  This paper proposes a generalised and robust multi-factor Gated Recurrent
Unit (GRU) based Deep Learning (DL) model to forecast electricity load in
distribution networks during wildfire seasons. The flexible modelling methods
consider data input structure, calendar effects and correlation-based leading
temperature conditions. Compared to the regular use of instantaneous
temperature, the Mean Absolute Percentage Error (MAPE) is decreased by 30.73%
by using the proposed input feature selection and leading temperature
relationships. Our model is generalised and applied to eight real distribution
networks in Victoria, Australia, during the wildfire seasons of 2015-2020. We
demonstrate that the GRU-based model consistently outperforms another DL model,
Long Short-Term Memory (LSTM), at every step, giving average improvements in
Mean Squared Error (MSE) and MAPE of 10.06% and 12.86%, respectively. The
sensitivity to large-scale climate variability in training data sets, e.g. El
Ni\~no or La Ni\~na years, is considered to understand the possible
consequences for load forecasting performance stability, showing minimal
impact. Other factors such as regional poverty rate and large-scale off-peak
electricity use are potential factors to further improve forecast performance.
The proposed method achieves an average forecast MAPE of around 3%, giving a
potential annual energy saving of AU\$80.46 million for the state of Victoria.
