Technical Method and Model of Boiler Peakregulation Based on Artificial Intelligence Algorithm

In the context of deep peak shaving, this article explores a machine prediction model based on artificial intelligence algorithms. Real time and historical data from power plants are processed to select relevant feature values, and LSTM and random forest models are used to construct the model. By continuously adjusting the data model, the final model parameters are determined to obtain a more accurate prediction model that predicts the flue gas temperature at the tail of the boiler. This will help solve the problem of high flue gas temperature at the tail of the boiler during peak shaving in power plants. At the same time, it also provides a new solution for the problems arising from peak shaving of power plant boilers.