关键词: 能源互联网, 电力需求预测, 负荷预测, 极端梯度提升, 多时间序列

Abstract: The forecasting of heterogeneous power demands in multiple regions not only ensures the stability of the power supply, but also reasonably distributes heterogeneous power resources produced nationwide. However, existing approaches mainly forecast single time series for single region, which cannot meet the power demand forecasting requirements for complex situations in the energy Internet. To solve the problem, an algorithm based on extreme gradient boosting is designed, which is able to predict the demand of multi-category power over different regions. The proposed algorithm improves the boosting tree method and effectively prevents over fitting. Meanwhile, it also improves the training efficiency by supporting distributed parallelization. Compared with other methods, the proposed method is less stringent on the total amount of training samples and characteristic data types and can be used for multi-time series forecasting. The experimental results show that the proposed algorithm can predict the different types of power demand in different regions quickly and accurately within the acceptable range of error.

Key words: energy Internet, power demand forecast, load forecast, extreme gradient boosting, multiple time series