基于极端梯度提升的跨地区多种类电力需求预测

Power Demand Forecast of Different Types in Multiple Regions Based on Extreme Gradient Boosting

对多个地区不同形式电力需求进行预测,不仅可以保证各地区电力供给稳定,还可以对全国产生的不同形式电力资源按地区进行合理分配。但目前的方法多针对单地区进行单一时间序列的预测,无法满足能源互联网中对复杂情况的电力需求预测要求。针对此问题,设计一种基于极端梯度提升的跨地区多种类电力需求预测算法。该算法改进提升树方法,有效地防止过拟合,同时通过支持分布式并行的方式,提高训练效率。与其他方法相比,所提方法对训练样本总量和特征数据类型要求不严苛,并可用于多时间序列预测。实验结果表明,所提方法能在可接受误差范围内对各地区不同形式电力需求进行快速、准确的预测。

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.