Sustainable Smart Homes and Buildings with Internet of Things

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, PhD, is working in the Department of Department of Data Science, School of Science, Engineering and environment, University of Salford, Manchester, United Kingdom. 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.

• Preface xv1 Development of a Framework to Integrate Smart Home and Energy Operation Systems to Manage Energy Efficiency Through AI 1Sasikala P., S. Sivakumar, Murali Kalipindi and Makhan Kumbhkar1.1 Introduction 21.2 Research Idea Definitions 31.2.1 A Service for Intelligence Awareness 31.2.2 IAT Sensor 41.2.3 IAT Smartphone 51.2.4 IAT Smart Appliance 61.2.5 Service-Based Intelligence Energy Efficiency 71.2.6 Service Idea for Intelligence Target 81.3 Algorithms for Intelligent Models 91.3.1 Algorithm for IAT 91.3.2 Algorithm of IE2S 111.3.3 Algorithm for IST 111.4 Analyzing and Implementing 121.4.1 Sensory Things 121.4.2 Server 131.5 Conclusion 15Bibliography 162 Development of a Hybrid System to Make the Decision and Optimization of Renewable Energy Sources 19M. Jayakrishna, S. Sivakumar, Nalam Chandra Sekhar and Yabesh Abraham Durairaj Isravel2.1 Introduction 202.2 Related Work 202.3 Methods of Modelling 222.3.1 Designing a Hybrid Energy Infrastructure 222.3.2 Modelling Web-Based SCADA Systems 222.4 Methodology 242.4.1 A Simulated Model 242.4.1.1 Model Experiment 262.5 Discussion and Result 272.6 Conclusion 31Bibliography 323 IoT-Based Renewable Energy Management Systems in Apartment 35Thulasi Bikku, S. Sivakumar, Sudha Arogya Mary Chinthamani and Pramoda Patro3.1 Introduction 363.2 Smart House Design Using Internet of Things 383.3 Problem Statement 413.4 The Proposed Methodology 423.5 A Mathematical Framework 423.5.1 Grid Model for Electricity 433.5.2 Energy-Use Model 443.5.3 Pricing Energy 453.5.4 Demand-Reply Paradigm 453.6 Optimize Design 463.6.1 Objectives and Restrictions 463.7 Discussion and Results 473.7.1 The Provided Data 473.8 Conclusion 48References 504 Framework of IoT-Based Meta Firewall System to Plan the Renewable Energy Consumption in Smart Homes or Buildings 53Mandeep Kaur Ghumman, A. Vinay Bhushan, Chetan Khemraj Lanjewar and Abhishek Choubey4.1 Introduction 544.1.1 Green Home 574.1.2 Green Dorms 584.2 Problem Formulation and System Model 584.2.1 System Design 584.2.2 The Research Goal 584.2.2.1 Comfort Error 594.2.2.2 Consumption of Energy 594.2.2.3 Co 2 Emissions 594.2.3 Baseline Methods 594.3 Meta-Control Firewall Plus (IMCF+) 604.3.1 Operation Summary 604.3.2 Procedure for Amortization 614.3.3 Algorithm for Green Plan (GP) 614.3.4 Analysis of Performance 634.4 Architecture of the IMCF+ System 634.4.1 A System Architecture 634.4.2 Graphical User Interface 654.5 Trial Methods and Assessment 664.5.1 Methods 664.5.1.1 Datasets 664.5.2 Evaluations of IMCF+ 674.5.2.1 Evaluation of Households 674.5.2.2 Evaluation of University Campus 694.5.2.3 Hotel Apartment Evaluation 704.5.3 Series of Micro-Benchmarks 704.5.3.1 Series-1: Evaluation of Performance 704.5.3.2 Series-2: K-Opt Assess 714.5.3.3 Series-3: Evaluation of Initialization 724.5.3.4 Series-4: Studying Energy Conservation 724.6 Conclusion 73Bibliography 745 Manage and Optimization of Renewable Energy Consumption Efficiency for Smart Homes 77Thulasi Bikku, V.O. Kavitha, Chetan Khemraj Lanjewar and Abhishek Choubey5.1 Introduction 785.2 Proposed Method 825.2.1 Preprocessing 825.2.2 Forecasting 845.2.3 Optimization 865.3 Results 885.3.1 Testing Environment 885.3.2 Dataset 885.3.3 Assessment 895.3.3.1 Preprocessing 895.3.3.2 Forecast 905.3.3.3 Optimization 905.4 Discussion 925.5 Conclusion 93References 936 Cost and Renewable Energy Management by IoT-Oriented Smart Home Based on Smart Grid Demand Response 97Omprakash B., Jatinkumar Patel, Dhanaselvam J. and Shruti Bhargava Choubey6.1 Introduction 986.2 Methodology 996.2.1 Edge MCU 1006.2.2 Pro Mini Arduino 1016.2.3 Measurement of Current and Voltage 1026.2.4 Blynk, A Creator of Interfaces for iOS and Android Platforms 1046.3 System Design 1046.4 Results 1076.5 Conclusion 110Bibliography 1127 IoT-Based Smart Green Building Energy Management System 115Rahama Salman, Ghada Elkady, Mukta Sandhu and Sandeep Gupta7.1 Introduction 1167.2 Methodology 1187.3 Results of Construction 1217.3.1 Hypotheses 1217.3.2 DPM Data Creation 1217.3.3 Room Power Management (Face Recognition), Power-Cut Feature 1227.4 Working Model 1237.4.1 Short-Term Load Forecasting (RT-STLF): Five Primary Blocks Make Up the RT-STLF 1237.4.2 Manage Room Power 1257.4.3 IoT Data Update 1267.5 Results of Testing 1267.5.1 Face Recognition (Classification) Accuracy 1267.5.2 Forecasting Methodologies Comparison 1287.6 Conclusion 130References 1308 The Framework of IoT-Based Paradigms to Renewable Power Utilization and Distribution by Microgrid 133Kannan Kaliappan, Basi Reddy A., D. Muthukumaran, Gopinath S., T. Aditya Sai Srinivas and R. Senthamil Selvan8.1 Introduction 1348.2 Related Work 1358.3 Intelligent Power System Design 1378.3.1 Connected Devices Network 1388.3.1.1 Methods of Processing and Computing 1398.3.1.2 Capacity for Storage 1398.3.1.3 Optimizing Energy Use in Microgrids 1408.4 Daily External Energy Requirements 1458.4.1 Factory Robots 1458.4.2 The Topic of Discussion Pertains to Domestic or Home Robots 1468.4.3 Robotic Doctors 1468.5 Conclusion 146Bibliography 1479 Machine Learning-Based Swarm Optimization for Residential Demand-Based Electricity 149Yalamanchili Salini, Kiran Sree Pokkuluri, D. Deepa and Mary Joseph9.1 Introduction 1509.2 Relevant Works 1509.3 The Motivation 1529.4 Energy Optimization Proposal 1539.4.1 Appliance Scheduling Problem Formulation 1569.4.2 Problem of Optimization 1569.5 Discussions and Results 1589.6 Conclusion 163References 16310 Integration of Intelligent System and Big Data Environment to Find the Energy Utilization in Smart Public Buildings 167Sushil Bhardwaj, Bharath Sampath, Latifjon Kosimov and Shakhlokhon Kosimova10.1 Introduction 16810.2 Methods and Materials 16910.2.1 Data 16910.2.2 Methods 17010.2.2.1 Data Collection/Preprocessing Methods 17010.2.2.2 Predictive Modelling Techniques 17110.3 Results 17410.3.1 Energy Consumption Results Using ML Systems 17410.3.2 Design of an Intelligent Energy Management System Architecture 17710.4 Discussions 18010.4.1 Theory Contributions 18210.4.2 Practice Implications 18310.4.3 Research Limitations and Direction 18310.5 Conclusion 184Bibliography 18511 Multi-Objective Optimization Process to Analyze the Renewable Energy Storage and Distribution System from the Grid 187Dinesh G., Manisha G., Dina Allam and Ghada Elkady11.1 Introduction 18811.2 Review of Literature 18911.3 Work Proposal 19211.4 Results and Discussion 19511.5 Conclusion 200References 20012 Deep Learning and Multi-Horizontal Solar Energy Forecasting of Different Weather Conditions in Smart Cities 203Pradosh Kumar Sharma, M. V. Kesava Kumar, Mohd Wazih Ahmad and Radhika M.12.1 Introduction 20412.2 Description of Data 20612.2.1 Information About Photovoltaic Production 20712.2.2 Weather Information from the CWB 20712.2.3 AccuWeather Reports 20812.2.4 Local Weather Position/Pyrheliometer 20812.3 Information Preparation 20912.3.1 Classifying Data 20912.3.2 Encryption of Data 21012.4 Procedures and Assessment 21212.4.1 Artificial Neural Network 21212.4.2 Long Short-Term Memory 21212.4.3 Gated Recurrent Unit 21312.5 Results 21312.5.1 Findings from Hyperparameter Tuning 21312.5.2 Different Weather Data Groups’ Forecast Performance 21412.6 Conclusion 216Bibliography 21713 Machine Learning Models are Used to Analyze the Effectiveness of Daily Residential Area Energy Consumption 221Kapil Aggarwal, D. M. Kalai Selvi, Vijay Kumar Rayabharapu and K. S. Chakradhar13.1 Introduction 22213.2 Intelligent Energy Systems for the House 22313.2.1 Tracking 22313.2.2 Management 22313.2.3 Leadership 22413.2.4 Recording 22413.3 Advanced Plan for Demand Response 22413.4 Results 22813.5 Conclusion 231Bibliography 23214 Integration of AI and IoT Used to Manage and Secure the Renewable Energy Management in the Environment 235R. Swathi, M. Prabha, Ravichandran Sekar, Basi Reddy A., T. Aditya Sai Srinivas and R. Senthamil Selvan14.1 Introduction 23614.1.1 Efforts 23714.2 Smart IoT Device Setting Out and Energy-Saving Equipment 23814.2.1 Smart IoT Deployment 23814.2.2 Relevant Work 24014.3 Key AI-Based Energy-Efficient Network Issues 24014.3.1 Energy from Renewable Sources 24114.3.2 AI Technology 24214.3.2.1 Algorithm Regression 24314.3.2.2 Neural Networks 24414.3.2.3 SVM Algorithm 24414.3.2.4 Analysis Clusters 24514.3.2.5 Suggest Algorithm 24514.4 AI-Based Managing Framework for Multidimensional Smart IoT Devices 24514.4.1 Logistic Regression/Clustering Analysis Interlayer 24614.4.1.1 Logistic Regression Interlayer 24614.4.1.2 Clustering-Analysis Interlayer 24714.4.2 Regression-Based Intra-Layer Control 24814.4.3 Pushing and Caching with Recommendation Procedure 24914.5 Research Futures 24914.6 Conclusion 250References 25115 Hybrid Genetic Optimization and Particle Swarm Optimization for Enhanced Electricity Demand Forecasting Using Artificial Neural Networks 253V. Sharmila, Gaikar Vilas B., Rajiv Nayan and Pavithra G.15.1 Introduction 25415.2 Electricity Sector 25515.3 Methodology 25715.3.1 ANN Method 25715.3.2 Particle Swarm Optimization (PSO) 25715.4 ANN-GA-PSO Methods 26015.4.1 Estimating Two Forms Method 26015.4.2 Algorithm for Hybrid Optimization using GA-PSO 26115.4.3 Data Management and Computation 26115.4.4 Forecast Performance Evaluation 26115.5 Results 26215.5.1 Future Estimation 26415.5.2 The Correlation Between Gross Domestic Product (GDP) and the Electricity Demand 26615.6 Conclusion 266References 26716 Harmonizing Renewable Energy, IoT, and Economic Prosperity: A Multifaceted Analysis 271Sri Silpa Padmanabhuni, Pradeep K. G. M., Sai Pallavi Akkisetti and G. Jayalaxmi16.1 Introduction 27216.1.1 Designing of Smart Home Models 27316.1.2 Prediction of Electricity from Smart Home Models 27616.2 Literature Survey 27816.3 Proposed Methodology 28416.4 Conclusion 287References 28817 An Optimized Demand for Cost and Environment Benefits Towards Smart Residentials Using IOT and Machine Learning 291Hemlata and Manish Rai17.1 Introduction 29217.1.1 Overview of Smart-Based Systems 29217.1.2 Benefits of Machine-Based Learning Algorithms in Smart-Based Systems 29317.1.3 Challenges and Limitations of Machine-Based Learning Algorithms in Smart-Based Systems 29317.1.4 Real-World Applications of Machine-Based Learning Algorithms in Smart-Based Systems 29417.2 Literature Review 29417.3 Key Considerations for Implementing Machine-Based Learning Algorithms in Smart-Based Systems 302Conclusion 304References 30518 IoT-Enabled RBFNN MPPT Algorithm for High Gain SEPIC Converter in Grid-Tied Rooftop PV Applications 309Thomas Thangam, P. Kavitha, P. Nammalvar, D. Karthikeyan and V. Pujari18.1 Introduction 31018.2 Related Works 31118.3 Proposed System 31218.4 Results and Discussion 31818.5 Conclusion 321References 324Index 327