Details

Hybrid Intelligent Approaches for Smart Energy


Hybrid Intelligent Approaches for Smart Energy

Practical Applications
Next Generation Computing and Communication Engineering 1. Aufl.

von: John A, Senthil Kumar Mohan, P. Sanjeevikumar, Yasir Hamid

150,99 €

Verlag: Wiley
Format: EPUB
Veröffentl.: 30.09.2022
ISBN/EAN: 9781119821854
Sprache: englisch
Anzahl Seiten: 336

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Beschreibungen

<b>HYBRID INTELLIGENT APPROACHES FOR SMART ENERGY</b> <p><b>Green technologies and cleaner energy are two of the most important topics facing our world today, and the march toward efficient energy systems, smart cities, and other green technologies, has been, and continues to be, a long and intricate one. Books like this one keep the veteran engineer and student, alike, up to date on current trends in the technology and offer a reference for the industry for its practical applications.</b> <p>Energy optimization and consumption prediction are necessary to prevent energy waste, schedule energy usage, and reduce the cost. Today, smart computing technologies are slowly replacing the traditional computational methods in energy optimization, consumption, scheduling, and usage. Smart computing is an important core technology in today’s scientific and engineering environment. Smart computation techniques such as artificial intelligence, machine learning, deep learning and Internet of Things (IoT) are the key role players in emerging technologies across different applications, industries, and other areas. These newer, smart computation techniques are incorporated with traditional computation and scheduling methods to reduce power usage in areas such as distributed environment, healthcare, smart cities, agriculture and various functional areas. <p>The scope of this book is to bridge the gap between traditional power consumption methods and modern consumptions methods using smart computation methods. This book addresses the various limitations, issues and challenges of traditional energy consumption methods and provides solutions for various issues using modern smart computation technologies. These smart technologies play a significant role in power consumption, and they are cheaper compared to traditional technologies. The significant limitations of energy usage and optimizations are rectified using smart computations techniques, and the computation techniques are applied across a wide variety of industries and engineering areas. Valuable as reference for engineers, scientists, students, and other professionals across many areas, this is a must-have for any library.
<p>List of Contributors xiii</p> <p>Preface xv</p> <p>Acknowledgements xix</p> <p> </p> <p><b>1 Review and Analysis of Machine Learning Based Techniques for Load Forecasting in Smart Grid System 1<br /> </b><i>Shihabudheen KV and Sheik Mohammed S</i></p> <p>1.1 Introduction 2</p> <p>1.2 Forecasting Methodology 4</p> <p>1.3 AI-Based Prediction Methods 5</p> <p>1.3.1 Single Prediction Methods 5</p> <p>1.3.1.1 Linear Regression 5</p> <p>1.3.1.2 Artificial Neural Networks (ANN) 7</p> <p>1.3.1.3 Support Vector Regression (SVR) 8</p> <p>1.3.1.4 Extreme Learning Machine 9</p> <p>1.3.1.5 Neuro-Fuzzy Techniques 10</p> <p>1.3.1.6 Deep Learning Techniques 11</p> <p>1.3.2 Hybrid Prediction Methods 12</p> <p>1.3.2.1 Combined AI-Based Prediction Techniques 12</p> <p>1.3.2.2 Signal Decomposition Based Prediction Techniques 13</p> <p>1.3.2.3 EMD Based Decomposition 14</p> <p>1.3.2.4 Wavelet Based Decomposition 14</p> <p>1.4 Results and Discussions 15</p> <p>1.4.1 Description of Dataset 15</p> <p>1.4.2 Performance Analysis of Single Prediction Methods for Load Forecasting 16</p> <p>1.4.2.1 Feature Selection 16</p> <p>1.4.2.2 Optimal Parameter Selection 17</p> <p>1.4.2.3 Prediction Results of Single Prediction Methods 17</p> <p>1.4.3 Performance Analysis of Hybrid Prediction Methods for Load Forecasting 17</p> <p>1.4.4 Comparative Analysis 21</p> <p>1.5 Conclusion 22</p> <p>References 23</p> <p><b>2 Energy Optimized Techniques in Cloud and Fog Computing 27<br /> </b><i>N.M. Balamurugan, TKS Rathish babu, K Maithili and M. Adimoolam</i></p> <p>2.1 Introduction 28</p> <p>2.2 Fog Computing and Its Applications 33</p> <p>2.3 Energy Optimization Techniques in Cloud Computing 38</p> <p>2.4 Energy Optimization Techniques in Fog Computing 42</p> <p>2.5 Summary and Conclusions 44</p> <p>References 45</p> <p><b>3 Energy-Efficient Cloud Computing Techniques for Next Generation: Ways of Establishing and Strategies for Future Developments 49<br /> </b><i>Praveen Mishra, M. Sivaram, M. Arvindhan, A. Daniel and Raju Ranjan</i></p> <p>3.1 Introduction 50</p> <p>3.2 A Layered Model of Cloud Computing 52</p> <p>3.2.1 System of Architecture 53</p> <p>3.3 Energy and Cloud Computing 54</p> <p>3.3.1 Performance of Network 55</p> <p>3.3.2 Reliability of Servers 55</p> <p>3.3.3 Forward Challenges 55</p> <p>3.3.4 Quality of Machinery 56</p> <p>3.4 Saving Electricity Prices 56</p> <p>3.4.1 Renewable Energy 57</p> <p>3.4.2 Cloud Freedom 57</p> <p>3.5 Energy-Efficient Cloud Usage 58</p> <p>3.6 Energy-Aware Edge OS 58</p> <p>3.7 Energy Efficient Edge Computing Based on Machine Learning 59</p> <p>3.8 Energy Aware Computing Offloading 61</p> <p>3.8.1 Energy Usage Calculation and Simulation 63</p> <p>3.9 Comments and Directions for the Future 63</p> <p>References 64</p> <p><b>4 Energy Optimization Using Silicon Dioxide Composite and Analysis of Wire Electrical Discharge Machining Characteristics 67<br /> </b><i>M.S. Kumaravel, N. Alagumurthi and P. Mathiyalagan</i></p> <p>4.1 Introduction 67</p> <p>4.2 Materials and Methods 69</p> <p>4.3 Results and Discussion 72</p> <p>4.3.1 XRD Analysis 72</p> <p>4.3.2 SEM Analysis 73</p> <p>4.3.3 Grey Relational Analysis (GRA) 73</p> <p>4.3.4 Main Effects Graph 76</p> <p>4.3.5 Analysis of Variance (ANOVA) 77</p> <p>4.3.6 Confirmatory Test 78</p> <p>4.4 Conclusion 80</p> <p>Acknowledgement 80</p> <p>References 80</p> <p><b>5 Optimal Planning of Renewable DG and Reconfiguration of Distribution Network Considering Multiple Objectives Using PSO Technique for Different Scenarios 83<br /> </b><i>Balmukund Kumar and Aashish Kumar Bohre</i></p> <p>5.1 Introduction 84</p> <p>5.2 Literature Review for Recent Development in DG Planning and Network Reconfiguration 84</p> <p>5.3 System Performance Parameters and Index 87</p> <p>5.4 Proposed Method 88</p> <p>5.4.1 Formulation of Multi-Objective Fitness Function 88</p> <p>5.4.2 Backward-Forward-Sweep Load Flow Based on BIBC-BCBV Method 89</p> <p>5.5 PSO Based Optimization 90</p> <p>5.6 Test Systems 92</p> <p>5.7 Results and Discussions 92</p> <p>5.8 Conclusions 101</p> <p>References 102</p> <p><b>6 Investigation of Energy Optimization for Spectrum Sensing in Distributed Cooperative IoT Network Using Deep Learning Techniques 107<br /> </b><i>M. Pavithra, R. Rajmohan, T. Ananth Kumar, S. Usharani and P. Manju Bala</i></p> <p>6.1 Introduction 108</p> <p>6.2 IoT Architecture 111</p> <p>6.3 Cognitive Spectrum Sensing for Distributed Shared Network 113</p> <p>6.4 Intelligent Distributed Sensing 115</p> <p>6.5 Heuristic Search Based Solutions 117</p> <p>6.6 Selecting IoT Nodes Using Framework 118</p> <p>6.7 Training With Reinforcement Learning 119</p> <p>6.8 Model Validation 120</p> <p>6.9 Performance Evaluations 123</p> <p>6.10 Conclusion and Future Work 125</p> <p>References 126</p> <p><b>7 Road Network Energy Optimization Using IoT and Deep Learning 129<br /> </b><i>N. M. Balamurugan, N. Revathi and R. Gayathri</i></p> <p>7.1 Introduction 129</p> <p>7.2 Road Network 132</p> <p>7.2.1 Types of Road 132</p> <p>7.2.2 Road Structure Representation 134</p> <p>7.2.3 Intelligent Road Lighting System 135</p> <p>7.3 Road Anomaly Detection 139</p> <p>7.4 Role of IoT in Road Network Energy Optimization 141</p> <p>7.5 Deep Learning of Road Network Traffic 142</p> <p>7.6 Road Safety and Security 142</p> <p>7.7 Conclusion 144</p> <p>References 144</p> <p><b>8 Energy Optimization in Smart Homes and Buildings 147<br /> </b><i>S. Sathya, G. Karthi, A. Suresh Kumar and S. Prakash</i></p> <p>8.1 Introduction 148</p> <p>8.2 Study of Energy Management 150</p> <p>8.3 Energy Optimization in Smart Home 150</p> <p>8.3.1 Power Spent in Smart-Building 153</p> <p>8.3.2 Hurdles of Execution in Energy Optimization 156</p> <p>8.3.3 Barriers to Assure SH Technologies 156</p> <p>8.4 Scope and Study Methodology 157</p> <p>8.4.1 Power Cost of SH 158</p> <p>8.5 Conclusion 159</p> <p>References 159</p> <p><b>9 Machine Learning Based Approach for Energy Management in the Smart City Revolution 161<br /> </b><i>Deepica S., S. Kalavathi, Angelin Blessy J. and D. Maria Manuel Vianny</i></p> <p>9.1 Introduction 162</p> <p>9.1.1 Smart City: What is the Need? 162</p> <p>9.1.2 Development of Smart City 163</p> <p>9.2 Need for Energy Optimization 166</p> <p>9.3 Methods for Energy Effectiveness in Smart City 166</p> <p>9.3.1 Smart Electricity Grids 166</p> <p>9.3.2 Smart Transportation and Smart Traffic Management 169</p> <p>9.3.3 Natural Ventilation Effect 172</p> <p>9.4 Role of Machine Learning in Smart City Energy Optimization 173</p> <p>9.4.1 Machine Learning: An Overview 173</p> <p>9.5 Machine Learning Applications in Smart City 175</p> <p>9.6 Conclusion 177</p> <p>References 178</p> <p><b>10 Design of an Energy Efficient IoT System for Poultry Farm Management 181<br /> </b><i>G. Rajakumar, G. Gnana Jenifer, T. Ananth Kumar and T. S. Arun Samuel</i></p> <p>10.1 Introduction 182</p> <p>10.2 Literature Survey 183</p> <p>10.3 Proposed Methodology 187</p> <p>10.3.1 Monitoring and Control Module 188</p> <p>10.3.2 Monitoring Temperature 188</p> <p>10.3.3 Monitoring Humidity 189</p> <p>10.3.4 Monitoring Air Pollutants 189</p> <p>10.3.5 Artificial Lightning 190</p> <p>10.3.6 Monitoring Water Level 190</p> <p>10.4 Hardware Components 190</p> <p>10.4.1 Arduino UNO 190</p> <p>10.4.2 Temperature Sensor 190</p> <p>10.4.3 Humidity Sensor 191</p> <p>10.4.4 Gas Sensor 192</p> <p>10.4.5 Water Level Sensor 192</p> <p>10.4.6 LDR Sensor 193</p> <p>10.4.7 GSM (Global System for Mobile Communication) Modem 194</p> <p>10.5 Results and Discussion 195</p> <p>10.5.1 Hardware Module 195</p> <p>10.5.2 Monitoring Temperature 196</p> <p>10.5.3 Monitoring Gas Content 198</p> <p>10.5.4 Monitoring Humidity 198</p> <p>10.5.5 Artificial Lighting 198</p> <p>10.5.6 Monitoring Water Level 198</p> <p>10.5.7 Poultry Energy-Efficiency Tips 199</p> <p>10.6 Conclusion 201</p> <p>References 203</p> <p><b>11 IoT Based Energy Optimization in Smart Farming Using AI 205<br /> </b><i>N. Padmapriya, T. Ananth Kumar, R. Aswini, R. Rajmohan, P. Kanimozhi and M. Pavithra</i></p> <p>11.1 Introduction 206</p> <p>11.2 IoT in Smart Farming 208</p> <p>11.2.1 Benefits of Using IoT in Agriculture 208</p> <p>11.2.2 The IoT-Based Smart Farming Cycle 209</p> <p>11.3 AI in Smart Farming 210</p> <p>11.3.1 Artificial Intelligence Revolutionises Agriculture 210</p> <p>11.4 Energy Optimization in Smart Farming 211</p> <p>11.4.1 Energy Optimization in Smart Farming Using IoT and AI 212</p> <p>11.5 Experimental Results 215</p> <p>11.5.1 Analysis of Network Throughput 216</p> <p>11.5.2 Analysis of Network Latency 217</p> <p>11.5.3 Analysis of Energy Consumption 218</p> <p>11.5.4 Applications of IoT and AI in Smart Farming 219</p> <p>11.6 Conclusion 220</p> <p>References 221</p> <p><b>12 Smart Energy Management Techniques in Industries 5.0 225<br /> </b><i>S. Usharani, P. Manju Bala, T. Ananth Kumar, R. Rajmohan and M. Pavithra</i></p> <p>12.1 Introduction 226</p> <p>12.2 Related Work 227</p> <p>12.3 General Smart Grid Architecture 229</p> <p>12.3.1 Energy Sub-Sectors 230</p> <p>12.3.1.1 Smart Grid: State-of-the-Art Inside Energy Sector 230</p> <p>12.3.2 EV and Power-to-Gas: State-of-the-Art within Biomass and Transport 231</p> <p>12.3.3 Constructing Zero Net Energy (CZNE): State-of-the-Art Inside Field of Buildings 233</p> <p>12.3.4 Manufacturing Industry: State-of-the-Art 234</p> <p>12.3.5 Smart Energy Systems 235</p> <p>12.4 Smart Control of Power 236</p> <p>12.4.1 Smart Control Thermal System 236</p> <p>12.4.2 Smart Control Cross-Sector 237</p> <p>12.5 Subsector Solutions 238</p> <p>12.6 Smart Energy Management Challenges in Smart Factories 239</p> <p>12.7 Smart Energy Management Importance 240</p> <p>12.8 System Design 241</p> <p>12.9 Smart Energy Management for Smart Grids 241</p> <p>12.10 Experimental Results 247</p> <p>12.11 Conclusions 250</p> <p>References 251</p> <p><b>13 Energy Optimization Techniques in Telemedicine Using Soft Computing 253<br /> </b><i>R. Indrakumari</i></p> <p>13.1 Introduction 253</p> <p>13.2 Essential Features of Telemedicine 255</p> <p>13.3 Issues Related to Telemedicine Networks 256</p> <p>13.4 Telemedicine Contracts 257</p> <p>13.5 Energy Efficiency: Policy and Technology Issue 258</p> <p>13.5.1 Soft Computing 258</p> <p>13.5.2 Fuzzy Logic 260</p> <p>13.5.3 Artificial Intelligence 260</p> <p>13.5.4 Genetic Algorithms 263</p> <p>13.5.5 Expert System 263</p> <p>13.5.6 Expert System Based on Fuzzy Logic Rules 264</p> <p>13.6 Patient Condition Monitoring 266</p> <p>13.7 Analysis of Physiological Signals and Data Processing 271</p> <p>13.8 M-Health Monitoring System Architecture 272</p> <p>13.9 Conclusions 275</p> <p>References 276</p> <p><b>14 Healthcare: Energy Optimization Techniques Using IoT and Machine Learning 279<br /> </b><i>G. Vallathan, Senthilkumar Meyyappan and T. Rajani</i></p> <p>14.1 Introduction 280</p> <p>14.2 Energy Optimization Process 281</p> <p>14.3 Energy Optimization Techniques in Healthcare 283</p> <p>14.3.1 Energy Optimization in Building 283</p> <p>14.3.2 Machine Learning for Energy Optimization 284</p> <p>14.3.3 Reinforcement Learning for Energy Optimization 286</p> <p>14.3.4 Energy Optimization of Sustainable Internet of Things (IoT) 287</p> <p>14.4 Future Direction of Energy Optimizations 288</p> <p>14.5 Conclusion 289</p> <p>References 289</p> <p><b>15 Case Study of Energy Optimization: Electric Vehicle Energy Consumption Minimization Using Genetic Algorithm 291<br /> </b><i>Pedram Asef</i></p> <p>15.1 Introduction 292</p> <p>15.2 Vehicle Modelling to Optimisation 295</p> <p>15.2.1 Vehicle Mathematical Modelling 295</p> <p>15.2.2 Vehicle Model Optimisation Process: Applied Genetic Algorithm 298</p> <p>15.2.3 GA Optimisation Results and Discussion 301</p> <p>15.3 Conclusion 305</p> <p>References 305</p> <p>About the Editors 307</p> <p>Index 309</p>
<p><b>John A, PhD,</b> is an assistant professor at Galgotias University, Greater Noida, India, and he received his PhD in computer science and engineering from Manonmaniam Sundaranar University, Tirunelveli, India. He has presented papers in various national and international conferences and has published papers in scientific journals. <p><b>Senthil Kumar Mohan, PhD,</b> is an associate professor in the Department of Software and System Engineering at the School of Information Technology and Engineering, Vellore Institute of Technology, Vellore, India. He received his PhD in engineering and technology from Vellore Institute of Technology, and he has contributed to many research articles in various technical journals and conferences. <p><b>Sanjeevikumar Padmanaban, PhD,</b> is a faculty member with the Department of Energy Technology, Aalborg University, Esbjerg, Denmark. He has almost ten years of teaching, research and industrial experience and is an associate editor on a number of international scientific refereed journals. He has published more than 300 research papers and has won numerous awards for his research and teaching. <p><b>Yasir Hamid, PhD,</b> is an assistant professor in the Department of Information Security Engineering Technology at Abu Dhabi Polytechnic. He earned his PhD in 2019 from Pondicherry University in Computer Science and Engineering. Before joining ADPOLY, he was an assistant professor in the Department of Computer Science, Islamic University of Science and Technology, India. He is an editorial board member on many scientific and technical journals.
<p><b>Green technologies and cleaner energy are two of the most important topics facing our world today, and the march toward efficient energy systems, smart cities, and other green technologies, has been, and continues to be, a long and intricate one. Books like this one keep the veteran engineer and student, alike, up to date on current trends in the technology and offer a reference for the industry for its practical applications.</b> <p>Energy optimization and consumption prediction are necessary to prevent energy waste, schedule energy usage, and reduce the cost. Today, smart computing technologies are slowly replacing the traditional computational methods in energy optimization, consumption, scheduling, and usage. Smart computing is an important core technology in today’s scientific and engineering environment. Smart computation techniques such as artificial intelligence, machine learning, deep learning and Internet of Things (IoT) are the key role players in emerging technologies across different applications, industries, and other areas. These newer, smart computation techniques are incorporated with traditional computation and scheduling methods to reduce power usage in areas such as distributed environment, healthcare, smart cities, agriculture and various functional areas. <p>The scope of this book is to bridge the gap between traditional power consumption methods and modern consumptions methods using smart computation methods. This book addresses the various limitations, issues and challenges of traditional energy consumption methods and provides solutions for various issues using modern smart computation technologies. These smart technologies play a significant role in power consumption, and they are cheaper compared to traditional technologies. The significant limitations of energy usage and optimizations are rectified using smart computations techniques, and the computation techniques are applied across a wide variety of industries and engineering areas. Valuable as reference for engineers, scientists, students, and other professionals across many areas, this is a must-have for any library.

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