China Deploys ‘Smartest Brain’ AI Models Across Energy Sector

China is rapidly accelerating the integration of artificial intelligence with its energy sector, deploying dozens of specialized “energy large models” across the entire energy value chain — from oil and gas exploration to power grid management and renewable energy forecasting. On May 26, the National Energy Administration (NEA) held a national “AI + Energy” promotion conference in Shenzhen, releasing 51 high-value AI application scenarios and the “China AI + Energy Development Report 2026,” while 25 energy companies signed an initiative to open AI application scenarios, as reported by Xinhua News Agency.

Policy Framework and Strategic Significance

The push is underpinned by a series of policy directives. In 2025, the NEA issued its “Implementation Opinions on Promoting High-Quality Development of AI + Energy” (Document No. 73), followed by a notice organizing AI + Energy pilot work (Document No. 168). At the Shenzhen conference, the NEA also unveiled an action plan for promoting two-way empowerment of AI and energy, and a special action plan for improving power supply quality from 2026 to 2028, according to China Daily.

China is the world’s largest energy consumer and the largest producer of renewable energy. Integrating AI into energy management is critical for managing the variability of wind and solar power, reducing reliance on imported energy technologies, and meeting the country’s 2030 carbon peak and 2060 carbon neutrality goals.

“With China’s ‘AI + Energy’ moving from concept to practice and from exploration to promotion, the industrial form is accelerating, innovative applications are breaking through on multiple fronts, and the foundation for integration is continuously being consolidated,” said Wang Hongzhi, Director of the National Energy Administration, at the Shenzhen conference.

Three Pillars of the AI-Energy Transformation

CNPC Kunlun Large Model: From Passive Q&A to Active Intelligence

China’s first nationally registered large model in the energy and chemical industry — the CNPC Kunlun Large Model — has entered an “active intelligence” phase, according to a report from National Business Daily. The model now possesses six advanced AI capabilities covering 152 application scenarios across the entire industry chain, from oil and gas exploration to refining, technical services, and capital finance. Daily token calls have reached 48.5 billion.

The Kunlun model uses a “1+4+N” architecture with full-stack domestic AI technology, 1,754P intelligent computing power, and 620TB of industry training data — achieving a data quality score of 99.8 out of 100. An international version supports seven languages, positioning Chinese AI-energy technology for global export.

“The Kunlun model has completed the key leap from general Q&A to active intelligence — from ‘passive Q&A’ to ‘active work,’ from ‘pilot trials’ to ‘full industry chain coverage,’ and from ‘external computing power dependence’ to ‘comprehensive independent controllability,’” CNPC stated.

CHN Energy ‘Qingyuan’ Power Generation Model

The China Energy Investment Corporation (CHN Energy) has deployed its “Qingyuan” power generation model across four key business areas: safety and environmental protection, power trading, production dispatch, and equipment maintenance. The model addresses persistent challenges in power generation including high safety risks, difficult trading decisions, complex multi-energy coordination, and passive equipment maintenance, as detailed by CCTV News.

China Southern Power Grid ‘Dawate’ Model

In the power grid sector, China Southern Power Grid has developed the “Dawate” model, focused on production applications. It provides professional power knowledge retrieval, transmission and distribution defect detection, power dispatch, grid planning, and safety supervision services across the entire power grid business.

Real-World Impact: Measurable Results on the Ground

The deployment of these AI models is already producing tangible results at China’s largest energy facilities:

Changqing Oilfield — China’s largest oil and gas field — has deployed AI-based intelligent parameter adjustment and diagnostic applications across over 4,000 wells, reducing manual management workload by 67%. Gas recovery rates could increase by 3 to 5 percentage points as a result.

At the Yalong River Hydropower Station on the upper Jinsha River, a fully domestic, independently controllable intelligent hub for integrated water-wind-solar operations is being developed. It has extended runoff forecast lead time to 60 days, significantly improving renewable energy integration and grid safety.

At Zhongyuan Oilfield, the “Cloud Wing Patrol” AI drone inspection system has achieved real-time pipeline leak detection, illegal construction alerts, and pressure monitoring with intelligent early warning and precise location capabilities.

CNPC’s Kunlun model has achieved drilling risk warning accuracy of over 85%, with more than 300 warnings issued in six months. AI has reduced processing cycles for acoustic full-waveform applications from 20 days to just 3 days, representing a cost reduction of over 30%.

Analysis: Strategic Implications

Domestic Technology Independence

The emphasis on “full-stack domestic AI technology” and “independent controllability” reflects China’s strategic priority to reduce dependence on foreign AI chips and platforms, particularly given US export restrictions on advanced semiconductors. The Kunlun model’s successful deployment on domestic hardware signals progress in this effort.

Global Competitiveness

The international version of the Kunlun model, supporting seven languages, positions Chinese AI-energy technology for export. It could compete with Western industrial AI platforms and serve China’s Belt and Road energy partnerships.

Workforce Transformation

AI is reshaping energy sector employment. The “expert digital twin” feature at CNPC, incorporating knowledge from 172 domain experts, suggests a model where AI augments rather than replaces human expertise. However, the shift from manual inspection to AI-driven monitoring will require significant workforce retraining.

Challenges Ahead

Despite the rapid progress, industry experts identify three major challenges, as reported by CCTV News. First, inconsistent data standards and data silos across enterprises limit the training effectiveness and application scope of large models. Second, new security risks — including data leaks, algorithm vulnerabilities, and cyber attacks — require robust防护 frameworks. Third, there is a shortage of specialized AI-energy talent.

“The high-value application scenarios focus on pain points that have long constrained industry development,” the NEA stated. “AI technology has significant empowerment potential, but industry applications are still in early stages and may have disruptive, transformative impacts on industry development in the future.”

What to Watch For

With a July 30 deadline for pilot project applications, the coming months will reveal how quickly China’s energy companies can scale their AI deployments. Key questions include how data-sharing mechanisms will be established across competing state-owned enterprises, what cybersecurity frameworks will protect AI-controlled energy infrastructure, and whether domestic AI chips can scale to meet the demands of the entire energy sector.

The NEA’s call for unified data standards and the establishment of pilot programs signals a concerted effort to overcome fragmentation. If successful, China’s AI-energy integration could serve as a model for other nations seeking to modernize their energy infrastructure while advancing toward carbon neutrality goals.