Using deep learning for wind energy prediction

By Dan Bradby, CTO, Eliiza
Published 12 March 2019

The outcomes of this customer project, undertaken by Eliiza data scientists and Kasna cloud engineers, was part of documentation provided to Google for Kasna to achieve the Google Cloud Machine Learning Specialisation in January 2020. As part of Mantel Group,  the two companies work together as one to deliver rapid, scalable and applied ML solutions for customers.

The National Energy Market (NEM) is a wholesale energy market in Australia for generators and retailers to trade electricity. This market is managed by the Australian Energy Market Operator (AEMO) and is one of the largest interconnected electricity systems in the world.

The NEM includes the South Australian region which makes particularly heavy use of wind, solar and gas. In a market that relies heavily on these types of resources it is especially important to be able to predict future energy production and how that will service the predicted demand. Knowing this can inform planning around supporting sources of electricity such as gas or imports from other states. AEMO makes use of the Australian Wind Energy Forecasting System (AWEFS) to enable such planning, however the existing AWEFS model is not entirely transparent and works on detailed wind farm information not available to the public.

At Eliiza we are always interested in how Deep Learning might be applied to various industries. The NEM provides this opportunity through energy datasets being publicly available, both historically and in real-time (every 5 minutes). We have been exploring the effectiveness of training a model using Deep Learning (using the available energy and weather data) to be able to predict the available wind energy across SA for a 24 hour period.

Having trained several hundred variations of models on historical energy and weather data we have published our current model as part of a South Australia Wind Energy Dashboard. This dashboard is designed to give the user a sense of the contribution wind energy is making to the grid, as well as the predicted wind energy in the next 24 hours. The dashboard is updated with real-time data and the timeline control in the top right can be used to explore historical data.

South Australia Wind Energy Dashboard

The dashboard graph shows:

The additional tiles in the dashboard provide further context to the wind energy status in SA through:

  • wind and temperature (for a single weather station near Wattle Point Wind Farm)
  • a map of the current wind output of wind farms across SA
  • current contribution of wind energy to the grid

We will be monitoring the model’s ability to predict wind energy and potentially exploring alternative model architectures over the coming months.

  • For those who are interested in the technical white paper then please email us at

Special thanks to the OpenNEM project which has been a huge source of inspiration for this project.