基于Hyperopt-LightGBM的直流配电网短期负荷抗噪声预测  

Hyperopt-LightGBM-based Noise-resistant Forecasting of Short-term Loads in the DC Dis-tribution Network

作　　者：韩璟琳 冯喜春 胡平 陈志永 李光毅 HAN Jinglin;FENG Xichun;HU Ping;CHEN Zhiyong;LI Guangyi(Economic and Technology Research Institute of State Grid Hebei Electric Power Co.,Ltd.,Shijiazhuang 050022,China;State Grid Hebei Electric Power Company,Shijiazhuang 050035,China)

机构地区：[1]国网河北省电力有限公司经济技术研究院,石家庄050022 [2]国网河北省电力有限公司,石家庄050035

出　　处：《高电压技术》2024年第11期4902-4911,I0007-I0009,共13页High Voltage Engineering

基　　金：河北省电力有限公司科技项目(SGHEJY00GHJS2000103)。

摘　　要：为提升直流配电网中的短期负荷预测准确性与抗噪性,提出了一种基于超参数优化(hyperparameter op-timization,Hyperopt)-轻量型梯度提升机(light gradient machine,LightGBM)的短期负荷抗噪声预测模型。首先,以环形中压直流配电网为场景,分析4种负荷的基本特征及其与历史数据(记为影响因素)的相关性,通过将相关性较强的影响因素作为输入,避免预测模型过拟合现象,从而提高负荷预测准确性及模型训练效率。然后,构建基于Hyperopt-LightGBM的中压直流配电网短期负荷预测模型,通过训练强学习器提高模型的抗噪性,进一步提高短期负荷预测准确性;通过Hyperopot提高模型自适应性,减轻人工调参负担。最后,基于直流配电网的4种负荷数据验证所提模型的有效性,不同预测模型下4种负荷的平均预测误差分别为:≤1.6%(所提模型),≤2.1%(极限梯度提升机模型),≤2%(随机森林模型)和≤4.1%(梯度提升决策树模型);不同噪声比下所提模型预测准确性>95%,且均高于传统模型。上述结果表明所提模型预测准确性更高、抗噪性及自适应性更好。In order to improve the accuracy and noise-immunity of short-term load forecasting in the direct current(DC)distribution network,this paper proposes a short-term load forecasting model for the DC distribution network based on light gradient boosting machine(LightGBM)with hyperparameter optimization(Hyperopt).Firstly,based on the ring-shaped medium voltage DC distribution network,the basic characteristics of four kinds of loads and their correlation with factors are analyzed.To improve the training efficiency of the forecasting model and avoid over-fitting,the factors with higher correlation are selected as inputs.Secondly,a short-term load forecasting model is constructed based on the Hyperopt-LightGBM,which can enhance the tolerance of the model to sample noise by training a strong learner and further improve the accuracy of load forecasting.To reduce the burden of manual parameter tuning and improve the adaptability of the model,the Hyperopt is used to obtain the optimal LightGBM model.Finally,four kinds of load simulation data of the DC distribution network are used to verify the effectiveness of the proposed model.The average prediction errors of different prediction models under the four kinds of loads are less than 1.6%(the proposed model),less than 2.1%(eXtreme gradient boosting based model),less than 3.2%(random forest based model),and less than 4.1%(gradient boosting decision tree based model),respectively.In addition,the prediction accuracy of the proposed model is always higher than 95%,and it is also higher than other models under different noise ratios.The results verify that the proposed prediction model has better performance in accuracy,noise immunity,and adaptability.Hence,the proposed model can be applied for high-accuracy and noise-resistant load forecasting of short-term loads in the DC distribution network.

关 键 词：中压直流配电网 短期负荷预测 样本噪声 超参数优化 轻量型梯度提升机 

分 类 号：TM715[电气工程—电力系统及自动化]