Spatial load forecasting method based on double-layer XGBoost and data enhancement

Abstract

Spatial load forecasting faces the problems of multiple characteristic factors and data shortage. A spatial load forecasting method based on double-layer extreme gradient boosting (XGBoost) and data enhancement is proposed. Firstly, the area to be predicted is divided into several sub regions according to the supply range of feeder power. Secondly, a feature selection model based on double-layer XGBoost is constructed. The first layer XGBoost scores and sorts the features. The combined features are loaded into the second layer XGBoost for sub regional load forecasting. The best feature variables of each sub region are selected according to the load forecasting results. Then, the training set samples of each sub region are enhanced by the generative adversarial network (GAN), and the load of sub regions is forecasted through the extreme learning machine (ELM). Finally, the predicted values of sub regions are added to obtain the load of the region to be predicted. Taking local areas of Shanghai as an example, the simulated experiment and comparative analysis are carried out. The results show that the proposed method can solve the problems of characteristic variable selection and data shortage at the same time, and has high prediction accuracy.

Keywords

• spatial load forecasting
• extreme gradient boosting (xgboost)
• feature selection
• generative adversarial network (gan)
• data enhancement
• extreme learning machine (elm)