Improving the efficiency of coastal clean power consumption using Bayesian networks

This study aims to improve the consumption efficiency of clean electricity in coastal areas through Bayesian network models. This article utilizes the probabilistic reasoning ability of Bayesian networks to analyze various factors that affect power consumption, construct a power consumption efficiency prediction model, and verify the effectiveness of the model through simulation. The research results indicate that Bayesian networks can effectively identify key influencing factors, improve the scientific nature of power consumption decisions, and thus enhance the overall consumption efficiency of clean electricity in coastal areas. In terms of the impact on power dispatch optimization, it was found that the average prediction error was -0.24 MW, with a standard deviation of 3.12 MW, and most of the errors were concentrated in the range of -5 to 5 MW. The experimental results have verified the effectiveness and reliability of Bayesian networks in optimizing power systems.