基于BKM-VMD-TCN的日前负荷精准预测

Accurate day-ahead load forecasting method based on BKM-VMD-TCN

中文关键词: 电力负荷预测  二分K均值  时间卷积网络  变分模态分解

英文关键词: power load forecasting  bisecting K-means  time convolutional network  variational modal decomposition

中文摘要:

      精准的日前负荷预测对于配电网优化规划至关重要。随着配电网获取的负荷数据日益多维和广泛，如何高效利用这些数据进行准确的日前负荷预测成为了当前研究的重点。为此，提出了一种集成数据预处理、数据分解和数据预测的全流程方法。首先，在数据预处理阶段，采用二分K均值聚类（bisecting K-means，BKM）技术降低数据噪声并分类数据，同时结合动态和静态特征提取负荷特性；接着，在数据分解阶段，运用变分模态分解（variational modal decomposition，VMD）技术分解预处理后的数据，得到具有强周期性和随机性的不同频率分量；最后，在数据预测阶段，基于时间卷积网络（temporal convolutional network，TCN）对各模态分量进行预测，并叠加各分量的预测结果，得到最终的日前负荷预测值。实例分析结果表明，提出的BKM-VMDTCN方法在预测精度上优于其他3种负荷预测方法。

英文摘要:

      Accurate day-ahead load forecasting is essential for optimizing distribution network planning. As the load data available to distribution networks becomes increasingly multidimensional and extensive, efficiently leveraging this data for precise day-ahead load forecasting has become a key research focus. To address this, an end-to-end approach that integrates data preprocessing, data decomposition,and data forecasting is proposed. In the data preprocessing stage, the bisecting K-means（BKM）clustering technique is used to reduce data noise and categorize the data, while combining dynamic and static feature extraction to capture load characteristics. In the data decomposition stage, the variational mode decomposition（VMD）technique is applied to decompose the preprocessed data into frequency components with strong periodicity and randomness. Finally, in the data forecasting stage, a temporal convolutional network（TCN）is employed to predict each mode component, and the predictions are aggregated to produce the final day-ahead load forecast. Case studies demonstrate that the BKM-VMD-TCN method proposed achieves superior forecasting accuracy compared to three other load forecasting methods.