A Load Forecasting Framework Considering Hybrid Ensemble Deep Learning With Two-Stage Load Decomposition-Reference-Cited by-同舟云学术

A Load Forecasting Framework Considering Hybrid Ensemble Deep Learning With Two-Stage Load Decomposition

Published:2024-05 Issue:3 Volume:60 Page:4568-4582
ISSN:0093-9994
Container-title:IEEE Transactions on Industry Applications
language:
Short-container-title:IEEE Trans. on Ind. Applicat.

Author:

Zhou Sisi¹^ORCID,Li Yong¹^ORCID,Guo Yixiu¹^ORCID,Yang Xusheng²^ORCID,Shahidehpour Mohammad³^ORCID,Deng Wei⁴^ORCID,Mei Yujie¹^ORCID,Ren Lei⁵^ORCID,Liu Yi⁴^ORCID,Kang Tong⁴^ORCID,You Jinliang⁴^ORCID

Affiliation:

1. College of Electrical and Information Engineering, Hunan University, Changsha, China

2. State Grid Integrated Energy Service Group Company Ltd., Beijing, China

3. Galvin Center for Electricity Innovation, Illinois Institute of Technology, Chicago, IL, USA

4. State Grid Hunan Power Company Limited Research Institute, Changsha, China

5. Hunan Xiangdian Test and Research Institute Company Ltd., Changsha, China

Funder

International Science and Technology Cooperation Program of China

National Natural Science Foundation of China

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx7/28/10535351/10400871.pdf?arnumber=10400871

Reference22 articles.

1. Short term load forecasting and the effect of temperature at the low voltage level

2. Probabilistic Residential Load Forecasting Based on Micrometeorological Data and Customer Consumption Pattern

3. Hybrid Ensemble Deep Learning for Deterministic and Probabilistic Low-Voltage Load Forecasting

4. Transfer Learning Featured Short-Term Combining Forecasting Model for Residential Loads With Small Sample Sets

5. Short-Term Load Forecasting for Industrial Customers Based on TCN-LightGBM

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