针对电力用户在数据隐私保护下的负荷预测问题,提出了基于改进联邦学习算法的电力负荷预测方法.首先,构建了基于横向联邦学习的多用户电力负荷预测框架,在此基础上,针对传统联邦学习算法预测精度不高、易受恶意攻击的问题,提出基于余弦相似度的优化局部模型更新与全局模型加权聚合方式的FedSTA(federated similarity training and aggregation)算法.采用实际负荷数据的算例结果表明,所提框架训练出的全局模型具有可观的预测精度与一定的泛化能力.除此之外,与FedAvg算法、FedAdp算法相比,FedSTA算法训练出的全局模型精度有明显提升.最后,验证了 FedSTA算法的鲁棒性和对受攻击客户端的识别能力,结果表明,该算法能准确识别受到攻击的客户端并赋予其较小的聚合权重,相较于FedAvg算法,全局模型预测精度受到的影响显著降低.
Power Load Forecasting Method Based on Improved Federated Learning Algorithm
Aiming at the problem of load prediction for power users under the protection of data privacy,we propose a power load prediction method based on improved federated learning algorithm.Firstly,a multi-user power load prediction framework based on lateral federated learning is constructed.On this basis,the traditional federated learning algorithm is not accurate enough and is vulnerable to malicious attacks,thus an innovative FedSTA(federated similarity training and aggregation)algorithm based on cosine similarity to optimize the local model update process and global model weighted aggregation is innovatively proposed.The calculation example results using actual load data show that the global model trained by the framework proposed in this paper has considerable prediction accuracy and certain generalization ability.In addition,compared with the FedAvg algorithm and the FedAdp algorithm,the FedSTA algorithm proposed in this paper significantly improves the accuracy of the global model trained.Finally,this paper verifies the robustness of the FedSTA algorithm and its ability to identify attacked clients.The results show that the algorithm can accurately identify the at-tacked clients and assign them a smaller aggregation weight.Compared with the FedAvg algorithm,the impact of the global model prediction accuracy is significantly reduced.
万方数据
维普
