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Accurate prediction of long-term electricity demand has a significant role indemand side management and electricity network planning and operation. Demandover-estimation results in over-investment in network assets, driving up theelectricity prices, while demand under-estimation may lead to under-investmentresulting in unreliable and insecure electricity. In this manuscript, we applydeep neural networks to predict Australia's long-term electricity demand. Astacked autoencoder is used in combination with multilayer perceptrons orcascade-forward multilayer perceptrons to predict the nation-wide electricityconsumption rates for 1-24 months ahead of time. The experimental results showthat the deep structures have better performance than classical neuralnetworks, especially for 12-month to 24-month prediction horizon.

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