Research on load prediction of low-calorific fuel fired gas turbine based on data and knowledge hybrid model

•An improved hybrid-dimension physical-based model is proposed for load prediction.•Different data-driven models with different input variables are constructed.•A novel hybrid prediction model is proposed and validated.•The hybrid prediction model has the highest accuracy.•The average relative prediction error of the hybrid model over the complete operation range is 0.68%. The high-precision load prediction technology plays a vital role in load control and health management for gas turbines. Low-calorific fuel fired gas turbines pose an especially significant challenge for load prediction due to the frequent and wide fluctuations of gas parameters. First, an improved hybrid-dimension physical-based model is proposed, which is corrected based on segmented operation data and integrates zero-dimensional thermodynamic knowledge of gas turbines with three-dimensional fluid dynamics simulation knowledge. Secondly, an optimal data-driven model is proposed through conducting a systematic comparative study on four popular machine learning models with two types of input variables feature extraction. Then, a novel hybrid model is proposed based on the improved physical-based model, optimal data-driven model, and the principle of minimizing hybrid model errors. The average relative prediction error of the hybrid model over the complete operation range is 0.68%. Compared with the physical-based model and the data-driven model, the proposed hybrid model can improve the prediction accuracy by 63% and 29%, respectively. As the high-calorific fuel fired gas turbine system is relatively simple and has a small range of fuel fluctuations, the method established in this paper can be extended to high-calorific fuel fired gas turbines, which is of great significance for promoting the dynamic balance of loads in a new type of electric power system based on new energy.