Federated Learning Forecasting Framework of Industry Power Load Under Privacy Protection of Meter Data

On the background of electricity market-oriented reform, the power consumption acquisition system of power supply companies is not open to the active distribution network operators. Meanwhile, end users prefer to keep user information locally to protect their privacy in the future, and active distribution network operators need to carry out power business such as load forecasting without meter reading right. Therefore, by selecting the weather and time factors as the correlation factors of load, a federated learning load forecasting framework for the protection of meter reading data of industrial users is proposed. On this basis, the industrial user data set is constructed; the load forecasting model is established based on the long- and short-term time-series network (LSTNet); and the sub industry load forecasting framework based on the federated learning is established by using FedML framework. The example analysis shows that the proposed method can enable users in the same industry to conduct federated training without sharing load data, and support active distribution network operators to carry out relevant business on the premise of protecting users’electricity privacy. It has better prediction performance, fewer models and shorter time consumption. © 2023 Automation of Electric Power Systems Press. All rights reserved.