A federated LSTM network for load forecasting using multi-source data with homomorphic encryption

A federated LSTM network for load forecasting using multi-source data with homomorphic encryption

Short-term load forecasting is of great significance to the operation of power systems. Various uncertain factors, such as meteorological social data, have already been combined with historical power data to create more accurate load forecasting models. In traditional systems, data from various indu...

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Bibliographic Details
Main Authors: Mengdi Wang, Rui Xin, Mingrui Xia, Zhifeng Zuo, Yinyin Ge, Pengfei Zhang, Hongxing Ye
Format: Article
Language:English
Published: AIMS Press 2025-03-01
Series:AIMS Energy
Subjects:
short-term load forecasting
federated learning
long short-term memory
homomorphic encryption
smart grid
Online Access:https://www.aimspress.com/article/doi/10.3934/energy.2025011
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https://www.aimspress.com/article/doi/10.3934/energy.2025011

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