摘要
传统的在线集成负荷预测模型根据固定时间长度窗口内样本数据的预测误差更新模型参数,忽略了窗口内不同预测误差对参数更新影响程度不同的问题。为此,该文提出一种新的基于误差在线更新的集成负荷预测模型。首先,建立基于拉格朗日的集成预测最优权重求解模型,在目标函数中引入误差影响矩阵来量化窗口内不同位置的预测误差对模型权重影响的差异。然后,借鉴AdaBoost调整样本权值的思想,设计一个权重衰减因子用于在窗口滑动过程中动态调节误差影响矩阵。应用提出的模型对中国北方某城市5个地区的真实用电负荷数据进行负荷预测实验,该文模型的平均预测误差平均绝对百分比误差为5.1%。与现有的在线集成预测模型相比,预测误差平均降低了0.91%。
The traditional online ensemble load forecasting model updates the model parameters according to the prediction errors of the sample data in the fixed length window and ignores the fact that different prediction errors in the window have different effects on the parameter updating.Therefore, a novel ensemble load forecasting model based on online error updating is proposed in this paper. First, a Lagrangian-based optimal weight solving model for ensemble prediction is established, and the error influence matrix is introduced into the objective function to quantify the influence difference of the prediction errors at different positions in the window. Then, inspired by Adaboost’s idea of adjusting sample weights, a weight decay factor is designed to dynamically adjust the error influence matrix during window sliding process. The model proposed in this paper is applied to the real power load data of five regions in a city in northern China. The mean absolute percentage error(MAPE) of the proposed model is 5.1%. Compared with the existing online ensemble prediction model, the prediction error is reduced by 0.91% on average.
作者
李俊良
焦润海
王双坤
何慧
LI Junliang;JIAO Runhai;WANG Shuangkun;HE Hui(School of Control and Computer Engineering,North China Electric Power University,Changping District,Beijing 102206,China)
出处
《中国电机工程学报》
EI
CSCD
北大核心
2023年第4期1402-1412,共11页
Proceedings of the CSEE
