Multi-Gradient-Descent Federated Learning With Parity for Cooperative Short-Term Load Forecasting

Short-term load forecasting (STLF) is essential in power system research. In scenarios where multiple distribution system operators are involved in cooperative forecasting, it is important to uphold the fairness of multi-party cooperation and drive the parity of model gains among parties under the p...

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Bibliographic Details
Published in:	IEEE transactions on power systems Vol. 40; no. 2; pp. 1199 - 1213
Main Authors:	Wang, Haijin, Xing, Shuangshuang, Si, Caomingzhe, Pan, Zibin, Zhao, Junhua, Qiu, Jing, Dong, Zhaoyang
Format:	Journal Article
Language:	English
Published:	New York IEEE 01.03.2025 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:	Accuracy Algorithms Cooperative STLF Data models Federated learning Forecasting Kuhn-Tucker method Load forecasting Load modeling Long short term memory multi-gradient-descent Multiple objective analysis Parity performance parity Privacy privacy-preserving
ISSN:	0885-8950, 1558-0679
Online Access:	Get full text
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