About the Editors xiii -- List of Contributors xv -- Preface xix -- 1 Fundamentals of Power System Data and Analytics 1 Pouya Ramezanzadeh, Mohsen Parsa Moghaddam, and Reza Zamani -- 1.1 Introduction 1 -- 1.2 Background 2 -- 1.2.1 Concept, Opportunities, and Challenges of Present and Future Power Systems 2 -- 1.2.2 Transformation in the Power Industry 3 -- 1.2.3 Drivers and Barriers 6 -- 1.3 Data-rich Power Systems 6 -- 1.3.1 Data Sources and Types 8 -- 1.3.2 Data Structure 10 -- 1.4 Data Analytics in Power Systems 11 -- 1.4.1 What Is Data Analytics? 12 -- 1.4.2 Analytics Techniques 12 -- 1.5 Data Analytics-Based Decision-Making in Future Power Systems 13 -- 1.5.1 Decision Framework 15 -- 1.5.1.1 Uncertainty Issues 15 -- 1.5.1.2 Behavioral Analytics 15 -- 1.5.1.3 Policy Mechanisms 15 -- 1.5.2 Computational Aspects 16 -- 1.6 Conclusion 16 -- 1.7 Future Trends and Challenges 16 -- References 17 -- 2 Advanced Predictive Modeling for Energy Consumption and Demand 21 Seyed Mohsen Hashemi and Abbas Marini -- 2.1 The Role of Load Forecasting in Power System Planning 21 -- 2.2 Need for Short-Term Demand Forecasting 22 -- 2.3 Components of Power Demand and Factors Affecting Demand Growth 22 -- 2.3.1 Electricity Demand from the Consumer Type Perspective 23 -- 2.3.2 Electricity Demand from the Supply Perspective 23 -- 2.4 Electricity Demand in Networks with High Renewable Energy Sources 24 -- 2.5 Machine Learning and Its Applications in Demand Forecast 25 -- 2.5.1 Application of Clustering in Load Forecasting 27 -- 2.6 The Impact of Macro-decisions on Long-term Load Forecasting 28 -- 2.6.1 Natural Gas as a Primary Energy Carrier for Heating Demand 29 -- 2.7 Conclusion 34 -- References 35 -- 3 Demand Response and Customer-Centric Energy Management 39 Alireza Mansoori, Mohsen Parsa Moghaddam, and Reza Zamani -- 3.1 Introduction 39 -- 3.2 Background 39 -- 3.3 Future Power Systems Aspects, Trends, and Challenges 41 -- 3.4 Transforming to Customer-Centric Era 41 -- 3.4.1 Differences Between Customer-Centric DR Solution and OtherWays in the Future -- Power System 42 -- 3.4.2 Drivers and Enablers 42 -- 3.5 Customer-Centric Power System Structure 45 -- 3.5.1 Physical Layer 45 -- 3.5.1.1 Physical Resources 45 -- 3.5.1.2 Physical Constraints of the System 46 -- 3.5.2 Cyber-Social Layers 49 -- 3.5.2.1 Centralized Approach (Traditional) 50 -- 3.5.2.2 Decentralized Approach (Future) 50 -- 3.6 Conclusion and Future Trends 54 -- References 57 -- 4 Applications of Data Mining in Industrial Tariff Design and Energy Management: Concepts and Practical Insights 61 Hamidreza Arasteh, Niki Moslemi, Majid Miri Larimi, Pierluigi Siano, Sobhan Naderian, andJosep M. Guerrero -- 4.1 Introduction 61 -- 4.1.1 Data Mining: Concepts, Procedures, and Tools 61 -- 4.1.2 Energy Management and the Role of Data Mining 65 -- 4.1.3 Aims and Scope 66 -- 4.2 Investigating Industrial Load Data: Analysis Through Various Indexes 67 -- 4.3 Classification of Industries 86 -- 4.4 Discussion and Conclusions 90 -- References 92 -- 5 Data-Driven Tariff Design for Equitable Energy Distribution 95 Salah Bahramara, Hamidreza Arasteh, Asrin Seyedzahedi, and Khabat Ghamari -- 5.1 Introduction 95 -- 5.1.1 Literature Review and Contributions 96 -- 5.1.2 Chapter Organization 97 -- 5.2 Proposed Approach and Formulations 97 -- 5.3 Describing the Case Study 98 -- 5.4 Simulation Results 100 -- 5.5 Conclusions and Future Works 100 -- References 105 -- 6 Applying Artificial Intelligence to Improve the Penetration of Renewable Energy in Power Systems 107 Abbas Marini and Seyed Mohsen Hashemi -- 6.1 Introduction 107 -- 6.2 Machine Learning Techniques 109 -- 6.2.1 Artificial Neural Network and Deep Neural Network 110 -- 6.2.2 Convolutional Neural Network 111 -- 6.2.3 Recurrent Neural Network 111 -- 6.2.4 Long Short-Term Memory 112 -- 6.3 General View of ML/DL Methods for RES Integration 112 -- 6.3.1 Data Preprocessing 114 -- 6.3.1.1 Normalization 115 -- 6.3.1.2 Wrong/Missing Values and Outliers 115 -- 6.3.1.3 Data Resolution 115 -- 6.3.1.4 Inactive Time Data 116 -- 6.3.1.5 Data Augmentation 116 -- 6.3.1.6 Correlation 116 -- 6.3.1.7 Data Clustering 116 -- 6.3.2 Deterministic/Probabilistic Forecasting Methods 116 -- 6.3.2.1 Deterministic Methods 116 -- 6.3.2.2 Probabilistic Forecasting Methods 119 -- 6.3.3 Evaluation Measures 119 -- 6.4 ML/DL Application for Integration of RES 121 -- 6.4.1 Renewable Resources Data Prediction/Planning 122 -- 6.4.2 RES Power Generation Prediction/Operation 125 -- 6.4.3 Electric Load and Demand Forecasting 126 -- 6.4.4 Stability Analysis 127 -- 6.4.4.1 Security Assessment 128 -- 6.4.4.2 Stability Assessment 129 -- 6.5 Integrated Machine Learning and Optimization Approach 129 -- 6.6 Conclusion 131 -- References 132 -- 7 Machine Learning-Based Solutions for Renewable Energy Integration: Applications, Optimization, and Grid Stability 135 Ali Paeizi, Mohammad Mehdi Amiri, Sasan Azad, and Mohammad Taghi Ameli -- 7.1 Introduction 135 -- 7.2 Machine Learning Importance in RESs Sector 137 -- 7.2.1 AI-Based Algorithms in RESs 137 -- 7.2.2 ML Algorithms Application in RESs 140 -- 7.3 Role of ML in Optimizing Renewable Energy Generation 150 -- 7.3.1 Different Programming Models in RES Optimization 150 -- 7.3.2 Optimization Objectives in RESs 150 -- 7.3.3 ML Applications in Optimizing Renewable Energy Generation 151 -- 7.4 Ensuring Grid Stability Through ML-Based Forecasting 155 -- 7.4.1 Grid Stability Forecasting 155 -- 7.4.2 Grid Stability Through ML-Based Forecasting 157 -- 7.5 Challenges and Future Direction in ML-Based Approaches to RESs 159 -- 7.5.1 Challenges in ML-Based Approaches to RESs 160 -- 7.5.2 Future Directions in ML-Based Approaches to RESs 161 -- 7.6 Conclusion 162 -- References 163 -- 8 Application of Artificial Neural Networks in Solar Photovoltaic Power Forecasting 167 Hamid Jabari, Afshin Ebrahimi, Ardalan Shafiei-Ghazani, and Farkhondeh Jabari -- 8.1 RES Share inWorld Energy Transition 167 -- 8.2 Applications of PV Panels in Energy Systems 168 -- 8.3 Disadvantages of PV Panels 169 -- 8.4 Importance of PV Power Forecasting 170 -- 8.5 Proposed Algorithm for PV Power Prediction 170 -- 8.6 Numerical Results and Discussions 172 -- 8.7 Concluding Remarks 172 -- References 175 -- 9 Power System Resilience Evaluation: Data Challenges and Solutions 179 Mohammad Reza Sheibani, Habibollah Raoufi, and Javad Nezafat Namini -- 9.1 Introduction 179 -- 9.2 A Review of Power System Resilience Metrics 180 -- 9.3 The General Framework for the Resilience Assessment of the Power System 182 -- 9.4 Data Required for Power System Resilience Studies 182 -- 9.4.1 Data of Natural Origin 184 -- 9.4.2 Basic Data of the Power System 184 -- 9.4.3 Data on Failure and Restoration Rates 186 -- 9.5 Data Analysis and Correction 187 -- 9.6 Disaster Forecasting in Power System Resilience Studies 188 -- 9.7 Modeling the Impact of Disaster on Power System Performance 189 -- 9.8 Static Model in Machine Learning 190 -- 9.9 Spatiotemporal Random Process 192 -- 9.9.1 Dynamic Model for Chain Failures 192 -- 9.9.2 Nonstationary Failure-Recovery-Impact Processes 192 -- 9.10 Lessons Learned and Concluding Remarks 193 -- 9.11 Future Work 194 -- References 194 -- 10 Nonintrusive Load Monitoring in Smart Grids Using Deep Learning Approach 197 Sobhan Naderian and Hamidreza Arasteh -- 10.1 Introduction 197 -- 10.2 Deep Learning Neural Networks 199 -- 10.2.1 RNN 199 -- 10.2.2 LSTM 199 -- 10.2.3 CNN 200 -- 10.2.4 Convolutional Layer 201 -- 10.2.5 Pooling Layer 201 -- 10.2.6 Fully Connected Layer 201 -- 10.3 The Proposed Method 201 -- 10.3.1 Pre-Processing and Preparing Data 201 -- 10.3.2 Proposed Method Architecture 202 -- 10.3.3 Proposed Method’s Parameters 202 -- 10.3.4 Performance Evaluation 203 -- 10.4 Results and Discussion 204 -- 10.5 Challenges and Future Trends 206 -- 10.6 Conclusion 206 -- References 207 -- 11 Power System Cyber-Physical Security and Resiliency Based on Data-Driven Methods 211 Hamed Delkhosh, Mahdi Ghaedi, and Maryam Azimi -- 11.1 Introduction 211 -- 11.2 Fundamental Concepts 212 -- 11.2.1 Cyber-Physical Power System (CPPS) 212 -- 11.2.2 Security and Resiliency 214 -- 11.3 Role of Data Analytics 215 -- 11.3.1 Basic Methods 215 -- 11.3.1.1 Supervised Learning (SL) 215 -- 11.3.1.2 Unsupervised Learning (UL) 216 -- 11.3.2 Advanced Techniques 216 -- 11.3.2.1 Dimensionality Reduction (DR) 217 -- 11.3.2.2 Feature Engineering 217 -- 11.3.2.3 Reinforcement Learning 217 -- 11.3.2.4 Integrated

Models 218 -- 11.4 Interdependency Modeling 218 -- 11.4.1 Direct Modeling 220 -- 11.4.2 Testbeds 220 -- 11.4.3 Game-Theoretic 221 -- 11.4.4 Machine Learning 222 -- 11.5 Cyber-Physical Threats 223 -- 11.5.1 Physical Attacks 224 -- 11.5.2 Cyberattacks 225 -- 11.5.2.1 Confidentiality 225 -- 11.5.2.2 Availability 226 -- 11.5.2.3 Integrity 226 -- 11.5.3 Coordinated Attacks 227 -- 11.6 Defense Framework 228 -- 11.6.1 Preventive Measures 228 -- 11.6.1.1 Supply Chain Security 229 -- 11.6.1.2 Access Control 229 -- 11.6.1.3 ...