Self-organizing maps for scenario reduction in long-term hydropower scheduling-Reference-Cited by-同舟云学术

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Self-organizing maps for scenario reduction in long-term hydropower scheduling

Published:2022-10-17 Issue: Volume: Page:
ISSN:
Container-title:IECON 2022 – 48th Annual Conference of the IEEE Industrial Electronics Society
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Author:

Wang Jinghao¹,Yousefi Mojtaba²,Cheng Xiaomei³,Rajasekharan Jayaprakash¹,Arghandeh Reza²,Pan Xueping⁴,Farahmand Hossein¹

Affiliation:

1. Norwegian University of Science and Technology,Department of Electric Power Engineering,Trondheim,Norway

2. Western Norway University of Applied Science,Department of Computer Science, Electrical Engineering and Mathematical Sciences,Bergen,Norway

3. Smart Innovation Norway,Halden,Norway

4. Hohai University,College of Energy and Electrical Engineering,Nanjing,China

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/9968313/9968303/09968753.pdf?arnumber=9968753

Reference24 articles.

1. A clustering approach to domestic electricity load profile characterisation using smart metering data

2. Load estimation for microgrid planning based on a self-organizing map methodology

3. Self-organized formation of topologically correct feature maps

4. Essentials of the self-organizing map

5. Long- and Medium-term Operations Planning and Stochastic Modelling in Hydro-dominated Power Systems Based on Stochastic Dual Dynamic Programming

Cited by 2 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. Short-term scheduling for hydroelectric power plants in a deregulated power market by means of a deep deterministic policy gradient algorithm;2024 4th International Conference on Applied Artificial Intelligence (ICAPAI);2024-04-16

2. Exploring the application of machine‐learning techniques in the next generation of long‐term hydropower‐thermal scheduling;IET Renewable Power Generation;2024-03-18

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