Probabilistic Multienergy Load Forecasting Based on Hybrid Attention-Enabled Transformer Network and Gaussian Process-Aided Residual Learning-Reference-Cited by-同舟云学术

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Probabilistic Multienergy Load Forecasting Based on Hybrid Attention-Enabled Transformer Network and Gaussian Process-Aided Residual Learning

Published:2024-06 Issue:6 Volume:20 Page:8379-8393
ISSN:1551-3203
Container-title:IEEE Transactions on Industrial Informatics
language:
Short-container-title:IEEE Trans. Ind. Inf.

Author:

Zhao Pengfei¹^ORCID,Hu Weihao¹^ORCID,Cao Di¹^ORCID,Zhang Zhenyuan¹^ORCID,Huang Yuehui²^ORCID,Dai Longcheng³^ORCID,Chen Zhe⁴^ORCID

Affiliation:

1. School of Mechanical and Electrical Engineering, University of Electronic Science and Technology of China, Chengdu, China

2. State Key Laboratory of Operation and Control of Renewable Energy and Storage System, China Electric Power Research Institute, Beijing, China

3. Electric Power Research Institute, State Grid Ningxia Electric Power Comapny Ltd., Yinchuan, China

4. Department of Energy Technology, Aalborg University, Aalborg, Denmark

Funder

National Natural Science Foundation of China

China Postdoctoral Science Foundation

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx7/9424/10550081/10473214.pdf?arnumber=10473214

Reference39 articles.

1. Bi-level Heat-Electricity Energy Sharing for Integrated Energy Systems with Energy Hubs and Prosumers

2. Distributed Optimal Energy Management for Integrated Energy Systems

3. BiLSTM Multitask Learning-Based Combined Load Forecasting Considering the Loads Coupling Relationship for Multienergy System

4. Analysis of the energy and environmental effects of green car deployment by an integrating energy system model with a forecasting model

5. Short-term multi-energy load forecasting for integrated energy systems based on CNN-BiGRU optimized by attention mechanism

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1. Enhancing multivariate, multi-step residential load forecasting with spatiotemporal graph attention-enabled transformer;International Journal of Electrical Power & Energy Systems;2024-09

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