Abstract: In the short-term power load forecasting, when the power load data is sufficient, the accuracy of load forecasting is usually high, but when the data is missing or the data quantity is small, the accuracy of load forecasting is often poor. Therefore, when the power load data in a certain region is small, the load prediction accuracy is difficult to meet the prediction accuracy requirements. A short-term load prediction method based on 1DCNN-LSTM ( 1D Convolutional Neural-Long Short-Term Memory Networks ) and parameter transfer is proposed. 1DCNN-LSTM combined with transfer learning is used to solve the problem of low prediction accuracy. The actual load data of a certain area in the United States are used for simulation analysis. Experimental results show that this method can effectively improve the accuracy of load prediction when regional power load data is missing.

Key words: load forecasting, transfer learning, one-dimensional convolutional neural networks, long and short-term memory networks