Development of a Model for Predicting the Load on a Steam Boiler of a Thermal Power Plant

Modern combined heat and power (CHP) plants are one of the main sources of energy and thermal resources for the private and industrial sectors. They are also a source of a large amount of data, which is sufficient for the application of machine learning methods. For accurate optimization of power equipment, it is necessary to predict the generated power to ensure sufficient electrical and thermal energy. This study develops an LSTM-based forecasting model. The model is trained on a time series collected from a combined heat and power (CHP) plant over 15 months of operation. Compared with a gradient-boosting model, the LSTM achieves higher accuracy across regression metrics. The developed model can serve as a component of decision-support systems and for subsequent optimization of equipment operation at power stations, thereby improving overall plant efficiency.