10526nam 22005413 450 991104241050332120251031080323.01-394-29030-61-394-29028-4(CKB)41826618100041(MiAaPQ)EBC32379056(Au-PeEL)EBL32379056(OCoLC)1547906433(EXLCZ)994182661810004120251031d2025 uy 0engur|||||||||||txtrdacontentcrdamediacrrdacarrierData-Driven Energy Management and Tariff Optimization in Power Systems Shaping the Future of Electricity Distribution Through Analytics1st ed.Newark :John Wiley & Sons, Incorporated,2025.©2026.1 online resource (320 pages)1-394-29027-6 Cover -- Title Page -- Copyright -- Contents -- About the Editors -- List of Contributors -- Preface -- Chapter 1 Fundamentals of Power System Data and Analytics -- 1.1 Introduction -- 1.2 Background -- 1.2.1 Concept, Opportunities, and Challenges of Present and Future Power Systems -- 1.2.2 Transformation in the Power Industry -- 1.2.3 Drivers and Barriers -- 1.3 Data‐rich Power Systems -- 1.3.1 Data Sources and Types -- 1.3.2 Data Structure -- 1.4 Data Analytics in Power Systems -- 1.4.1 What Is Data Analytics? -- 1.4.2 Analytics Techniques -- 1.5 Data Analytics‐Based Decision‐Making in Future Power Systems -- 1.5.1 Decision Framework -- 1.5.1.1 Uncertainty Issues -- 1.5.1.2 Behavioral Analytics -- 1.5.1.3 Policy Mechanisms -- 1.5.2 Computational Aspects -- 1.6 Conclusion -- 1.7 Future Trends and Challenges -- References -- Chapter 2 Advanced Predictive Modeling for Energy Consumption and Demand -- 2.1 The Role of Load Forecasting in Power System Planning -- 2.2 Need for Short‐Term Demand Forecasting -- 2.3 Components of Power Demand and Factors Affecting Demand Growth -- 2.3.1 Electricity Demand from the Consumer Type Perspective -- 2.3.2 Electricity Demand from the Supply Perspective -- 2.4 Electricity Demand in Networks with High Renewable Energy Sources -- 2.5 Machine Learning and Its Applications in Demand Forecast -- 2.5.1 Application of Clustering in Load Forecasting -- 2.6 The Impact of Macro‐decisions on Long‐term Load Forecasting -- 2.6.1 Natural Gas as a Primary Energy Carrier for Heating Demand -- 2.7 Conclusion -- References -- Chapter 3 Demand Response and Customer‐Centric Energy Management -- 3.1 Introduction -- 3.2 Background -- 3.3 Future Power Systems Aspects, Trends, and Challenges -- 3.4 Transforming to Customer‐Centric Era -- 3.4.1 Differences Between Customer‐Centric DR Solution and Other Ways in the Future Power System.3.4.2 Drivers and Enablers -- 3.5 Customer‐Centric Power System Structure -- 3.5.1 Physical Layer -- 3.5.1.1 Physical Resources -- 3.5.1.2 Physical Constraints of the System -- 3.5.2 Cyber‐Social Layers -- 3.5.2.1 Centralized Approach (Traditional) -- 3.5.2.2 Decentralized Approach (Future) -- 3.6 Conclusion and Future Trends -- References -- Chapter 4 Applications of Data Mining in Industrial Tariff Design and Energy Management: Concepts and Practical Insights -- 4.1 Introduction -- 4.1.1 Data Mining: Concepts, Procedures, and Tools -- 4.1.2 Energy Management and the Role of Data Mining -- 4.1.3 Aims and Scope -- 4.2 Investigating Industrial Load Data: Analysis Through Various Indexes -- 4.3 Classification of Industries -- 4.4 Discussion and Conclusions -- References -- Chapter 5 Data‐Driven Tariff Design for Equitable Energy Distribution -- 5.1 Introduction -- 5.1.1 Literature Review and Contributions -- 5.1.2 Chapter Organization -- 5.2 Proposed Approach and Formulations -- 5.3 Describing the Case Study -- 5.4 Simulation Results -- 5.5 Conclusions and Future Works -- References -- Chapter 6 Applying Artificial Intelligence to Improve the Penetration of Renewable Energy in Power Systems -- 6.1 Introduction -- 6.2 Machine Learning Techniques -- 6.2.1 Artificial Neural Network and Deep Neural Network -- 6.2.2 Convolutional Neural Network -- 6.2.3 Recurrent Neural Network -- 6.2.4 Long Short‐Term Memory -- 6.3 General View of ML/DL Methods for RES Integration -- 6.3.1 Data Preprocessing -- 6.3.1.1 Normalization -- 6.3.1.2 Wrong/Missing Values and Outliers -- 6.3.1.3 Data Resolution -- 6.3.1.4 Inactive Time Data -- 6.3.1.5 Data Augmentation -- 6.3.1.6 Correlation -- 6.3.1.7 Data Clustering -- 6.3.2 Deterministic/Probabilistic Forecasting Methods -- 6.3.2.1 Deterministic Methods -- 6.3.2.2 Probabilistic Forecasting Methods -- 6.3.3 Evaluation Measures.6.4 ML/DL Application for Integration of RES -- 6.4.1 Renewable Resources Data Prediction/Planning -- 6.4.2 RES Power Generation Prediction/Operation -- 6.4.3 Electric Load and Demand Forecasting -- 6.4.4 Stability Analysis -- 6.4.4.1 Security Assessment -- 6.4.4.2 Stability Assessment -- 6.5 Integrated Machine Learning and Optimization Approach -- 6.6 Conclusion -- References -- Chapter 7 Machine Learning‐Based Solutions for Renewable Energy Integration -- 7.1 Introduction -- 7.2 Machine Learning Importance in RESs Sector -- 7.2.1 AI‐Based Algorithms in RESs -- 7.2.2 ML Algorithms Application in RESs -- 7.3 Role of ML in Optimizing Renewable Energy Generation -- 7.3.1 Different Programming Models in RES Optimization -- 7.3.2 Optimization Objectives in RESs -- 7.3.3 ML Applications in Optimizing Renewable Energy Generation -- 7.4 Ensuring Grid Stability Through ML‐Based Forecasting -- 7.4.1 Grid Stability Forecasting -- 7.4.2 Grid Stability Through ML‐Based Forecasting -- 7.5 Challenges and Future Direction in ML‐Based Approaches to RESs -- 7.5.1 Challenges in ML‐Based Approaches to RESs -- 7.5.2 Future Directions in ML‐Based Approaches to RESs -- 7.6 Conclusion -- References -- Chapter 8 Application of Artificial Neural Networks in Solar Photovoltaic Power Forecasting -- 8.1 RES Share in World Energy Transition -- 8.2 Applications of PV Panels in Energy Systems -- 8.3 Disadvantages of PV Panels -- 8.4 Importance of PV Power Forecasting -- 8.5 Proposed Algorithm for PV Power Prediction -- 8.6 Numerical Results and Discussions -- 8.7 Concluding Remarks -- References -- Chapter 9 Power System Resilience Evaluation Data Challenges and Solutions -- 9.1 Introduction -- 9.2 A Review of Power System Resilience Metrics -- 9.3 The General Framework for the Resilience Assessment of the Power System -- 9.4 Data Required for Power System Resilience Studies.9.4.1 Data of Natural Origin -- 9.4.2 Basic Data of the Power System -- 9.4.3 Data on Failure and Restoration Rates -- 9.5 Data Analysis and Correction -- 9.6 Disaster Forecasting in Power System Resilience Studies -- 9.7 Modeling the Impact of Disaster on Power System Performance -- 9.8 Static Model in Machine Learning -- 9.9 Spatiotemporal Random Process -- 9.9.1 Dynamic Model for Chain Failures -- 9.9.2 Nonstationary Failure‐Recovery‐Impact Processes -- 9.10 Lessons Learned and Concluding Remarks -- 9.11 Future Work -- References -- Chapter 10 Nonintrusive Load Monitoring in Smart Grids Using Deep Learning Approach -- 10.1 Introduction -- 10.2 Deep Learning Neural Networks -- 10.2.1 RNN -- 10.2.2 LSTM -- 10.2.3 CNN -- 10.2.4 Convolutional Layer -- 10.2.5 Pooling Layer -- 10.2.6 Fully Connected Layer -- 10.3 The Proposed Method -- 10.3.1 Pre‐Processing and Preparing Data -- 10.3.2 Proposed Method Architecture -- 10.3.3 Proposed Method's Parameters -- 10.3.4 Performance Evaluation -- 10.4 Results and Discussion -- 10.5 Challenges and Future Trends -- 10.6 Conclusion -- References -- Chapter 11 Power System Cyber‐Physical Security and Resiliency Based on Data‐Driven Methods -- 11.1 Introduction -- 11.2 Fundamental Concepts -- 11.2.1 Cyber‐Physical Power System (CPPS) -- 11.2.2 Security and Resiliency -- 11.3 Role of Data Analytics -- 11.3.1 Basic Methods -- 11.3.1.1 Supervised Learning (SL) -- 11.3.1.2 Unsupervised Learning (UL) -- 11.3.2 Advanced Techniques -- 11.3.2.1 Dimensionality Reduction (DR) -- 11.3.2.2 Feature Engineering -- 11.3.2.3 Reinforcement Learning -- 11.3.2.4 Integrated Models -- 11.4 Interdependency Modeling -- 11.4.1 Direct Modeling -- 11.4.2 Testbeds -- 11.4.3 Game‐Theoretic -- 11.4.4 Machine Learning -- 11.5 Cyber‐Physical Threats -- 11.5.1 Physical Attacks -- 11.5.2 Cyberattacks -- 11.5.2.1 Confidentiality.11.5.2.2 Availability -- 11.5.2.3 Integrity -- 11.5.3 Coordinated Attacks -- 11.6 Defense Framework -- 11.6.1 Preventive Measures -- 11.6.1.1 Supply Chain Security -- 11.6.1.2 Access Control -- 11.6.1.3 Personnel Training -- 11.6.1.4 Resource Allocation -- 11.6.1.5 Infrastructure Hardening -- 11.6.1.6 Moving Target Defense -- 11.6.2 Mitigation Actions -- 11.6.2.1 Attack Detection -- 11.6.2.2 Data Recovery -- 11.6.2.3 Reconfiguration and Restoration -- 11.6.2.4 Forensic Analysis -- 11.7 Conclusion -- References -- Chapter 12 Application of Artificial Intelligence in Undervoltage Load Shedding in Digitalized Power Systems -- 12.1 Introduction -- 12.2 Load‐Shedding Strategies -- 12.2.1 Conventional LS -- 12.2.2 Adaptive LS -- 12.2.3 AI‐Based LS -- 12.3 Principles of UVLS -- 12.3.1 Amount of Load Shed -- 12.3.2 Location for LS -- 12.3.3 Application of VSI for UVLS -- 12.4 AI‐Based Methods -- 12.5 Case Study -- 12.5.1 Database Generation -- 12.5.2 Offline Training -- 12.5.3 Online Application -- 12.6 Future Challenges and Transfer Learning -- 12.7 Conclusion -- References -- Index -- EULA.Presents a comprehensive guide to transforming power systems through data Data-Driven Energy Management and Tariff Optimization in Power Systems offers an authoritative examination of how data science is reshaping the energy landscape.Electric power systemsManagementGenerated by AIEnergy consumptionForecastingGenerated by AIElectric power systemsManagementEnergy consumptionForecasting333.7932Arasteh Hamidreza1857775Siano Pierluigi1289215Moslemi Niki1857776Guerrero Josep M1275734MiAaPQMiAaPQMiAaPQBOOK9911042410503321Data-Driven Energy Management and Tariff Optimization in Power Systems4458781UNINA