Research on Load Forecasting of Novel Power System Based on Efficient Federated Transfer Learning

Jian Wang, Baoquan Wei, Jianjun Zeng and Fangming Deng ()
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Jian Wang: School of Electrical and Automation Engineering, East China Jiaotong University, Nanchang 330013, China
Baoquan Wei: School of Electrical and Automation Engineering, East China Jiaotong University, Nanchang 330013, China
Jianjun Zeng: School of Electrical and Automation Engineering, East China Jiaotong University, Nanchang 330013, China
Fangming Deng: School of Electrical and Automation Engineering, East China Jiaotong University, Nanchang 330013, China

Energies, 2023, vol. 16, issue 16, 1-14

Abstract: The load forecasting research for an NPS faces challenges including a high model accuracy, non-sharing of data, and a high communication cost. This paper proposes a load forecasting method for an NPS, based on efficient federated transfer learning (FTL). The adversarial feature extractor is added on the basis that FTL can effectively transfer the parameter features of the non-mask load to the local load data, and make up for the loss of mask load prediction accuracy. In order to improve the efficiency of the gradient compression of federated learning (FL), a depth dynamic threshold compression sensing method is proposed, which replaces the sparse signal in compressed sensing via the U-Net model and achieves an observation dimension reduction through a convolutional neural network (CNN). The experimental results show that the mean absolute percentage error (MAPE) and the root-mean-square error (RMSE) of the load forecasting method proposed in this paper are reduced by 9.6% and 2.31 kW, on average, when the load data are covered up to different degrees. Compared with the traditional FL model, the proposed compression algorithm saves 23.5% of the communication cost, without changing the accuracy of the model. The proposed prediction framework is easily interpretable, and robust under different validation metrics.

Keywords: novel power system; federated learning; gradient compression; load forecasting (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2023
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