Artificial Intelligence for Renewable Energy Systems. Группа авторов

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      1  Cover

      2  Title Page

      3  Copyright

      4  Preface

      5  1 Analysis of Six-Phase Grid Connected Synchronous Generator in Wind Power Generation 1.1 Introduction 1.2 Analytical Modeling of Six-Phase Synchronous Machine 1.3 Linearization of Machine Equations for Stability Analysis 1.4 Dynamic Performance Results 1.5 Stability Analysis Results 1.6 Conclusions References Appendix Symbols Meaning

      6  2 Artificial Intelligence as a Tool for Conservation and Efficient Utilization of Renewable Resource 2.1 Introduction 2.2 AI in Water Energy 2.3 AI in Solar Energy 2.4 AI in Wind Energy 2.5 AI in Geothermal Energy 2.6 Conclusion References

      7  3 Artificial Intelligence–Based Energy-Efficient Clustering and Routing in IoT-Assisted Wireless Sensor Network 3.1 Introduction 3.2 Related Study 3.3 Clustering in WSN 3.4 Research Methodology 3.5 Conclusion References

      8  4 Artificial Intelligence for Modeling and Optimization of the Biogas Production 4.1 Introduction 4.2 Artificial Neural Network 4.3 Evolutionary Algorithms 4.4 Conclusion References

      9  5 Battery State-of-Charge Modeling for Solar PV Array Using Polynomial Regression 5.1 Introduction 5.2 Dynamic Battery Modeling 5.3 Results and Discussion 5.4 Conclusion References

      10  6 Deep Learning Algorithms for Wind Forecasting: An Overview Nomenclature 6.1 Introduction 6.2 Models for Wind Forecasting 6.3 The Deep Learning Paradigm 6.4 Deep Learning Approaches for Wind Forecasting 6.5 Research Challenges 6.6 Conclusion References

      11  7 Deep Feature Selection for Wind Forecasting-I 7.1 Introduction 7.2 Wind Forecasting System Overview 7.3 Current Forecasting and Prediction Methods 7.4 Deep Learning–Based Wind Forecasting 7.5 Case Study References

      12  8 Deep Feature Selection for Wind Forecasting-II 8.1 Introduction 8.2 Literature Review 8.3 Long Short-Term Memory Networks 8.4 Gated Recurrent Unit 8.5 Bidirectional Long Short-Term Memory Networks 8.6 Results and Discussion 8.7 Conclusion and Future Work References

      13  9 Data Falsification Detection in AMI: A Secure Perspective Analysis 9.1 СКАЧАТЬ