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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.
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.
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.
John A., PhD, 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.
Senthil Kumar Mohan, PhD, 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.
Sanjeevikumar Padmanaban, PhD, 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.
Yasir Hamid, PhD, 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.
List of Contributors xiii
Preface xv
Acknowledgements xix
1 Review and Analysis of Machine Learning Based Techniques for Load Forecasting in Smart Grid System 1 Shihabudheen KV and Sheik Mohammed S
1.1 Introduction 2
1.2 Forecasting Methodology 4
1.3 AI-Based Prediction Methods 5
1.3.1 Single Prediction Methods 5
1.3.1.1 Linear Regression 5
1.3.1.2 Artificial Neural Networks (ANN) 7
1.3.1.3 Support Vector Regression (SVR) 8
1.3.1.4 Extreme Learning Machine 9
1.3.1.5 Neuro-Fuzzy Techniques 10
1.3.1.6 Deep Learning Techniques 11
1.3.2 Hybrid Prediction Methods 12
1.3.2.1 Combined AI-Based Prediction Techniques 12
1.3.2.2 Signal Decomposition Based Prediction Techniques 13
1.3.2.3 EMD Based Decomposition 14
1.3.2.4 Wavelet Based Decomposition 14
1.4 Results and Discussions 15
1.4.1 Description of Dataset 15
1.4.2 Performance Analysis of Single Prediction Methods for Load Forecasting 16
1.4.2.1 Feature Selection 16
1.4.2.2 Optimal Parameter Selection 17
1.4.2.3 Prediction Results of Single Prediction Methods 17
1.4.3 Performance Analysis of Hybrid Prediction Methods for Load Forecasting 17
1.4.4 Comparative Analysis 21
1.5 Conclusion 22
References 23
2 Energy Optimized Techniques in Cloud and Fog Computing 27 N.M. Balamurugan, TKS Rathish babu, K Maithili and M. Adimoolam
2.1 Introduction 28
2.2 Fog Computing and Its Applications 33
2.3 Energy Optimization Techniques in Cloud Computing 38
2.4 Energy Optimization Techniques in Fog Computing 42
2.5 Summary and Conclusions 44
References 45
3 Energy-Efficient Cloud Computing Techniques for Next Generation: Ways of Establishing and Strategies for Future Developments 49 Praveen Mishra, M. Sivaram, M. Arvindhan, A. Daniel and Raju Ranjan
3.1 Introduction 50
3.2 A Layered Model of Cloud Computing 52
3.2.1 System of Architecture 53
3.3 Energy and Cloud Computing 54
3.3.1 Performance of Network 55
3.3.2 Reliability of Servers 55
3.3.3 Forward Challenges 55
3.3.4 Quality of Machinery 56
3.4 Saving Electricity Prices 56
3.4.1 Renewable Energy 57
3.4.2 Cloud Freedom 57
3.5 Energy-Efficient Cloud Usage 58
3.6 Energy-Aware Edge OS 58
3.7 Energy Efficient Edge Computing Based on Machine Learning 59
3.8 Energy Aware Computing Offloading 61
3.8.1 Energy Usage Calculation and Simulation 63
3.9 Comments and Directions for the Future 63
References 64
4 Energy Optimization Using Silicon Dioxide Composite and Analysis of Wire Electrical Discharge Machining Characteristics 67 M.S. Kumaravel, N. Alagumurthi and P. Mathiyalagan
4.1 Introduction 67
4.2 Materials and Methods 69
4.3 Results and Discussion 72
4.3.1 XRD Analysis 72
4.3.2 SEM Analysis 73
4.3.3 Grey Relational Analysis (GRA) 73
4.3.4 Main Effects Graph 76
4.3.5 Analysis of Variance (ANOVA) 77
4.3.6 Confirmatory Test 78
4.4 Conclusion 80
Acknowledgement 80
References 80
5 Optimal Planning of Renewable DG and Reconfiguration of Distribution Network Considering Multiple Objectives Using PSO Technique for Different Scenarios 83 Balmukund Kumar and Aashish Kumar Bohre
5.1 Introduction 84
5.2 Literature Review for Recent Development in DG Planning and Network Reconfiguration 84
5.3 System Performance Parameters and Index 87
5.4 Proposed Method 88
5.4.1 Formulation of Multi-Objective Fitness Function 88
5.4.2 Backward-Forward-Sweep Load Flow Based on BIBC-BCBV Method 89
5.5 PSO Based Optimization 90
5.6 Test Systems 92
5.7 Results and Discussions 92
5.8 Conclusions 101
References 102
6 Investigation of Energy Optimization for Spectrum Sensing in Distributed Cooperative IoT Network Using Deep Learning Techniques 107 M. Pavithra, R. Rajmohan, T. Ananth Kumar, S. Usharani and P. Manju Bala
6.1 Introduction 108
6.2 IoT Architecture 111
6.3 Cognitive Spectrum Sensing for Distributed Shared Network 113
6.4 Intelligent Distributed Sensing 115
6.5 Heuristic Search Based Solutions 117
6.6 Selecting IoT Nodes Using Framework 118
6.7 Training With Reinforcement Learning 119
6.8 Model Validation 120
6.9 Performance Evaluations 123
6.10 Conclusion and Future Work 125
References 126
7 Road Network Energy Optimization Using IoT and Deep Learning 129 N. M. Balamurugan, N. Revathi and R. Gayathri
7.1 Introduction 129
7.2 Road Network 132
7.2.1 Types of Road 132
7.2.2 Road Structure Representation 134
7.2.3 Intelligent Road Lighting System 135
7.3 Road Anomaly Detection 139
7.4 Role of IoT in Road Network Energy Optimization 141
7.5 Deep Learning of Road Network Traffic 142
7.6 Road Safety and Security 142
7.7 Conclusion 144
References 144
8 Energy Optimization in Smart Homes and Buildings 147 S. Sathya, G. Karthi, A. Suresh Kumar and S. Prakash
8.1 Introduction 148
8.2 Study of Energy Management 150
8.3 Energy Optimization in Smart Home 150
8.3.1 Power Spent in Smart-Building 153
8.3.2 Hurdles of Execution in Energy Optimization 156
8.3.3 Barriers to Assure SH Technologies 156
8.4 Scope and Study Methodology 157
8.4.1 Power Cost of SH 158
8.5 Conclusion 159
References 159
9 Machine Learning Based Approach for Energy Management in the Smart City Revolution 161 Deepica S., S. Kalavathi, Angelin Blessy J. and D. Maria Manuel Vianny
9.1 Introduction 162
9.1.1 Smart City: What is the Need? 162
9.1.2 Development of Smart City 163
9.2 Need for Energy Optimization 166
9.3 Methods for Energy Effectiveness in Smart City 166
9.3.1 Smart Electricity Grids 166
9.3.2 Smart Transportation and Smart Traffic Management 169
9.3.3 Natural Ventilation Effect 172
9.4 Role of Machine Learning in Smart City Energy Optimization 173
9.4.1 Machine Learning: An Overview 173
9.5 Machine Learning Applications in Smart City 175
9.6 Conclusion 177
References 178
10 Design of an Energy Efficient IoT System for Poultry Farm Management 181 G. Rajakumar, G. Gnana Jenifer, T. Ananth Kumar and T. S. Arun Samuel
10.1 Introduction 182
10.2 Literature Survey 183
10.3 Proposed Methodology 187
10.3.1 Monitoring and Control Module 188
10.3.2 Monitoring Temperature 188
10.3.3 Monitoring Humidity 189
10.3.4 Monitoring Air Pollutants 189
10.3.5 Artificial Lightning 190
10.3.6 Monitoring Water Level 190
10.4 Hardware Components 190
10.4.1 Arduino UNO 190
10.4.2 Temperature Sensor 190
10.4.3 Humidity Sensor 191
10.4.4 Gas Sensor 192
10.4.5 Water Level Sensor 192
10.4.6 LDR Sensor 193
10.4.7 GSM (Global System for Mobile Communication) Modem 194
10.5 Results and Discussion 195
10.5.1 Hardware Module 195
10.5.2 Monitoring Temperature 196
10.5.3 Monitoring Gas Content 198
10.5.4 Monitoring Humidity 198
10.5.5 Artificial Lighting 198
10.5.6 Monitoring Water Level 198
10.5.7 Poultry Energy-Efficiency Tips 199
10.6 Conclusion 201
References 203
11 IoT Based Energy Optimization in Smart Farming Using AI 205 N. Padmapriya, T. Ananth Kumar, R. Aswini, R. Rajmohan, P. Kanimozhi and M. Pavithra
11.1 Introduction 206
11.2 IoT in Smart Farming 208
11.2.1 Benefits of Using IoT in Agriculture 208
11.2.2 The IoT-Based Smart Farming Cycle 209
11.3 AI in Smart Farming 210
11.3.1 Artificial Intelligence Revolutionises Agriculture 210
11.4 Energy Optimization in Smart Farming 211
11.4.1 Energy Optimization in Smart Farming Using IoT and AI 212
11.5 Experimental Results 215
11.5.1 Analysis of Network Throughput 216
11.5.2 Analysis of Network Latency 217
11.5.3 Analysis of Energy Consumption 218
11.5.4 Applications of IoT and AI in Smart Farming 219
11.6 Conclusion 220
References 221
12 Smart Energy Management Techniques in Industries 5.0 225 S. Usharani, P. Manju Bala, T. Ananth Kumar, R. Rajmohan and M. Pavithra
12.1 Introduction 226
12.2 Related Work 227
12.3 General Smart Grid Architecture 229
12.3.1 Energy Sub-Sectors 230
12.3.1.1 Smart Grid: State-of-the-Art Inside Energy Sector 230
12.3.2 EV and Power-to-Gas: State-of-the-Art within Biomass and Transport 231
12.3.3 Constructing Zero Net Energy (CZNE): State-of-the-Art Inside Field of Buildings 233
12.3.4 Manufacturing Industry: State-of-the-Art 234
12.3.5 Smart Energy Systems 235
12.4 Smart Control of Power 236
12.4.1 Smart Control Thermal System 236
12.4.2 Smart Control Cross-Sector 237
12.5 Subsector Solutions 238
12.6 Smart Energy Management Challenges in Smart Factories 239
12.7 Smart Energy Management Importance 240
12.8 System Design 241
12.9 Smart Energy Management for Smart Grids 241
12.10 Experimental Results 247
12.11 Conclusions 250
References 251
13 Energy Optimization Techniques in Telemedicine Using Soft Computing 253 R. Indrakumari
13.1 Introduction 253
13.2 Essential Features of Telemedicine 255
13.3 Issues Related to Telemedicine Networks 256
13.4 Telemedicine Contracts 257
13.5 Energy Efficiency: Policy and Technology Issue 258
13.5.1 Soft Computing 258
13.5.2 Fuzzy Logic 260
13.5.3 Artificial Intelligence 260
13.5.4 Genetic Algorithms 263
13.5.5 Expert System 263
13.5.6 Expert System Based on Fuzzy Logic Rules 264
13.6 Patient Condition Monitoring 266
13.7 Analysis of Physiological Signals and Data Processing 271
13.8 M-Health Monitoring System Architecture 272
13.9 Conclusions 275
References 276
14 Healthcare: Energy Optimization Techniques Using IoT and Machine Learning 279 G. Vallathan, Senthilkumar Meyyappan and T. Rajani
14.1 Introduction 280
14.2 Energy Optimization Process 281
14.3 Energy Optimization Techniques in Healthcare 283
14.3.1 Energy Optimization in Building 283
14.3.2 Machine Learning for Energy Optimization 284
14.3.3 Reinforcement Learning for Energy Optimization 286
14.3.4 Energy Optimization of Sustainable Internet of Things (IoT) 287
14.4 Future Direction of Energy Optimizations 288
14.5 Conclusion 289
References 289
15 Case Study of Energy Optimization: Electric Vehicle Energy Consumption Minimization Using Genetic Algorithm 291 Pedram Asef
15.1 Introduction 292
15.2 Vehicle Modelling to Optimisation 295
15.2.1 Vehicle Mathematical Modelling 295
15.2.2 Vehicle Model Optimisation Process: Applied Genetic Algorithm 298
15.2.3 GA Optimisation Results and Discussion 301
15.3 Conclusion 305
References 305
About the Editors 307
Index 309
Shihabudheen KV1 and Sheik Mohammed S2*
1Electrical Engineering Department, National Institute of Technology, Calicut, Kerala, India
2Electrical and Electronic Engineering Programme Area, Faculty of Engineering, Universiti Teknologi Brunei, Gadong, Brunei Darussalam
Electrical load forecasting is an important process that can improve the efficiency and economy of the utility grid especially in the smart grid environment. Load forecasting plays a significant role in making decisions such as planning, generation scheduling, operation, pricing customer satisfaction, and system security. However, load forecasting is a tedious and difficult task due to the intermittent nature of Renewable Energy Systems (RES) that varies depending on the seasons and parameters such as change in temperature and humidity. Moreover, the connect loads are also complex in nature as they vary from season to season. Artificial Intelligent (AI) techniques are a promising approach for better load forecasting having chaotic and random variation of both load and generation. In this chapter, a load-forecasting algorithm for time series loads using AI techniques with supervised methods is presented and discussed. A comparative assessment of load forecasting based on supervised artificial intelligent algorithms, such as Artificial Neural Networks (ANN), Support Vector Machine (SVM), and Extreme Learning Machine (ELM), is performed on smart meter data. The results are presented and performance of the selected algorithms are analysed.
Keywords: Electricity load forecasting, Artificial Intelligence (AI), Artificial Neural Networks (ANN), Support Vector Machine (SVM), extreme learning machine (ELM), smart grid, smart meter
Traditionally, electric grids are a network of electric power generation, transmission, and distribution systems controlled by a centralized system. The conventional electric grid system has one way power flow and a communication approach from the generating station until the end customer. However, the power generation, transmission, and distribution systems had a noteworthy revolution in recent times. The progress and development in power generation using renewable energy sources is one of the important reasons behind this transformation. The restructured power system has made distribution systems bi-directional, controllable grids called Smart Grids. The Smart Grid consists of a number of RES with loads, ESS, sensors, and communications networks connected in a well-arranged fashion so that it has the potential to improve overall system performance. The coordinated and controlled operation of this integrated structure makes the grid smarter by managing generation, distribution, customers, and the market in both an efficient and effective manner. Figure 1.1 shows the different domains and stakeholders of the Smart Grid.
Figure 1.1 Structure of smart grid [1].
Electricity load forecasting is a projection of the load demand that electricity users are expected to have in the future. Load forecasts enable the utilities to manage supply and demand and also ensure the stability of power grids. Load forecasting is the key element for smart grid operation, as it plays a vital role in decision-making such as planning, scheduling, operation, capacity addition, pricing, generation planning, and system security. Another major advantage of load forecasting is that it helps both the utility and consumers to optimize their energy usage.
Load forecasting is classified based on horizon and scale. Scale level is the unit size at which the forecasting is performed. Scale level forecasting ranges from individual forecasting (meter-level) in homes and building levels (multi-meters level) to region, district, state, and up to country (integrated) level load forecasting. On the other hand, horizon defines the time range of load forecasting. Horizon level forecasting is classified as very short-term load forecasting (VSTLF), short-term load forecasting (STLF), medium-term load forecasting (MTLF) and long-term load forecasting (LTLF). In VSTLF, minutes to hour ahead prediction is carried out and SLTF is day ahead to weekly forecasting. MTLF deals with one week to three years prediction and more than three years prediction is known as LTLF. Each type of forecasting serves different purposes in the power system for scheduling, economic dispatch, operation planning, maintenance, capacity expansion planning, fuel economy, sales, etc. [2-4].
Traditionally, expected demand is forecast using the information about past use and other related data with the aid of charts and graphs by applying an engineering approach. Later, data driven approaches are predominantly used to build prediction algorithms to improve the efficiency and accuracy of forecasting. Statistical based techniques and intelligent computing are the two main categories of data driven approaches applied for electricity load forecasting [5, 6]. Statistical approaches use historical data to compare energy consumption with the most relevant variables as inputs. More high-quality historical data therefore plays a vital role in the efficacy of statistical models. Conventional approaches like Regression Models, Conditional Demand Analysis (CDA), Auto Regressive Moving Average (ARMA), and ARIMA are the most commonly adopted statistical methods for time series prediction. However, many researchers have investigated forecasting using AI based techniques and deep learning and those techniques have become the widely accepted technology over the past decade [7]. In addition to that, machine learning techniques like Classification and Regression Trees (CART), as well as Support Vector Machine (SVM) techniques are also used for time series prediction. Fuzzy Logic Systems, Artificial Neural Networks (ANN), Evolutionary Programming, and expert systems are some of the AI based approaches. Among them, ANN has widespread acceptance for time series forecasting [8-12]. Many attempts are made to solve the load-forecasting problems using AI based hybrid approaches. A comprehensive review on all such types of forecasting techniques are discussed in [13-16]. A review based on different categorization of the various forecasting, including the hybrid method, is less attempted in most of the literature.
In this book chapter, an extensive review of different supervised AI based load forecasting methodology is discussed. The review includes different categories of prediction such as single prediction and hybrid prediction methods. The details of hybrid prediction such as combined AI based prediction and signal decomposition based prediction techniques are included. Moreover, a comparative simulation study is performed on smart meter data. Methodology of forecasting is described in Section 1.2. A comprehensive review of various AI based prediction strategies applied for load forecasting is presented in Section 1.3. Comparative assessment of single and hybrid predictions is performed on smart meter data and the results are presented and discussed in Section 1.4. AI techniques such as Back Propagation Based Neural Network (BPNN), Support Vector Machine (SVM), and Extreme Learning Machine (ELM) are used for single prediction. For hybrid prediction, Empirical Model Decomposition (EMD) based BPNN and SVM (EMD-BPNN and EMD-SVM) prediction are performed. The chapter is concluded in Section 1.5.
The forecasting methodology for prediction of time series loads consists of two steps, as shown in Figure 1.2. The first step of methodology is feature extraction. Initially, a sufficient quantity of features is extracted form load time series data. Feature extraction procures the features which aid in the prediction of time series data. Transferring the collected features into a more informative analysis domain helps in sensing the hidden characteristics of future loads. The second step is the implementation of a predictor for accurate forecasting.
Figure 1.2 Overall steps for load time series forecasting methodology.
Let y(t) represent a load time series data. The prediction equation can be mathematically represented by
where g(t) indicates the extracted features, f represents the predictive function to be approximated by predictor and yt^( + k) is k step ahead of predicted values of y(t).
Many AI-based prediction methods are proposed in literatures for time series based load forecasting. The classification of AI based prediction techniques is shown in Figure 1.3. An overview some of commonly used prediction methods are outlined in this section.
Single prediction indicates the prediction of time series, which is formed using a single AI technique. Some of the AI techniques used for single prediction of time series data are Linear Regression, Artificial Neural Networks, Support Vector Regression, Extreme Learning Machine, and Neuro-Fuzzy Methods.
This approach is used to predict the dependent variable using several independent variables or features. It uses the assumption that a linear relation may exist between the features and output signals. A linear...
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