  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 theory-guided deep-learning
load forecasting 2.0 (TgDLF2.0) to solve this issue, which is an improved
version of the theory-guided deep-learning framework for load forecasting via
ensemble long short-term memory (TgDLF). TgDLF2.0 introduces the deep-learning
model Transformer and transfer learning on the basis of dividing the electrical
load into dimensionless trends and local fluctuations, which realizes the
utilization of domain knowledge, captures the long-term dependency of the load
series, and is more appropriate for realistic scenarios with scarce samples.
Cross-validation experiments on different districts show that TgDLF2.0 is
approximately 16% more accurate than TgDLF and saves more than half of the
training time. TgDLF2.0 with 50% weather noise has the same accuracy as TgDLF
without noise, which proves its robustness. We also preliminarily mine the
interpretability of Transformer in TgDLF2.0, 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.
