An adaptive deep-learning load forecasting framework by integrating transformer and domain knowledge

Abstract

Electrical energy is essential in today's society. Accurate electrical load forecasting is beneficial for better scheduling of electricity generation and saving electrical energy. In this paper, we propose an adaptive deep-learning load forecasting framework by integrating Transformer and domain knowledge (Adaptive-TgDLF). Adaptive-TgDLF introduces the deep-learning model Transformer and adaptive learning methods (including transfer learning for different locations and online learning for different time periods), which captures the long-term dependency of the load series, and is more appropriate for realistic scenarios with scarce samples and variable data distributions. Under the theory-guided framework, the electrical load is divided into dimensionless trends and local fluctuations. The dimensionless trends are considered as the inherent pattern of the load, and the local fluctuations are considered to be determined by the external driving forces. Adaptive learning can cope with the change of load in location and time, and can make full use of load data at different locations and times to train a more efficient model. Cross-validation experiments on different districts show that Adaptive-TgDLF is approximately 16% more accurate than the previous TgDLF model and saves more than half of the training time. Adaptive-TgDLF with 50% weather noise has the same accuracy as the previous TgDLF model without noise, which proves its robustness. We also preliminarily mine the interpretability of Transformer in Adaptive-TgDLF, which may provide future potential for better theory guidance. Furthermore, experiments demonstrate that transfer learning can accelerate convergence of the model in half the number of training epochs and achieve better performance, and online learning enables the model to achieve better results on the changing load.