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.