Ultra-short-term Power Load Forecasting Based on Two-layer XGBoost Algorithm Considering the Influence of Multiple Features

Historical data is indispensable in power load forecasting, however, many problems exist in the selected historical data, such as a large amount of data but little characteristic dimension, many invalid data, and unclear characteristic relationship among data, which significantly affect the accuracy of power load forecasting. In order to improve the accuracy of ultra-short-term power load forecasting, an ultra-short-term power load forecasting method based on the double-layer XGBoost (eXtreme Gradient Boosting) algorithm is proposed. The first layer of the method, the data processing layer, is based on the XGBoost algorithm and feature engineering to construct multiple weak learners to train layer by layer, and to filter out the feature set that has a significant impact on the power load; the second layer is the load forecasting layer. The feature set and load selected in the first layer are input, the hyperparameters of the XGBoost algorithm are optimized and the model is trained to obtain the load forecasting model with the highest accuracy and the smallest root mean square error. The built load forecasting model can avoid standardization of data features, and can reduce the impact of missing data fields, regardless of whether the features are interdependent, and the model learning effect is good. In the analysis of the calculation example, the load forecasting models based on single layer XGBoost, BP neural network and ARIMA are compared. The proposed method has higher prediction accuracy and has good generalization ability under different time periods of data sets. © 2021, High Voltage Engineering Editorial Department of CEPRI. All right reserved.