摘要
为提升短期电力负荷预测精度,提出了一种变权组合预测策略。首先,为了降低负荷数据的不平稳度,使用变分模态分解(variational mode decomposition,VMD)将负荷数据分解成了高频、低频、残差3种特征模态分量。其次,充分计及负荷数据的时序特点,参考指数加权法原理设计自适应误差重要性量化函数,并结合组合模型在时间窗口内的历史负荷数据的均方预测误差设计改进最优加权法的目标函数和约束条件,以完成子模型的准确变权。最后,针对波动较强的高频分量选定极端梯度提升(XGBoost)和卷积神经网络-长短期记忆(CNN-LSTM)模型并使用改进最优加权法进行组合预测、低频分量使用多元线性回归(MLR)模型预测、残差分量使用LSTM模型预测,叠加各模态分量的预测结果,实现了短期负荷数据的准确预测。实验结果表明,使用策略组合模型的平均绝对百分比误差为4.18%。与使用传统组合策略的组合模型相比,平均绝对百分比预测误差平均降低了0.87%。
To increase short-term power load forecasting accuracy,this paper proposes a weighted combination prediction strategy.Firstly,in order to reduce the instability of load data,the variational mode decomposition(VMD)is used to decompose the load data into three feature mode components:high-frequency,low-frequency,and residual.Secondly,considering the temporal characteristics of the load data,an adaptive error importance quantification function is designed based on the principle of exponential weighting,and the objective function and constraint conditions of the improved optimal weighting method are designed based on the mean-square prediction error of the historical load data within the time window,in order to achieve accurate weight variation of the submodels.Finally,XGBoost and CNN-LSTM models are selected for the high-frequency components with strong fluctuations,and the improved optimal weighting method is used for combination prediction.The MLR model is used to predict the low-frequency components,and the LSTM model is used to predict the residual components.By superimposing the prediction results of each mode component,accurate prediction of short-term load data is achieved.The experimental results show that the average absolute percentage error of the combined model using this strategy is 4.18%.Compared to the combined model using existing combination strategies,the average absolute percentage prediction error is reduced by 0.87%.
作者
李志军
徐博
杨金荣
宁阮浩
Li Zhijun;Xu Bo;Yang Jinrong;Ning Ruanhao(State Key Laboratory of Reliability and Intelligence of Electrical Equipment,Hebei University of Technology,Tianjin 300130,China;School of Artificial Intelligence,Hebei University of Technology,Tianjin 300130,China;School of Electrical Engineering,Hebei University of Technology,Tianjin 300130,China)
出处
《国外电子测量技术》
2024年第2期1-8,共8页
Foreign Electronic Measurement Technology
基金
河北省科技支撑计划(15212105D)项目资助。
