Data mining prediction method for combined cycle unit coupled with multi-source energy storage
By constructing an association rule base R and a response surface model M, the problem of state transition tracking in the collaborative operation of combined cycle units and multi-source energy storage systems was solved. This decoupled the dynamic coupling relationship between the response delay of the energy storage system and the energy efficiency changes of the unit, thereby improving the accuracy of prediction and the efficiency of system operation.
Patent Information
- Application Number
- CN202610543803.4
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2026-04-23
- Publication Date
- 2026-07-17
AI Technical Summary
Existing technologies struggle to track system state transitions in real time during the coordinated operation of gas-steam combined cycle generator sets and multi-source energy storage systems. This can lead to overcharging or over-discharging of energy storage or suboptimal load distribution of the generator set. Furthermore, the lack of quantitative analysis capabilities regarding the lag correlation between the energy storage system's response timing and changes in the generator set's energy efficiency results in prediction biases.
A rule base R and a response surface model M are constructed. By performing time-series lag correlation analysis at continuous time points, and combining the time-series lag correlation coefficient δ of the energy efficiency fluctuation sequence and the boundary parameter fluctuation sequence, the dynamic coupling relationship between the response delay of the multi-source energy storage system and the energy efficiency change of the combined cycle unit is decoupled by using a dynamic adjustment correction factor β.
It significantly reduced prediction bias, improved the ability to predict the efficient and coordinated operation of the coupled system in the future, enhanced the economic efficiency and frequency regulation and peak shaving performance of the system, and reduced fuel consumption and energy storage equipment losses.
Smart Images
Figure CN122414474A_ABST