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

2018/01/07 by Homayoun Hamedmoghadam, Hamedmoghadam, Homayoun, Nima Joorabloo +3
Computer Science · Engineering · #Energy Load and Power Forecasting #FOS: Computer and information sciences #Image and Signal Denoising Methods #Neural and Evolutionary Computing (cs.NE) #Smart Grid Energy Management

paper · pdf · doi:10.48550/arxiv.1801.02148

openalex publication_date 2018/01/07 · openalex created_date 2018/01/26 · openalex updated_date 2026/07/28

Abstract

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.

Citations

• Deep Learning in Neural Networks: An Overview

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