A new method for short-term power load forecasting is proposed to address issues such as complex and non-stationary load data, as well as large prediction errors. Firstly, this study utilizes the maximum information coefficient (MIC) to analyze the correlation of feature variables and selects relevant variables related to power load sequences. At the same time, as the variational mode decomposition (VMD) method is susceptible to subjective factors, the study employs the rime optimization algorithm (RIME) to optimize VMD and decompose the original power load sequence. Then, the long and short-term time series network (LSTNet) is improved as the prediction model by replacing the recursive LSTM layer with BiLSTM and incorporating the convolutional block attention mechanism (CBAM). Comparative experiments and ablation experiments demonstrate that RIME-VMD reduces the root mean square error (RMSE) of the LSTM, GRU, and LSTNet models by more than 20%, significantly improving the prediction accuracy of the models, and can be adapted to different prediction models. Compared with LSTM, GRU, and LSTNet, the proposed BLSTNet-CBAM model reduces the RMSE by 35.54%, 6.78%, and 1.46% respectively, improving the accuracy of short-term power load forecasting.