A Multitask Integrated Deep-Learning Probabilistic Prediction for Load Forecasting-Reference-Cited by-同舟云学术

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A Multitask Integrated Deep-Learning Probabilistic Prediction for Load Forecasting

Published:2024-01 Issue:1 Volume:39 Page:1240-1250
ISSN:0885-8950
Container-title:IEEE Transactions on Power Systems
language:
Short-container-title:IEEE Trans. Power Syst.

Author:

Wang Jianzhou¹,Wang Kang¹^ORCID,Li Zhiwu²^ORCID,Lu Haiyan³^ORCID,Jiang He⁴^ORCID,Xing Qianyi¹

Affiliation:

1. School of Statistics, Dongbei University of Finance and Economics, Dalian, China

2. Institute of Systems Engineering, Macau University of Science and Technology, Macau, China

3. School of Computer Science, Faculty of Engineering and Information Technology, University of Technology Sydney, Sydney, NSW, Australia

4. School of Statistics, Jiangxi University of Finance and Economics, NanChang, China

Funder

Natural Science Foundation of Jiangxi Province

National Natural Science Foundation of China

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Energy Engineering and Power Technology

Link

http://xplorestaging.ieee.org/ielx7/59/10375285/10070851.pdf?arnumber=10070851

Reference37 articles.

1. Analysis of net-load forecast error and new methodology to determine Non-Spin Reserve Service requirement

2. A new spinning reserve requirement forecast method for deregulated electricity markets

3. A probabilistic risk-based approach for spinning reserve provision using day-ahead demand response program

4. Research on Operation Analysis Method of East China Power Grid Peak-Regulation Ancillary Service Market

5. Optimal scheduling of spinning reserve with ramp constraints

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