# Australia's long-term electricity demand forecasting using deep neural networks

arXiv: https://arxiv.org/abs/1801.02148 · HTML: https://mlsanity.com/paper/1801.02148/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Jan 2018.

## Built on by (most cited first, 2 of 2)

- [Dynamical prediction of two meteorological factors using the deep neural network and the long short term memory (1)](https://mlsanity.com/paper/2101.09356/) (2021)
- [Dynamical prediction of two meteorological factors using the deep neural network and the long short-term memory (2)](https://mlsanity.com/paper/2104.14406/) (2021)

## Abstract (arXiv)

Accurate prediction of long-term electricity demand has a significant role in demand side management and electricity network planning and operation. Demand over-estimation results in over-investment in network assets, driving up the electricity prices, while demand under-estimation may lead to under-investment resulting in unreliable and insecure electricity. In this manuscript, we apply deep neural networks to predict Australia's long-term electricity demand. A stacked autoencoder is used in combination with multilayer perceptrons or cascade-forward multilayer perceptrons to predict the nation-wide electricity consumption rates for 1-24 months ahead of time. The experimental results show that the deep structures have better performance than classical neural networks, especially for 12-month to 24-month prediction horizon.