Efficient Heat Generation Systems Through Transformer and Recurrent Neural Forecasting Combined With Mixed-Integer Predictive Control

Accurate heat demand forecasting and control are critical for optimizing the operational efficiency of thermal energy systems, including power plants and district heating networks. This work presents a receding horizon control framework for optimizing heat production in district energy systems under real-world operational conditions. The proposed controller minimizes production costs while considering electricity market prices, which significantly affect the economic performance of heat generation units. The system coordinates multiple heterogeneous generation assets under mixed-integer programming constraints. To support the controller, we develop a forecasting framework that combines transformer and gated recurrent unit (GRU) architectures to improve prediction accuracy. This hybrid model utilizes multivariate input features, including ambient temperature and historical thermal output, to generate robust and probabilistic forecasts. The Transformer captures long-range dependencies in the data, while the GRU models temporal sequences and cyclical behavior. Experimental results on real-world data demonstrate that this method can reduce total operational costs by 16.3% and enhance resource efficiency in thermal energy production, providing a scalable solution for smart energy management.