Short-term electric load prediction using transfer learning with interval estimate adjustment
Yuwei Jin, Moses Amoasi Acquah, Mingyu Seo, Sekyung Han
Abstract
Although we are currently in the era of big data, it is always challenging to obtain complete and large-scale data due to the information protection for users and enterprises. In most cases, only partial data can be obtained, which harms the machine learning-based prediction model's performance. To effectively improve load forecasting accuracy, we propose a CNN-GRU hybrid model with parameter-based transfer learning. By transferring the training model details from a model with an extensive dataset to another model trained with a smaller dataset, the performance and accuracy of a prediction model with a smaller dataset can be improved. After that, we use the solve-the-equation (STE) method to estimate the bandwidth of data distribution by minimizing the mean integrated square error (MISE), this will in conjunction with prediction results provides a load prediction interval, which can provide the future load curve's fluctuation range at a specific confidence interval (CI). To verify the proposed method's effectiveness, we use residential data from the United States and commercial data from South Korea for predictive analysis. The experimental results show that the load forecast method with interval estimate adjustment proposed in this paper can effectively improve the accuracy and reliability of load forecasting under the scarcity of data.