基于C4.5决策树算法的调度规则挖掘与应用OA

Aiming at the problems of opaque decision-making logic and difficult practical adoption caused by the"black-box"characteristic of models in optimal operation of reservoir groups,this paper proposes a data-driven paradigm of"optimization first,mining later".Taking the reservoir group in the Minjiang-Dadu River Basin as a case study,a sample set featuring indicators such as water level,inflow discharge and forecast-period discharge is firstly constructed on the basis of 57-year long-term multi-objective optimal operation results.Furthermore,the C4.5 decision tree algorithm is adopted to train monthly and extract intuitive scheduling rules in the form of"if-then"statements.The results demonstrate that the extracted rules can effectively capture the primary operation logic of the system.During the dry season,the rules can significantly improve power generation,with the increment rate of dry-season power generation of most hydropower stations ranging from 0.39%to 15.34%,and the annual power generation remains stable or slightly increased.Meanwhile,under the guarantee rates of 90%,95% and 98%,the minimum daily discharge in the dry season at the downstream Gaochang Station under rule-based operation is slightly lower than that of the original optimal results,but the difference is negligible.This study verifies that the rule mining method based on the C4.5 decision tree can transform complex numerical optimization schemes into intelligible,verifiable and executable scheduling knowledge.On the premise of ensuring comprehensive benefits,it significantly improves the interpretability and engineering reliability of scheduling decisions,providing a practically valuable technical approach for the intelligent operation of reservoir groups.