The real-time state identification of the electricity-heat system based on Borderline-SMOTE and XGBoost

It is meaningful to study the real-time state monitoring and identification of integrated energy system and grasp its state in time for stable operation. A state identification method based on multi-class data equalisation and extreme gradient boost (XGBoost) is proposed for integrated energy systems. First, Latin hypercube sampling is used to simulate the load at different moments. Different system states are set up and combined with the simulative load at different moments to determine the system operation state at different moments. Then, the energy flow model is used to calculate the system power flow under different states, and the feature indexes are obtained to form the original data set. Aiming at the unbalanced data, the oversampling technology is used to preprocess data to achieve the balance of data sets. The pre-processed data is utilised to train the XGBoost, and the optimal hyperparameters of the model are obtained based on the K-fold cross-validation and grid search. Finally, the pre-processed data set is used to verify the proposed method. The calculation results show the accuracy of the identification model reaches 87.79%. Compared with traditional methods, the model can accurately identify the operating state of the electricity–heat energy system at any time section.