Improving load forecasting based on deep learning and K-shape clustering-Reference-Cited by-同舟云学术

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Improving load forecasting based on deep learning and K-shape clustering

Published:2017-05 Issue: Volume: Page:
ISSN:
Container-title:2017 International Joint Conference on Neural Networks (IJCNN)
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Author:

Fahiman Fateme,Erfani Sarah M.,Rajasegarar Sutharshan,Palaniswami Marimuthu,Leckie Christopher

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/7958416/7965814/07966378.pdf?arnumber=7966378

Cited by 54 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. SAC-ConvLSTM: A novel spatio-temporal deep learning-based approach for a short term power load forecasting;Expert Systems with Applications;2024-03

2. Shape-based clustering for demand response potential evaluation: A perspective of comprehensive evaluation metrics;Sustainable Energy, Grids and Networks;2023-12

3. Electricity consumption forecasting with outliers handling based on clustering and deep learning with application to the Algerian market;Expert Systems with Applications;2023-10

4. A Novel Clustering-based Forecast Framework: The Clusters with Competing Configurations Approach;Academic Platform Journal of Engineering and Smart Systems;2023-09-30

5. Using Clustering To Reduce Models Required For Medium Term Load Forecasting;2023 International Aegean Conference on Electrical Machines and Power Electronics (ACEMP) & 2023 International Conference on Optimization of Electrical and Electronic Equipment (OPTIM);2023-09-01

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