AI to unlock the next wave of renewable integration in ASEAN

This report by Ember examines how artificial intelligence (AI) can facilitate the integration of variable renewable energy (VRE) in ASEAN power systems. It identifies five key AI applications—forecasting, predictive maintenance, dispatch optimisation, real-time control, and dynamic line rating—that can reduce operational costs and emissions. While the region shows strong AI readiness, adoption is currently fragmented and limited to pilot projects. The report estimates that widespread AI adoption could save up to $67 billion and reduce CO2 emissions by nearly 400 million tonnes by 2035.

• Widespread adoption of AI in ASEAN's power sector could result in cumulative cost savings of up to $67 billion USD and a reduction of nearly 400 million tons of CO2 emissions between 2026 and 2035, specifically under high-VRE deployment pathways.
• ASEAN's variable renewable energy (VRE) share grew from 2.3% in 2020 to approximately 5% in 2025. Projections suggest VRE could reach 42–47% of generation by 2045, with some scenarios exceeding 60%.
• AI improves VRE integration through five primary applications: enhancing forecast accuracy (reducing reserve capacity and ramping needs), enabling predictive maintenance (reducing unplanned outages), optimising dispatch (lowering balancing costs), providing real-time control (strengthening security), and supporting dynamic line rating (deferring transmission investment).
• Several ASEAN nations, including Indonesia, Viet Nam, Thailand, Malaysia, and the Philippines, score above the global average in AI readiness indicators, though current deployment is uneven and largely limited to pilot projects rather than system-wide implementation.
• AI integration faces significant risks, including data quality and accessibility limitations, regulatory uncertainty regarding liability for probabilistic models, and increased cybersecurity vulnerabilities. Additionally, the growth of data centres could increase electricity demand by 2–30% of national demand across ASEAN (excluding Viet Nam) by 2030.
• The report recommends that ASEAN governments align regulations to incentivise accurate VRE forecasting, establish secure collaborative data ecosystems, implement 'cybersecurity by design', and create AI sandboxes for controlled experimentation.

APA

Pham, L., & Setyawati, D. (2026). AI to unlock the next wave of renewable integration in ASEAN. Ember. https://ember-energy.org/app/uploads/2026/03/AI-to-unlock-the-next-wave-of-renewable-integration-in-ASEAN.pdf

Chicago

Pham, Lam, and Dinita Setyawati. AI to unlock the next wave of renewable integration in ASEAN. Ember, 2026. https://ember-energy.org/app/uploads/2026/03/AI-to-unlock-the-next-wave-of-renewable-integration-in-ASEAN.pdf.

Wikipedia

{{cite report |last1=Pham |first1=Lam |last2=Setyawati |first2=Dinita |title=AI to unlock the next wave of renewable integration in ASEAN |publisher=Ember |date=3 March 2026 |url=https://ember-energy.org/app/uploads/2026/03/AI-to-unlock-the-next-wave-of-renewable-integration-in-ASEAN.pdf |access-date=17 August 2026 |via=Climate Insights Directory}}

BibTeX

@techreport{pham2026unlock, author = {Pham, Lam and Setyawati, Dinita}, title = {{AI to unlock the next wave of renewable integration in ASEAN}}, institution = {Ember}, year = {2026}, month = mar, url = {https://ember-energy.org/app/uploads/2026/03/AI-to-unlock-the-next-wave-of-renewable-integration-in-ASEAN.pdf}, urldate = {2026-08-17}, note = {Indexed by Climate Insights Directory} }