| 引用本文: | 杨德州,刘嘉明,宋汶秦,等.基于改进型自适应白噪声完备集成经验模态分解的工业用户负荷预测方法[J].电力系统保护与控制,2022,50(4):36-43.[点击复制] |
| YANG Dezhou,LIU Jiaming,SONG Wenqin,et al.A load forecasting method for industrial customers based on the ICEEMDAN algorithm[J].Power System Protection and Control,2022,50(4):36-43[点击复制] |
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| 摘要: |
| 工业用户的负荷通常由多种负荷类型共同组成，结构较为复杂，并且常常含有较大的冲击性负荷。传统的负荷预测方法难以准确预测负荷突变，导致预测精度不高。将负荷分解成不同频率的分量再分别进行预测是较为可行的解决方式。提出了基于改进型自适应白噪声完备集成经验模态分解的工业用户负荷预测方法。首先，采用ICEEMDAN算法将工业用户的负荷分解为高、低频模态分量。该算法利用局部均值来替换模态的估计，避免了高斯噪声对模态分解的影响，改善了传统模态分解方法中模态混叠的现象。其次，采用长短期记忆神经网络、最小二乘支持向量回归算法分别建立高、低频分量的预测模型。最后，将各分量的预测结果进行叠加重构，得到了最终的预测结果。相比于单一预测方法、其他组合预测方法等多种预测方法，所提方法的平均绝对百分比误差分别降低了26.35%，12.75%，具有最高的预测精度。 |
| 关键词: 工业用户 负荷预测 ICEEMDAN算法 模态分解 |
| DOI：DOI: 10.19783/j.cnki.pspc.210665 |
| 投稿时间：2021-06-04修订日期：2021-08-26 |
| 基金项目:国家电网公司科技项目资助(522730191002) |
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| A load forecasting method for industrial customers based on the ICEEMDAN algorithm |
| YANG Dezhou,LIU Jiaming,SONG Wenqin,YANG Changhai,TUO Jianjun,WANG Fei |
| (1. Economic and Technological Research Institute of Gansu Electric Power Company, Lanzhou 730050, China;
2. North China Electric Power University, Baoding 071003, China) |
| Abstract: |
| The load of industrial customers usually contains various types. This leads to a complicated load structure and an inevitable composition of large impact loads. Traditional load forecasting methods find it difficult to accurately forecast these sudden changes in load patterns, resulting in low forecast accuracy. To improve load forecasting, an improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN) based method is proposed for industrial customers. It decomposes the load of industrial customers into different components in terms of frequencies and forecasts them separately. First, the ICEEMDAN algorithm decomposes the load into high and low-frequency modal components. The local mean is introduced to replace the modal estimation. This avoids the influence of Gaussian noise on modal decomposition, and improves on mode mixing in the traditional modal decomposition method. Secondly, long short-term memory (LSTM) and least squares support vector regression (LSSVR) algorithms are adopted to establish the forecasting models of high and low-frequency modes. Finally, the forecasting results of each component are superimposed and reconstructed to obtain the final load forecasting. Compared with multiple traditional methods such as the single forecasting and other combined forecasting methods, the mean absolute percentage error (MAPE) of the proposed method is decreased by 26.35% and 12.75% respectively, thus it has the highest forecast accuracy among them.
This work is supported by the Science and Technology Project of State Grid Corporation of China (No. 522730191002). |
| Key words: industrial customers load forecasting ICEEMDAN algorithm modal decomposition |
