SCA-LFD: Side-Channel Analysis-Based Load Forecasting Disturbance in the Energy Internet

Li Ding, Jun Wu, Changlian Li, Alireza Jolfaei, Xi Zheng

Research output: Contribution to journalArticlepeer-review

3 Citations (Scopus)


The energy Internet (EI) equipment may face threats that attackers poison federated learning (FL) models to disturb electricity load forecasting. To mitigate this vulnerability, it is important to study load forecasting disturbance approaches. This article proposes a side-channel analysis (SCA)-based disturbance approach. First, we design an FL SCA scheme to extract power information from the FL chip running forecasting model. Second, we propose an FL data speculation method using an optimized convolutional neural network trained with SCA information. Third, we design a label-flipping-based poisoning scheme with speculated data characteristics for load forecasting disturbance. Experimental results show attackers can successfully poison and disturb FL-based load forecasting. The average accuracy of EI load data speculation is 99.8%. This work is the first to study EI load forecasting disturbance from an SCA perspective.

Original languageEnglish
Pages (from-to)3199-3208
Number of pages10
Issue number3
Early online date5 May 2022
Publication statusPublished - Mar 2023
Externally publishedYes


  • Energy Internet (EI)
  • federated learning (FL)
  • side-channel analysis (SCA)


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