabha, Ravichandran Sekar, Basi Reddy A., T. Aditya Sai Srinivas and R. Senthamil Selvan
14.1 Introduction
14.1.1 Efforts
14.2 Smart IoT Device Setting Out and Energy-Saving Equipment
14.2.1 Smart IoT Deployment
14.2.2 Relevant Work
14.3 Key AI-Based Energy-Efficient Network Issues
14.3.1 Energy from Renewable Sources
14.3.2 AI Technology
14.3.2.1 Algorithm Regression
14.3.2.2 Neural Networks
14.3.2.3 SVM Algorithm
14.3.2.4 Analysis Clusters
14.3.2.5 Suggest Algorithm
14.4 AI-Based Managing Framework for Multidimensional Smart IoT Devices
14.4.1 Logistic Regression/Clustering Analysis Interlayer
14.4.1.1 Logistic Regression Interlayer
14.4.1.2 Clustering-Analysis Interlayer
14.4.2 Regression-Based Intra-Layer Control
14.4.3 Pushing and Caching with Recommendation Procedure
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
V. Sharmila, Gaikar Vilas B., Rajiv Nayan and Pavithra G.
15.1 Introduction
15.2 Electricity Sector
15.3 Methodology
15.3.1 ANN Method
15.3.2 Particle Swarm Optimization (PSO)
15.4 ANN-GA-PSO Methods
15.4.1 Estimating Two Forms Method
15.4.2 Algorithm for Hybrid Optimization using GA-PSO
15.4.3 Data Management and Computation
15.4.4 Forecast Performance Evaluation
15.5 Results
15.5.1 Future Estimation
15.5.2 The Correlation Between Gross Domestic Product (GDP) and the Electricity Demand
15.6 Conclusion
References
16. Harmonizing Renewable Energy, IoT, and Economic Prosperity: A Multifaceted Analysis
Sri Silpa Padmanabhuni, Pradeep K. G. M., Sai Pallavi Akkisetti and G. Jayalaxmi
16.1 Introduction
16.1.1 Designing of Smart Home Models
16.1.2 Prediction of Electricity from Smart Home Models
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
Hemlata and Manish Rai
17.1 Introduction
17.1.1 Overview of Smart-Based Systems
17.1.2 Benefits of Machine-Based Learning Algorithms in Smart-Based Systems
17.1.3 Challenges and Limitations of Machine-Based Learning Algorithms in Smart-Based Systems
17.1.4 Real-World Applications of Machine-Based Learning Algorithms in Smart-Based Systems
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
Thomas Thangam, P. Kavitha, P. Nammalvar, D. Karthikeyan and V. Pujari
18.1 Introduction
18.2 Related Works
18.3 Proposed System
18.4 Results and Discussion
18.5 Conclusion
References
Index

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