Federated Learning-Based Ultra-Short term load forecasting in power Internet of things
Jianbin Li, Yuqi Ren, Suwan Fang, Kunchang Li, Mingyu Sun
Abstract
The stable and efficient management and dispatching of power system depend on the accurate short term load forecasting of the following few minutes to a week. With the rapid development of the power Internet of Things, the number of network edge devices and data volume has increased exponentially. However, the traditional centralized method cannot accurately grasp load variation patterns of all area, which entails storage pressure and delays of data calculation and transmission. In addition, the centralized method has potential data security risk for its transmitting and storing all data in the data center. The present research proposes an ultra-short term load forecasting method for the power Internet of Things based on federated learning, which learns the model parameters from the data distributed in multiple edge nodes. Simulation results show that the method effectively generates accurate load forecasting and reduces the data security risk under the condition that the data of each edge node does not come out of its location.