Short-term Kinetic Energy Forecast using a Structural Time Series Model: Study Case of Nordic Power System

Author:

Gonzalez-Longatt F.,Acosta M. N.,Chamorro H. R.,Topic Danijel

Publisher

IEEE

Cited by 18 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Framework for Training and Deployment Machine Learning Methods in Real-Time Simulator: Short-Term Kinetic Energy Forecasting in Power Systems;2024 IEEE 22nd Mediterranean Electrotechnical Conference (MELECON);2024-06-25

2. Inertia Forecasting using Hybrid Machine Learning;2024 1st International Conference on Innovative Sustainable Technologies for Energy, Mechatronics, and Smart Systems (ISTEMS);2024-04-26

3. A machine learning-based methodology for short-term kinetic energy forecasting with real-time application: Nordic Power System case;International Journal of Electrical Power & Energy Systems;2024-02

4. Inertia monitoring in power systems: Critical features, challenges, and framework;Renewable and Sustainable Energy Reviews;2024-02

5. Application of data‐driven methods in power systems analysis and control;IET Energy Systems Integration;2023-10-26

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