Research on real-time scheduling optimization technology of power system based on deep learning

In the context of the increasingly severe world climate form, how to rationally arrange and dispatch energy has become an urgent need. This paper proposes a deep learning-based power system scheduling model based on the concept of perfect scheduling, using GRU to learn scheduling data. A different training set is constructed to train the model according to the load characteristics at different moments, and the model is updated in real time based on the data at the current moment. The analysis of the algorithms reveals that the scheduling error rate of this model ranges from −-3% to 2%, and the average RMSE of the scheduling scheme is 2.72, placing it in close proximity to the optimal scheduling strategy. Due to a 6.5% reduction in scheduling cost compared to the average cost of the two analyzed algorithms, the average time reduction is 76.3%. The scheduling optimization model proposed in this paper exhibits excellent performance.