Power Network Scheduling Strategy Based on Federated Learning Algorithm in Edge Computing Environment

Abstract

Distributed new energy consumption scenarios, such as photovoltaic, energy storage, charging stake, etc., are facing the needs of processing massive real-time data, large-scale distributed new energy and access to diverse loads. Based on the business characteristics such as business peak-valley dynamic change, network connection and time-delay differential demand of different business in energy and power business, a reasonable and effective integrated resource scheduling model of computing resources suitable for distributed new energy consumption scenario is studied to support power planning and dynamic dispatch application. In this article, we propose an arithmetic planning strategy based on federal learning. Specifically, we first introduce a computing priority network scheduling framework in edge cloud computing environments. Secondly, we process the absorption data of corresponding energy nodes by random forest algorithm, adjust the connection relationship between a large number of internal nodes, control and dispatch the nodes, and then conduct integrated training through federal learning to dispatch the computing power of the overall network, so as to achieve fast and accurate algorithm dispatch. Then, under the same environment conditions, the simulation experiments of deep neural network and random forest algorithm are compared. A large number of simulation results show that the system can effectively assist the smart grid in reasonable algorithm dispatch and improve the resource utilization efficiency.