基于XGBOOST-DNN的中期电力负荷预测  

Mid-Term Power Load Forecasting Based on XGBoost-DNN

作　　者：杨洋 谷震浩 YANG Yang;GU Zhen-Hao(University of Chinese Academy of Sciences,Beijing 100049,China;Shenyang Institute of Computing Technology,Chinese Academy of Sciences,Shenyang 110168,China)

机构地区：[1]中国科学院大学,北京100049 [2]中国科学院沈阳计算技术研究所,沈阳110168

出　　处：《计算机系统应用》2021年第9期186-191,共6页Computer Systems & Applications

摘　　要：精准的负荷预测是电力工作者重要的工作之一,而负荷预测以预测周期的不同,一般可以划分为短期电力负荷预测与中长期电力负荷预测.其中中长期电力负荷预测相较短期电力负荷预测而言,该领域缺乏大量前沿工作者的探索.因此本文提出一种可应用于中期电力负荷预测领域且基于XGBoost-DNN的算法.该算法将树模型和深度神经网络相结合,并将短期电力负荷预测引入到了中期电力负荷预测的工作中,基于树模型自身特点,将数据特征加工成高阶的交叉特征,同时结合原有数据利用深度神经网络可学习到丰富的特征信息.这里是以2017全球能源预测竞赛的数据进行算法分析,其中实验表明,在中期电力负荷预测领域,该方法提出的XGBoost-DNN模型相较于DNN,LSTM而言,其具备更加精准的准确性.Accurate load forecasting is one of the important tasks for power workers,and power load forecasting can be generally divided into short-term forecasting and medium-and long-term forecasting depending on the forecasting period.Compared with short-term power load forecasting,medium-and long-term forecasting is little explored by cutting-edge workers.Therefore,this study proposes an XGBoost-DNN-based algorithm that can be applied to mid-term power load forecasting.The algorithm combines the tree model with the deep neural network and introduces short-term forecasting into mid-term forecasting.According to the characteristics of the tree model,the data features are processed into highorder cross features,and in combination with the original data,the deep neural network is used to learn rich feature information.Algorithm analysis with the data of the 2017 Global Energy Forecasting Competition shows that in mid-term power load forecasting,the XGBoost-DNN model proposed by this method is more accurate than DNN and LSTM.

关 键 词：负荷预测 中期负荷预测 多目标回归 深度神经网络 XGBoost 

分 类 号：TM715[电气工程—电力系统及自动化]