Tackling variable renewables: the role of artificial intelligence

This report by Ember examines how artificial intelligence (AI) can accelerate the integration of variable renewable energy (VRE) in ASEAN power systems. It identifies five commercially proven AI applications—forecasting, predictive maintenance, dispatch optimisation, real-time control, and dynamic line rating—that address the technical challenges of wind and solar variability to improve grid stability, reduce operational costs, and defer expensive transmission investments.

• ASEAN's variable renewable energy (VRE) share grew from 2.3% in 2020 to approximately 5% in 2025. As of 2025, Viet Nam leads the region with over 12% of electricity from solar and wind, followed by Cambodia (7.6%), Thailand (6.3%), the Philippines (4.8%), and Singapore (2.7%), while Brunei and Timor Leste have not yet integrated VRE.
• AI-based forecasting can improve short-term prediction accuracy by about 25% compared to traditional numerical weather prediction. This reduces the need for reserve capacity and ramping stress on conventional generators. Examples include Google DeepMind improving wind power forecast accuracy by up to 20% for 36-hour ahead predictions for the UK's National Grid, and Elia in Belgium reducing system imbalance forecast error by 41%.
• AI enables predictive maintenance (PM) by detecting early-stage faults weeks or months in advance, reducing unplanned outages and extending asset life. Siemens reports up to 85% improvement in downtime forecasting and a 50% reduction in unplanned machine downtime. AES in the US achieved approximately 90% accuracy in predicting component failures, reducing repair costs per job from about $100,000 to $30,000.
• AI dispatch optimisation improves grid balancing by capturing non-linear factors like transmission losses and congestion in real-time. This can lead to fuel cost reductions of up to 1% and efficiency gains of 5%. Practical applications include Tata Power and AutoGrid in India achieving 75 MW of peak demand reduction within six months, and Octopus Energy orchestrating over 2 GW of flexible capacity in the UK.
• AI enhances real-time system control and security, providing computational speed-ups of 3–4 orders of magnitude over conventional solvers, as seen with IBM’s GridFM model. These tools allow operators to identify grid faults within seconds and precisely pinpoint locations, which is particularly critical for ASEAN regions with ageing infrastructure and extreme weather.
• Dynamic Line Rating (DLR) supported by AI allows transmission lines to safely carry 10-30% additional capacity above their maximum rating for about 90% of the time. This can reduce total system costs by 3-5.5% (approximately €4 billion annually in high-renewables scenarios according to a German study). In the US, PPL Electric Utilities avoided $65 million in congestion costs on one line in a single year using DLR.

APA

Ember (2026). Tackling variable renewables: the role of artificial intelligence. https://ember-energy.org/chapter/tackling-variable-renewables-the-role-of-artificial-intelligence/

Chicago

Ember. Tackling variable renewables: the role of artificial intelligence. 2026. https://ember-energy.org/chapter/tackling-variable-renewables-the-role-of-artificial-intelligence/.

Wikipedia

{{cite report |author=Ember |title=Tackling variable renewables: the role of artificial intelligence |date=3 March 2026 |url=https://ember-energy.org/chapter/tackling-variable-renewables-the-role-of-artificial-intelligence/ |access-date=17 August 2026 |via=Climate Insights Directory}}

BibTeX

@techreport{ember2026tackling, author = {{Ember}}, title = {{Tackling variable renewables: the role of artificial intelligence}}, institution = {Ember}, year = {2026}, month = mar, url = {https://ember-energy.org/chapter/tackling-variable-renewables-the-role-of-artificial-intelligence/}, urldate = {2026-08-17}, note = {Indexed by Climate Insights Directory} }