首页|基于VMDT-POA-DELM-GPR的两阶段短期负荷预测

基于VMDT-POA-DELM-GPR的两阶段短期负荷预测

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针对传统负荷预测方法精度不高的问题,为准确捕捉到负荷数据波动的规律,提出了一种两阶段负荷预测方法.第1阶段首先用变分模态分解(VMD)对原始负荷序列进行分解,得到分解处理后的残差分量,再采用时变滤波经验模态分解(TVF-EMD)方法进行特征提取;然后对全部子序列分别建立深度极限学习机(DELM)模型,同时利用鹈鹕优化算法(POA)进行参数寻优,叠加各子序列的预测值得到初始负荷预测值.第2阶段采用POA-DELM模型对误差分量进行预测;然后将第一阶段中所有子序列预测值和误差预测值作为特征输入到高斯过程回归(GPR)模型中,得到负荷最终的预测结果.结果表明,两阶段模型的均方根误差(RMSE)、平均绝对误差(MAE)分别为对比模型的4%～77%、4%～76%,而平均百分比误差(MAPE)仅为0.067 8%,可有效提高电力负荷的预测精度.
Two-stage short-term load forecasting based on VMDT-POA-DELM-GPR
For the sake of enhancing the power load forecasting accuracy,a two-stage short-term power load forecasting method is proposed.In the first stage,the original load series is decomposed using variational mode decomposition(VMD)to obtain the residual components after decomposition.Then,the time-varying filtering empirical mode decomposition(TVF-EMD)method is used for feature extraction.Next,a deep extreme learning machine(DELM)model is established for all subsequence,and pelican optimization algorithm(POA)is used to optimize the parameters.The initial load prediction value is obtained by adding the prediction value of each subsequence.In the second stage,the POA-DELM model is used to predict the error components.All subsequence prediction values and error prediction values in the first stage are input into the Gaussian process regression(GPR)model as features to obtain the final load prediction results.The results show that the root-mean-square deviation(RMSE)and mean absolute error(MAE)of the two-stage model are 4%～77%and 4%～76%of the comparison model respectively,while the average percentage error(MAPE)is only 0.067 8%,which can effectively improve the accuracy of power load forecasting.

variational mode decompositiontime-varying filtering empirical mode decompositionpelican optimization algorithmdeep extreme learning machinetwo-stage load forecasting

王强、刘宏伟、聂子凡

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三峡大学电气与新能源学院 宜昌 443002

智慧能源技术湖北省工程研究中心 宜昌 443002

变分模态分解 时变滤波经验模态分解 鹈鹕优化算法 深度极限学习机 两阶段负荷预测

国家自然基金科学基金宜昌科技研究与开发项目

52077120A201230215

2024

10.19652/j.cnki.femt.2305371

国外电子测量技术
北京方略信息科技有限公司

国外电子测量技术

CSTPCD
影响因子：1.414
ISSN：1002-8978
年,卷(期)：2024.43(1)
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