AI to unlock the next wave of renewable integration in ASEAN

The report argues that AI can significantly accelerate the energy transition in ASEAN by managing the complexity of rising variable renewable energy (VRE) shares. 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 ASEAN shows strong digital readiness, current AI adoption is fragmented and pilot-based. The report estimates that widespread AI adoption could save up to $67 billion and reduce nearly 400 million tonnes of CO2 by 2035, provided that regulatory, data, and cybersecurity challenges are addressed.

• 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 on high-VRE deployment pathways.
• AI is particularly valuable as the share of variable renewable energy (VRE) increases; cost benefits are maximised in scenarios where VRE reaches a 42% share of generation by 2035 because renewable-based systems have higher upfront CAPEX and O&M costs.
• AI improves VRE forecast accuracy by approximately 25% compared to traditional numerical weather prediction, which allows operators to reduce reserve margins and reliance on inefficient standby thermal generation.
• Dynamic Line Rating (DLR) enabled by AI can allow transmission lines to safely carry an additional 10-30% capacity above their maximum rating for about 90% of the time, reducing the need for new transmission investment.
• Despite strong AI readiness in countries like Indonesia, Viet Nam, Thailand, Malaysia, and the Philippines, current deployment is uneven and largely limited to pilot projects rather than system-wide planning.
• AI integration faces significant risks, including data quality limitations, regulatory uncertainty regarding liability for probabilistic models, and increased cybersecurity vulnerabilities, with some ASEAN countries like Cambodia, Myanmar, and Lao PDR ranking below the world average in cybersecurity capacity.
• The growth of AI-driven data centres may strain power grids, with projections that they could account for 2–30% of national electricity demand across ASEAN (excluding Viet Nam) by 2030.

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} }