A generalised multi-factor deep learning electricity load forecasting model for wildfire-prone areas. (arXiv:2304.10686v1 [eess.SY])

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 …

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