  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.
