针对配电网电力负荷预测场景下的数据隐私保护,提出一个基于联邦学习的用户级负荷预测方案,应用于配电网负荷预测场景中的数据隐私保护。在方案中多个家庭智能电表共同参与模型训练过程,同时每个家庭智能电表会训练自己的个性化模型,用于本地个性化预测。此外,方案将自适应梯度裁剪加噪算法结合到个性化联邦学习中,增强了方案的隐私保护能力,同时也可以尽量减少对预测精度的影响。通过实验分析,说明了方案可以保护电力负荷数据隐私的同时,又能够提供精准的配电网电力负荷预测,从而有助于配电网的稳定运行和优化管理。

Addressing data privacy protection in the context of power load forecasting for distribution networks, a user-level load forecasting scheme based on federated learning is proposed, specifically tailored for data privacy safeguarding in user-level load forecasting scenarios. In this approach, multiple smart meters from different households collaboratively participate in the model training process, with each meter training its own personalized model for local, tailored predictions. Furthermore, the scheme integrates an adaptive gradient clipping and noise addition algorithm into personalized federated learning, bolstering the privacy protection capabilities of the solution while minimizing the impact on forecasting accuracy. Experimental analysis demonstrates that this scheme not only safeguards the privacy of power load data but also provides precise power load forecasting for distribution networks, thereby contributing to the stable operation and optimal management of the distribution grid.

TP311