Fine-grained power data prediction method, system, equipment and medium

The invention discloses a fine-grained power data prediction method, system and device and a medium, and relates to the technical field of industrial power energy consumption prediction. The method comprises the following steps: acquiring original power data; inputting the original power data into a pre-training network for optimization training, and determining the trained network as a prediction model; the prediction model is used for performing fine-grained power data prediction to obtain a final prediction result; the pre-training network comprises an EMD decomposition layer, a multi-branch BiLSTM layer and a DLSTM layer which are connected in sequence. According to the invention, data leakage can be avoided, and the data prediction accuracy is improved. 本发明公开一种细粒度电力数据预测方法、系统、设备及介质，涉及工业电力能耗预测技术领域。所述方法包括：获取原始电力数据；将所述原始电力数据输入预训练网络中进行优化训练，并将训练好的网络确定为预测模型；所述预测模型用于进行细粒度电力数据预测，得到最终的预测结果；所述预训练网络包括依次连接的EMD分解层、多分支BiLSTM层以及DLSTM层。本发明能够避免数据泄露，并提高数据预测准确率。