Machine learning boosts solar forecasting to ease grid pressures in Australian study

Facebook
Twitter
LinkedIn
Reddit
Email
An operational solar farm in Australia, where the study took place. Image: Nextracker.

Machine learning techniques have been used in a study to boost the accuracy of renewables forecasts by up to 45%, helping improve grid operations in Australia.

The study, funded by the Australian Renewable Energy Agency (ARENA), is being coordinated by Monash University’s Grid Innovation Hub, industrial engineering firm Worley and renewables investor Palisade Energy and is designed to provide electricity generators with more accurate forecasting tools.

This article requires Premium SubscriptionBasic (FREE) Subscription

Unlock unlimited access for 12 whole months of distinctive global analysis

Photovoltaics International is now included.

  • Regular insight and analysis of the industry’s biggest developments
  • In-depth interviews with the industry’s leading figures
  • Unlimited digital access to the PV Tech Power journal catalogue
  • Unlimited digital access to the Photovoltaics International journal catalogue
  • Access to more than 1,000 technical papers
  • Discounts on Solar Media’s portfolio of events, in-person and virtual

Or continue reading this article for free

First launched in 2018, the study has contributed towards the development of forecasting models that are based on machine learning algorithms that draw on supervisory control and data acquisition (SCADA) data feeds from specific generators, which include a wind farm in South Australia and a solar farm in Queensland.

These forecasting models have been used to produce more accurate five-minute ahead forecasts, which in turn reduce the frequency of poor dispatch, allowing greater penetration of renewables sources on national grids.

Worley said that the forecasting algorithms helped improve power output predictions by 45%.

“Natural variations in weather makes it difficult for renewable generators to accurately forecast their short-term power generation levels and this impacts grid stability. In 2020 alone, inaccurate power predictions cost Australian generators AU$210 million, so using machine learning algorithms to see five minutes into the future is incredibly valuable,” Denis Marshment, global vice president for data science customer solutions at Worley, said.  

Worley added that the findings from the study would add to the “overall body of knowledge” around the use of machine learning in renewables forecasting and confirmed that the resulting forecasting models can be applied to all generators based in Australia, but stressed that more research was needed with regards solar projects.

The study was led by Dr Christoph Bergmeir from the Department of Data Science and AI at Monash University’s Faculty of Information Technology. Bergmeir said reliable short-term prediction methods were necessary to achieve network stability while accommodating greater quantities of renewable power.

“If renewable generators can lower their causer pays factors, they can produce electricity cheaper, and eventually that saving could be passed on to the customers. It would also make renewables more competitive, which is also a desirable outcome,” Bergmeir said.

Read Next

July 10, 2025
The Australian Renewable Energy Agency (ARENA) has announced a new AU$60 million (US$39.4 million) funding round to bolster R&D efforts to achieve ultra-low-cost solar.
Premium
July 9, 2025
PV Talk: Jon Previtali of VDE Americas explains the hard science behind managing hail risk, one of the costliest sources of damage to PV projects.
Premium
July 7, 2025
Collecting project performance data and managing cybersecurity concerns is no simple task for many project managers.
July 2, 2025
Robotics company Luminous has received AU$4.9 million (US$3.2 million) via Australia’s Solar ScaleUp Challenge to support deploying its ‘LUMI’ technology at utility-scale solar PV power plants.
Premium
June 30, 2025
Solargis CEO Marcel Suri explores three areas where new standards could help underpin greater efficiency, accuracy and market resilience.
Premium
June 10, 2025
PV Tech Premium spoke with Geoffrey Lehv of kWh Analytics about cybersecurity, AI and solar project underperformance.

Subscribe to Newsletter

Upcoming Events

Media Partners, Solar Media Events
September 2, 2025
Mexico City, Mexico
Solar Media Events
September 16, 2025
Athens, Greece
Solar Media Events
September 22, 2025
Bilbao, Spain
Solar Media Events
September 30, 2025
Seattle, USA
Solar Media Events
October 1, 2025
London, UK