{"product_id":"sustainable-smart-homes-and-buildings-with-internet-of-things-hardback-9781394231485","title":"Sustainable Smart Homes and Buildings with Internet of Things (Hardback) 9781394231485","description":"\u003cfont face=\"Georgia\"\u003e\r\n\u003cp\u003e\u003cfont size=\"6\"\u003eSustainable Smart Homes and Buildings with Internet of Things\u003c\/font\u003e\u003cbr\u003e\r\n\r\n\r\n\r\n\r\n\r\n\u003c\/p\u003e\n\u003cp\u003e\u003cfont size=\"4\"\u003ePramod Singh Rathore (Edited by), Singh Rathore (Author), Abhishek Kumar (Edited by), Surbhi Bhatia (Edited by), Arwa Mashat (Edited by), Thippa Reddy Gadekal (Edited by)\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e9781394231485, Wiley\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003eHardback, published 29 November 2024\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e368 pages\u003cbr\u003e22.9 x 15.2 x 2.3 cm, 0.744 kg\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\r\n\r\n\r\n\u003cp align=\"justify\"\u003e\u003cstrong\u003e\u003cfont size=\"3\"\u003e\u003cp\u003e\u003cb\u003eWritten 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.\u003c\/b\u003e \u003c\/p\u003e\n\u003cp\u003e\u003ci\u003eSustainable Smart Homes and Buildings with Internet of Things \u003c\/i\u003e(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. \u003c\/p\u003e\n\u003cp\u003eThe 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. \u003c\/p\u003e\n\u003cp\u003eThe 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.\u003c\/p\u003e\u003c\/font\u003e\u003c\/strong\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e\u003cp\u003ePreface xv\u003c\/p\u003e \u003cp\u003e\u003cb\u003e1 Development of a Framework to Integrate Smart Home and Energy Operation Systems to Manage Energy Efficiency Through AI 1\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eSasikala P., S. Sivakumar, Murali Kalipindi and Makhan Kumbhkar\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e1.1 Introduction 2\u003c\/p\u003e \u003cp\u003e1.2 Research Idea Definitions 3\u003c\/p\u003e \u003cp\u003e1.2.1 A Service for Intelligence Awareness 3\u003c\/p\u003e \u003cp\u003e1.2.2 IAT Sensor 4\u003c\/p\u003e \u003cp\u003e1.2.3 IAT Smartphone 5\u003c\/p\u003e \u003cp\u003e1.2.4 IAT Smart Appliance 6\u003c\/p\u003e \u003cp\u003e1.2.5 Service-Based Intelligence Energy Efficiency 7\u003c\/p\u003e \u003cp\u003e1.2.6 Service Idea for Intelligence Target 8\u003c\/p\u003e \u003cp\u003e1.3 Algorithms for Intelligent Models 9\u003c\/p\u003e \u003cp\u003e1.3.1 Algorithm for IAT 9\u003c\/p\u003e \u003cp\u003e1.3.2 Algorithm of IE2S 11\u003c\/p\u003e \u003cp\u003e1.3.3 Algorithm for IST 11\u003c\/p\u003e \u003cp\u003e1.4 Analyzing and Implementing 12\u003c\/p\u003e \u003cp\u003e1.4.1 Sensory Things 12\u003c\/p\u003e \u003cp\u003e1.4.2 Server 13\u003c\/p\u003e \u003cp\u003e1.5 Conclusion 15\u003c\/p\u003e \u003cp\u003eBibliography 16\u003c\/p\u003e \u003cp\u003e\u003cb\u003e2 Development of a Hybrid System to Make the Decision and Optimization of Renewable Energy Sources 19\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eM. Jayakrishna, S. Sivakumar, Nalam Chandra Sekhar and Yabesh Abraham Durairaj Isravel\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e2.1 Introduction 20\u003c\/p\u003e \u003cp\u003e2.2 Related Work 20\u003c\/p\u003e \u003cp\u003e2.3 Methods of Modelling 22\u003c\/p\u003e \u003cp\u003e2.3.1 Designing a Hybrid Energy Infrastructure 22\u003c\/p\u003e \u003cp\u003e2.3.2 Modelling Web-Based SCADA Systems 22\u003c\/p\u003e \u003cp\u003e2.4 Methodology 24\u003c\/p\u003e \u003cp\u003e2.4.1 A Simulated Model 24\u003c\/p\u003e \u003cp\u003e2.4.1.1 Model Experiment 26\u003c\/p\u003e \u003cp\u003e2.5 Discussion and Result 27\u003c\/p\u003e \u003cp\u003e2.6 Conclusion 31\u003c\/p\u003e \u003cp\u003eBibliography 32\u003c\/p\u003e \u003cp\u003e\u003cb\u003e3 IoT-Based Renewable Energy Management Systems in Apartment 35\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eThulasi Bikku, S. Sivakumar, Sudha Arogya Mary Chinthamani and Pramoda Patro\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e3.1 Introduction 36\u003c\/p\u003e \u003cp\u003e3.2 Smart House Design Using Internet of Things 38\u003c\/p\u003e \u003cp\u003e3.3 Problem Statement 41\u003c\/p\u003e \u003cp\u003e3.4 The Proposed Methodology 42\u003c\/p\u003e \u003cp\u003e3.5 A Mathematical Framework 42\u003c\/p\u003e \u003cp\u003e3.5.1 Grid Model for Electricity 43\u003c\/p\u003e \u003cp\u003e3.5.2 Energy-Use Model 44\u003c\/p\u003e \u003cp\u003e3.5.3 Pricing Energy 45\u003c\/p\u003e \u003cp\u003e3.5.4 Demand-Reply Paradigm 45\u003c\/p\u003e \u003cp\u003e3.6 Optimize Design 46\u003c\/p\u003e \u003cp\u003e3.6.1 Objectives and Restrictions 46\u003c\/p\u003e \u003cp\u003e3.7 Discussion and Results 47\u003c\/p\u003e \u003cp\u003e3.7.1 The Provided Data 47\u003c\/p\u003e \u003cp\u003e3.8 Conclusion 48\u003c\/p\u003e \u003cp\u003eReferences 50\u003c\/p\u003e \u003cp\u003e\u003cb\u003e4 Framework of IoT-Based Meta Firewall System to Plan the Renewable Energy Consumption in Smart Homes or Buildings 53\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eMandeep Kaur Ghumman, A. Vinay Bhushan, Chetan Khemraj Lanjewar and Abhishek Choubey\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e4.1 Introduction 54\u003c\/p\u003e \u003cp\u003e4.1.1 Green Home 57\u003c\/p\u003e \u003cp\u003e4.1.2 Green Dorms 58\u003c\/p\u003e \u003cp\u003e4.2 Problem Formulation and System Model 58\u003c\/p\u003e \u003cp\u003e4.2.1 System Design 58\u003c\/p\u003e \u003cp\u003e4.2.2 The Research Goal 58\u003c\/p\u003e \u003cp\u003e4.2.2.1 Comfort Error 59\u003c\/p\u003e \u003cp\u003e4.2.2.2 Consumption of Energy 59\u003c\/p\u003e \u003cp\u003e4.2.2.3 Co 2 Emissions 59\u003c\/p\u003e \u003cp\u003e4.2.3 Baseline Methods 59\u003c\/p\u003e \u003cp\u003e4.3 Meta-Control Firewall Plus (IMCF+) 60\u003c\/p\u003e \u003cp\u003e4.3.1 Operation Summary 60\u003c\/p\u003e \u003cp\u003e4.3.2 Procedure for Amortization 61\u003c\/p\u003e \u003cp\u003e4.3.3 Algorithm for Green Plan (GP) 61\u003c\/p\u003e \u003cp\u003e4.3.4 Analysis of Performance 63\u003c\/p\u003e \u003cp\u003e4.4 Architecture of the IMCF+ System 63\u003c\/p\u003e \u003cp\u003e4.4.1 A System Architecture 63\u003c\/p\u003e \u003cp\u003e4.4.2 Graphical User Interface 65\u003c\/p\u003e \u003cp\u003e4.5 Trial Methods and Assessment 66\u003c\/p\u003e \u003cp\u003e4.5.1 Methods 66\u003c\/p\u003e \u003cp\u003e4.5.1.1 Datasets 66\u003c\/p\u003e \u003cp\u003e4.5.2 Evaluations of IMCF+ 67\u003c\/p\u003e \u003cp\u003e4.5.2.1 Evaluation of Households 67\u003c\/p\u003e \u003cp\u003e4.5.2.2 Evaluation of University Campus 69\u003c\/p\u003e \u003cp\u003e4.5.2.3 Hotel Apartment Evaluation 70\u003c\/p\u003e \u003cp\u003e4.5.3 Series of Micro-Benchmarks 70\u003c\/p\u003e \u003cp\u003e4.5.3.1 Series-1: Evaluation of Performance 70\u003c\/p\u003e \u003cp\u003e4.5.3.2 Series-2: K-Opt Assess 71\u003c\/p\u003e \u003cp\u003e4.5.3.3 Series-3: Evaluation of Initialization 72\u003c\/p\u003e \u003cp\u003e4.5.3.4 Series-4: Studying Energy Conservation 72\u003c\/p\u003e \u003cp\u003e4.6 Conclusion 73\u003c\/p\u003e \u003cp\u003eBibliography 74\u003c\/p\u003e \u003cp\u003e\u003cb\u003e5 Manage and Optimization of Renewable Energy Consumption Efficiency for Smart Homes 77\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eThulasi Bikku, V.O. Kavitha, Chetan Khemraj Lanjewar and Abhishek Choubey\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e5.1 Introduction 78\u003c\/p\u003e \u003cp\u003e5.2 Proposed Method 82\u003c\/p\u003e \u003cp\u003e5.2.1 Preprocessing 82\u003c\/p\u003e \u003cp\u003e5.2.2 Forecasting 84\u003c\/p\u003e \u003cp\u003e5.2.3 Optimization 86\u003c\/p\u003e \u003cp\u003e5.3 Results 88\u003c\/p\u003e \u003cp\u003e5.3.1 Testing Environment 88\u003c\/p\u003e \u003cp\u003e5.3.2 Dataset 88\u003c\/p\u003e \u003cp\u003e5.3.3 Assessment 89\u003c\/p\u003e \u003cp\u003e5.3.3.1 Preprocessing 89\u003c\/p\u003e \u003cp\u003e5.3.3.2 Forecast 90\u003c\/p\u003e \u003cp\u003e5.3.3.3 Optimization 90\u003c\/p\u003e \u003cp\u003e5.4 Discussion 92\u003c\/p\u003e \u003cp\u003e5.5 Conclusion 93\u003c\/p\u003e \u003cp\u003eReferences 93\u003c\/p\u003e \u003cp\u003e\u003cb\u003e6 Cost and Renewable Energy Management by IoT-Oriented Smart Home Based on Smart Grid Demand Response 97\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eOmprakash B., Jatinkumar Patel, Dhanaselvam J. and Shruti Bhargava Choubey\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e6.1 Introduction 98\u003c\/p\u003e \u003cp\u003e6.2 Methodology 99\u003c\/p\u003e \u003cp\u003e6.2.1 Edge MCU 100\u003c\/p\u003e \u003cp\u003e6.2.2 Pro Mini Arduino 101\u003c\/p\u003e \u003cp\u003e6.2.3 Measurement of Current and Voltage 102\u003c\/p\u003e \u003cp\u003e6.2.4 Blynk, A Creator of Interfaces for iOS and Android Platforms 104\u003c\/p\u003e \u003cp\u003e6.3 System Design 104\u003c\/p\u003e \u003cp\u003e6.4 Results 107\u003c\/p\u003e \u003cp\u003e6.5 Conclusion 110\u003c\/p\u003e \u003cp\u003eBibliography 112\u003c\/p\u003e \u003cp\u003e\u003cb\u003e7 IoT-Based Smart Green Building Energy Management System 115\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eRahama Salman, Ghada Elkady, Mukta Sandhu and Sandeep Gupta\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e7.1 Introduction 116\u003c\/p\u003e \u003cp\u003e7.2 Methodology 118\u003c\/p\u003e \u003cp\u003e7.3 Results of Construction 121\u003c\/p\u003e \u003cp\u003e7.3.1 Hypotheses 121\u003c\/p\u003e \u003cp\u003e7.3.2 DPM Data Creation 121\u003c\/p\u003e \u003cp\u003e7.3.3 Room Power Management (Face Recognition), Power-Cut Feature 122\u003c\/p\u003e \u003cp\u003e7.4 Working Model 123\u003c\/p\u003e \u003cp\u003e7.4.1 Short-Term Load Forecasting (RT-STLF): Five Primary Blocks Make Up the RT-STLF 123\u003c\/p\u003e \u003cp\u003e7.4.2 Manage Room Power 125\u003c\/p\u003e \u003cp\u003e7.4.3 IoT Data Update 126\u003c\/p\u003e \u003cp\u003e7.5 Results of Testing 126\u003c\/p\u003e \u003cp\u003e7.5.1 Face Recognition (Classification) Accuracy 126\u003c\/p\u003e \u003cp\u003e7.5.2 Forecasting Methodologies Comparison 128\u003c\/p\u003e \u003cp\u003e7.6 Conclusion 130\u003c\/p\u003e \u003cp\u003eReferences 130\u003c\/p\u003e \u003cp\u003e\u003cb\u003e8 The Framework of IoT-Based Paradigms to Renewable Power Utilization and Distribution by Microgrid 133\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eKannan Kaliappan, Basi Reddy A., D. Muthukumaran, Gopinath S., T. Aditya Sai Srinivas and R. Senthamil Selvan\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e8.1 Introduction 134\u003c\/p\u003e \u003cp\u003e8.2 Related Work 135\u003c\/p\u003e \u003cp\u003e8.3 Intelligent Power System Design 137\u003c\/p\u003e \u003cp\u003e8.3.1 Connected Devices Network 138\u003c\/p\u003e \u003cp\u003e8.3.1.1 Methods of Processing and Computing 139\u003c\/p\u003e \u003cp\u003e8.3.1.2 Capacity for Storage 139\u003c\/p\u003e \u003cp\u003e8.3.1.3 Optimizing Energy Use in Microgrids 140\u003c\/p\u003e \u003cp\u003e8.4 Daily External Energy Requirements 145\u003c\/p\u003e \u003cp\u003e8.4.1 Factory Robots 145\u003c\/p\u003e \u003cp\u003e8.4.2 The Topic of Discussion Pertains to Domestic or Home Robots 146\u003c\/p\u003e \u003cp\u003e8.4.3 Robotic Doctors 146\u003c\/p\u003e \u003cp\u003e8.5 Conclusion 146\u003c\/p\u003e \u003cp\u003eBibliography 147\u003c\/p\u003e \u003cp\u003e\u003cb\u003e9 Machine Learning-Based Swarm Optimization for Residential Demand-Based Electricity 149\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eYalamanchili Salini, Kiran Sree Pokkuluri, D. Deepa and Mary Joseph\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e9.1 Introduction 150\u003c\/p\u003e \u003cp\u003e9.2 Relevant Works 150\u003c\/p\u003e \u003cp\u003e9.3 The Motivation 152\u003c\/p\u003e \u003cp\u003e9.4 Energy Optimization Proposal 153\u003c\/p\u003e \u003cp\u003e9.4.1 Appliance Scheduling Problem Formulation 156\u003c\/p\u003e \u003cp\u003e9.4.2 Problem of Optimization 156\u003c\/p\u003e \u003cp\u003e9.5 Discussions and Results 158\u003c\/p\u003e \u003cp\u003e9.6 Conclusion 163\u003c\/p\u003e \u003cp\u003eReferences 163\u003c\/p\u003e \u003cp\u003e\u003cb\u003e10 Integration of Intelligent System and Big Data Environment to Find the Energy Utilization in Smart Public Buildings 167\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eSushil Bhardwaj, Bharath Sampath, Latifjon Kosimov and Shakhlokhon Kosimova\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e10.1 Introduction 168\u003c\/p\u003e \u003cp\u003e10.2 Methods and Materials 169\u003c\/p\u003e \u003cp\u003e10.2.1 Data 169\u003c\/p\u003e \u003cp\u003e10.2.2 Methods 170\u003c\/p\u003e \u003cp\u003e10.2.2.1 Data Collection\/Preprocessing Methods 170\u003c\/p\u003e \u003cp\u003e10.2.2.2 Predictive Modelling Techniques 171\u003c\/p\u003e \u003cp\u003e10.3 Results 174\u003c\/p\u003e \u003cp\u003e10.3.1 Energy Consumption Results Using ML Systems 174\u003c\/p\u003e \u003cp\u003e10.3.2 Design of an Intelligent Energy Management System Architecture 177\u003c\/p\u003e \u003cp\u003e10.4 Discussions 180\u003c\/p\u003e \u003cp\u003e10.4.1 Theory Contributions 182\u003c\/p\u003e \u003cp\u003e10.4.2 Practice Implications 183\u003c\/p\u003e \u003cp\u003e10.4.3 Research Limitations and Direction 183\u003c\/p\u003e \u003cp\u003e10.5 Conclusion 184\u003c\/p\u003e \u003cp\u003eBibliography 185\u003c\/p\u003e \u003cp\u003e\u003cb\u003e11 Multi-Objective Optimization Process to Analyze the Renewable Energy Storage and Distribution System from the Grid 187\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eDinesh G., Manisha G., Dina Allam and Ghada Elkady\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e11.1 Introduction 188\u003c\/p\u003e \u003cp\u003e11.2 Review of Literature 189\u003c\/p\u003e \u003cp\u003e11.3 Work Proposal 192\u003c\/p\u003e \u003cp\u003e11.4 Results and Discussion 195\u003c\/p\u003e \u003cp\u003e11.5 Conclusion 200\u003c\/p\u003e \u003cp\u003eReferences 200\u003c\/p\u003e \u003cp\u003e\u003cb\u003e12 Deep Learning and Multi-Horizontal Solar Energy Forecasting of Different Weather Conditions in Smart Cities 203\u003cbr\u003e \u003c\/b\u003e\u003ci\u003ePradosh Kumar Sharma, M. V. Kesava Kumar, Mohd Wazih Ahmad and Radhika M.\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e12.1 Introduction 204\u003c\/p\u003e \u003cp\u003e12.2 Description of Data 206\u003c\/p\u003e \u003cp\u003e12.2.1 Information About Photovoltaic Production 207\u003c\/p\u003e \u003cp\u003e12.2.2 Weather Information from the CWB 207\u003c\/p\u003e \u003cp\u003e12.2.3 AccuWeather Reports 208\u003c\/p\u003e \u003cp\u003e12.2.4 Local Weather Position\/Pyrheliometer 208\u003c\/p\u003e \u003cp\u003e12.3 Information Preparation 209\u003c\/p\u003e \u003cp\u003e12.3.1 Classifying Data 209\u003c\/p\u003e \u003cp\u003e12.3.2 Encryption of Data 210\u003c\/p\u003e \u003cp\u003e12.4 Procedures and Assessment 212\u003c\/p\u003e \u003cp\u003e12.4.1 Artificial Neural Network 212\u003c\/p\u003e \u003cp\u003e12.4.2 Long Short-Term Memory 212\u003c\/p\u003e \u003cp\u003e12.4.3 Gated Recurrent Unit 213\u003c\/p\u003e \u003cp\u003e12.5 Results 213\u003c\/p\u003e \u003cp\u003e12.5.1 Findings from Hyperparameter Tuning 213\u003c\/p\u003e \u003cp\u003e12.5.2 Different Weather Data Groups’ Forecast Performance 214\u003c\/p\u003e \u003cp\u003e12.6 Conclusion 216\u003c\/p\u003e \u003cp\u003eBibliography 217\u003c\/p\u003e \u003cp\u003e\u003cb\u003e13 Machine Learning Models are Used to Analyze the Effectiveness of Daily Residential Area Energy Consumption 221\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eKapil Aggarwal, D. M. Kalai Selvi, Vijay Kumar Rayabharapu and K. S. Chakradhar\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e13.1 Introduction 222\u003c\/p\u003e \u003cp\u003e13.2 Intelligent Energy Systems for the House 223\u003c\/p\u003e \u003cp\u003e13.2.1 Tracking 223\u003c\/p\u003e \u003cp\u003e13.2.2 Management 223\u003c\/p\u003e \u003cp\u003e13.2.3 Leadership 224\u003c\/p\u003e \u003cp\u003e13.2.4 Recording 224\u003c\/p\u003e \u003cp\u003e13.3 Advanced Plan for Demand Response 224\u003c\/p\u003e \u003cp\u003e13.4 Results 228\u003c\/p\u003e \u003cp\u003e13.5 Conclusion 231\u003c\/p\u003e \u003cp\u003eBibliography 232\u003c\/p\u003e \u003cp\u003e\u003cb\u003e14 Integration of AI and IoT Used to Manage and Secure the Renewable Energy Management in the Environment 235\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eR. Swathi, M. Prabha, Ravichandran Sekar, Basi Reddy A., T. Aditya Sai Srinivas and R. Senthamil Selvan\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e14.1 Introduction 236\u003c\/p\u003e \u003cp\u003e14.1.1 Efforts 237\u003c\/p\u003e \u003cp\u003e14.2 Smart IoT Device Setting Out and Energy-Saving Equipment 238\u003c\/p\u003e \u003cp\u003e14.2.1 Smart IoT Deployment 238\u003c\/p\u003e \u003cp\u003e14.2.2 Relevant Work 240\u003c\/p\u003e \u003cp\u003e14.3 Key AI-Based Energy-Efficient Network Issues 240\u003c\/p\u003e \u003cp\u003e14.3.1 Energy from Renewable Sources 241\u003c\/p\u003e \u003cp\u003e14.3.2 AI Technology 242\u003c\/p\u003e \u003cp\u003e14.3.2.1 Algorithm Regression 243\u003c\/p\u003e \u003cp\u003e14.3.2.2 Neural Networks 244\u003c\/p\u003e \u003cp\u003e14.3.2.3 SVM Algorithm 244\u003c\/p\u003e \u003cp\u003e14.3.2.4 Analysis Clusters 245\u003c\/p\u003e \u003cp\u003e14.3.2.5 Suggest Algorithm 245\u003c\/p\u003e \u003cp\u003e14.4 AI-Based Managing Framework for Multidimensional Smart IoT Devices 245\u003c\/p\u003e \u003cp\u003e14.4.1 Logistic Regression\/Clustering Analysis Interlayer 246\u003c\/p\u003e \u003cp\u003e14.4.1.1 Logistic Regression Interlayer 246\u003c\/p\u003e \u003cp\u003e14.4.1.2 Clustering-Analysis Interlayer 247\u003c\/p\u003e \u003cp\u003e14.4.2 Regression-Based Intra-Layer Control 248\u003c\/p\u003e \u003cp\u003e14.4.3 Pushing and Caching with Recommendation Procedure 249\u003c\/p\u003e \u003cp\u003e14.5 Research Futures 249\u003c\/p\u003e \u003cp\u003e14.6 Conclusion 250\u003c\/p\u003e \u003cp\u003eReferences 251\u003c\/p\u003e \u003cp\u003e\u003cb\u003e15 Hybrid Genetic Optimization and Particle Swarm Optimization for Enhanced Electricity Demand Forecasting Using Artificial Neural Networks 253\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eV. Sharmila, Gaikar Vilas B., Rajiv Nayan and Pavithra G.\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e15.1 Introduction 254\u003c\/p\u003e \u003cp\u003e15.2 Electricity Sector 255\u003c\/p\u003e \u003cp\u003e15.3 Methodology 257\u003c\/p\u003e \u003cp\u003e15.3.1 ANN Method 257\u003c\/p\u003e \u003cp\u003e15.3.2 Particle Swarm Optimization (PSO) 257\u003c\/p\u003e \u003cp\u003e15.4 ANN-GA-PSO Methods 260\u003c\/p\u003e \u003cp\u003e15.4.1 Estimating Two Forms Method 260\u003c\/p\u003e \u003cp\u003e15.4.2 Algorithm for Hybrid Optimization using GA-PSO 261\u003c\/p\u003e \u003cp\u003e15.4.3 Data Management and Computation 261\u003c\/p\u003e \u003cp\u003e15.4.4 Forecast Performance Evaluation 261\u003c\/p\u003e \u003cp\u003e15.5 Results 262\u003c\/p\u003e \u003cp\u003e15.5.1 Future Estimation 264\u003c\/p\u003e \u003cp\u003e15.5.2 The Correlation Between Gross Domestic Product (GDP) and the Electricity Demand 266\u003c\/p\u003e \u003cp\u003e15.6 Conclusion 266\u003c\/p\u003e \u003cp\u003eReferences 267\u003c\/p\u003e \u003cp\u003e\u003cb\u003e16 Harmonizing Renewable Energy, IoT, and Economic Prosperity: A Multifaceted Analysis 271\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eSri Silpa Padmanabhuni, Pradeep K. G. M., Sai Pallavi Akkisetti and G. Jayalaxmi\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e16.1 Introduction 272\u003c\/p\u003e \u003cp\u003e16.1.1 Designing of Smart Home Models 273\u003c\/p\u003e \u003cp\u003e16.1.2 Prediction of Electricity from Smart Home Models 276\u003c\/p\u003e \u003cp\u003e16.2 Literature Survey 278\u003c\/p\u003e \u003cp\u003e16.3 Proposed Methodology 284\u003c\/p\u003e \u003cp\u003e16.4 Conclusion 287\u003c\/p\u003e \u003cp\u003eReferences 288\u003c\/p\u003e \u003cp\u003e\u003cb\u003e17 An Optimized Demand for Cost and Environment Benefits Towards Smart Residentials Using IOT and Machine Learning 291\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eHemlata and Manish Rai\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e17.1 Introduction 292\u003c\/p\u003e \u003cp\u003e17.1.1 Overview of Smart-Based Systems 292\u003c\/p\u003e \u003cp\u003e17.1.2 Benefits of Machine-Based Learning Algorithms in Smart-Based Systems 293\u003c\/p\u003e \u003cp\u003e17.1.3 Challenges and Limitations of Machine-Based Learning Algorithms in Smart-Based Systems 293\u003c\/p\u003e \u003cp\u003e17.1.4 Real-World Applications of Machine-Based Learning Algorithms in Smart-Based Systems 294\u003c\/p\u003e \u003cp\u003e17.2 Literature Review 294\u003c\/p\u003e \u003cp\u003e17.3 Key Considerations for Implementing Machine-Based Learning Algorithms in Smart-Based Systems 302\u003c\/p\u003e \u003cp\u003eConclusion 304\u003c\/p\u003e \u003cp\u003eReferences 305\u003c\/p\u003e \u003cp\u003e\u003cb\u003e18 IoT-Enabled RBFNN MPPT Algorithm for High Gain SEPIC Converter in Grid-Tied Rooftop PV Applications 309\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eThomas Thangam, P. Kavitha, P. Nammalvar, D. Karthikeyan and V. Pujari\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e18.1 Introduction 310\u003c\/p\u003e \u003cp\u003e18.2 Related Works 311\u003c\/p\u003e \u003cp\u003e18.3 Proposed System 312\u003c\/p\u003e \u003cp\u003e18.4 Results and Discussion 318\u003c\/p\u003e \u003cp\u003e18.5 Conclusion 321\u003c\/p\u003e \u003cp\u003eReferences 324\u003c\/p\u003e \u003cp\u003eIndex 327\u003c\/p\u003e\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003eSubject Areas: Civil engineering, surveying \u0026amp; building [\u003ca title=\"See our other books on Civil engineering, surveying \u0026amp; building\" href=\"https:\/\/freshlyprintedbooks.co.uk\/search?q=%22Civil%20engineering,%20surveying%20\u0026amp;%20building%20%5BTN%5D%22\"\u003eTN\u003c\/a\u003e]\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\u003c\/font\u003e","brand":"Wiley-Scrivener","offers":[{"title":"Brand New","offer_id":52433221779736,"sku":"9781394231485","price":151.99,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0730\/2037\/5320\/files\/9781394231485.jpg?v=1784852098","url":"https:\/\/freshlyprintedbooks.co.uk\/products\/sustainable-smart-homes-and-buildings-with-internet-of-things-hardback-9781394231485","provider":"Freshly Printed Books","version":"1.0","type":"link"}