Individualized Short-Term Electric Load Forecasting With Deep Neural Network Based Transfer Learning and Meta Learning

While the general belief is that the best way to predict electric load is through individualized models, the existing studies have focused on one-for-all models because the individual models are difficult to train and require a significantly larger data accumulation time per individual. In recent years, applying deep learning for forecasting electric load has become an important research topic but still one-for-all has been the main approach. In this work, we adopt transfer learning and meta learning that can be smoothly integrated into deep neural networks, and show how a high-performance individualized model can be formed using the individual's data collected over just several days. This is made possible by extracting the common patterns of many individuals using a sufficiently large dataset, and then customizing each individual model using the specific individual's small dataset. The proposed methods are evaluated over residential and non-residential datasets. When compared to the conventional methods, the meta learning model shows 7.84% and 15.07% RMSE improvements over the residential and non-residential datasets, respectively. Our results suggest that the individualized models can be used as effective tools for many short-term load forecasting tasks.