Research on energy consumption prediction based on fast attribute reduction of weighted neighborhood rough set with moving horizon-Reference-Cited by-同舟云学术

Research on energy consumption prediction based on fast attribute reduction of weighted neighborhood rough set with moving horizon

Published:2023-01-05 Issue: Volume: Page:
ISSN:
Container-title:2023 The 7th International Conference on Machine Learning and Soft Computing (ICMLSC)
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Author:

Tan Jun¹^ORCID,Hou Qun²^ORCID,Liu Xin³^ORCID,Xiong Yunke²^ORCID

Affiliation:

1. Jianghan University, China and CSSC(Wuhan) LINGJIU HI-TECH CO.,LTD., China

2. Jianghan University, China

3. CSSC(Wuhan) LINGJIU HI-TECH CO.,LTD., China

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3583788.3583791

Reference29 articles.

1. Exploring the impacts of energy and environmental constraints on China’s urbanization process

2. Josep Roman Cardell. 2020. Python-based Deep-Learning methods for energy consumption forecasting. (2020). https://hdl.handle.net/2117/178030 Josep Roman Cardell. 2020. Python-based Deep-Learning methods for energy consumption forecasting. (2020). https://hdl.handle.net/2117/178030

3. Rough sets

4. ROUGH FUZZY SETS AND FUZZY ROUGH SETS*

5. Didier Dubois and Henri Prade . 1992. Putting Rough Sets and Fuzzy Sets Together . In Intelligent Decision Support: Handbook of Applications and Advances of the Rough Sets Theory, Roman Słowiński (Ed.) . Springer Netherlands , Dordrecht , 203–232. https://doi.org/10.1007/978-94-015-7975-9_14 10.1007/978-94-015-7975-9_14 Didier Dubois and Henri Prade. 1992. Putting Rough Sets and Fuzzy Sets Together. In Intelligent Decision Support: Handbook of Applications and Advances of the Rough Sets Theory, Roman Słowiński (Ed.). Springer Netherlands, Dordrecht, 203–232. https://doi.org/10.1007/978-94-015-7975-9_14