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

A generalised multi-factor deep learning electricity load forecasting model for wildfire-prone areas