Scheduling Model of New Energy Storage System Based on Machine Learning

Under the current low-carbon and environmental protection issues, new energy storage systems, as systems for storing various new energies, its development planning and energy dispatch are both important issues, so this article believes that the dispatch model of the new energy storage system can be constructed through machine learning methods. Finally, this article verifies the practicability of this method through comparative experiments between a machine learning-based scheduling model and a conventional scheduling model. In terms of average energy utilization performance, the scheduling model based on machine learning is 93.87%, and the conventional scheduling model is 88.55%. The obvious gap reflects the auxiliary role of machine learning in the scheduling model. This article also compares and discusses this method with other mainstream methods, and the result is that this method is sufficiently competitive. In the end, this article finds that from the perspective of energy utilization and scheduling accuracy, machine learning can improve the performance of the scheduling model of new energy storage systems.