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

by   Weijia Yang, et al.

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 sensitivity to large-scale climate variability in training data sets, e.g. El Niño or La Niña 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 potential annual energy saving of AU$80.46 million for the state of Victoria.


Wholesale Electricity Price Forecasting using Integrated Long-term Recurrent Convolutional Network Model

Electricity price is a key factor affecting the decision-making for all ...

A Deep Learning Framework for Short-term Power Load Forecasting

The scheduling and operation of power system becomes prominently complex...

Advanced Statistical Learning on Short Term Load Process Forecasting

Short Term Load Forecast (STLF) is necessary for effective scheduling, o...

The Importance of Environmental Factors in Forecasting Australian Power Demand

In this paper, a seasonal autoregressive integrated moving average (SARI...

A Single Scalable LSTM Model for Short-Term Forecasting of Disaggregated Electricity Loads

As a powerful tool to improve their efficiency and sustainability, most ...

Deep Learning for Forecasting the Energy Consumption in Public Buildings

In this paper we propose a Long Short-Term Memory Network based method t...

Enhancing streamflow forecast and extracting insights using long-short term memory networks with data integration at continental scales

Recent observations with varied schedules and types (moving average, sna...

Please sign up or login with your details

Forgot password? Click here to reset