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Load forecasting is very essential in the analysis and grid planning of powersystems. For this reason, we first propose a household load forecasting methodbased on federated deep learning and non-intrusive load monitoring (NILM). Forall we know, this is the first research on federated learning (FL) in householdload forecasting based on NILM. In this method, the integrated power isdecomposed into individual device power by non-intrusive load monitoring, andthe power of individual appliances is predicted separately using a federateddeep learning model. Finally, the predicted power values of individualappliances are aggregated to form the total power prediction. Specifically, byseparately predicting the electrical equipment to obtain the predicted power,it avoids the error caused by the strong time dependence in the power signal ofa single device. And in the federated deep learning prediction model, thehousehold owners with the power data share the parameters of the local modelinstead of the local power data, guaranteeing the privacy of the household userdata. The case results demonstrate that the proposed approach provides a betterprediction effect than the traditional methodology that directly predicts theaggregated signal as a whole. In addition, experiments in various federatedlearning environments are designed and implemented to validate the validity ofthis methodology.

Privacy-preserving household load forecasting based on non-intrusive load monitoring: A federated deep learning approach