How AI Is Solving Problems in China’s Energy Industry

In 2020, China Gas Holdings Ltd., one of China's leading state-owned natural gas suppliers serving more than 200 million urban residents, adopted artificial intelligence (AI) to enhance its gas demand forecasting processes. By integrating AI models that analyze historical consumption data alongside real-time variables—such as weather conditions, pricing trends, and geopolitical or economic shifts—China Gas replaced traditional human-based demand forecasting methods. This pivot resulted in significant cost reductions, with the company cutting expenses on emergency or “off-contract” gas purchases by 50 percent. These savings, amounting to tens of millions of yuan according to internal estimates, highlight the tangible financial benefits of AI integration [para. 1][para. 2][para. 3].

Building on these successes, China Gas expanded its AI applications to other operational areas. It implemented virtual assistants to handle customer inquiries, used AI-powered systems to streamline contract reviews, and deployed smart cameras for safety compliance. Notably, its AI-driven maintenance system—operating around the clock—has reduced equipment failure rates by 30 percent. Such advancements underscore the company's comprehensive approach to digital transformation, using AI to foster efficiency, safety, and cost-effectiveness [para. 4].

China Gas's progress mirrors a sector-wide trend in China, as energy companies—from oil and gas to power grids and mining—rapidly accelerate AI adoption. Their goals include reducing costs, bolstering efficiency, and improving workplace safety. This trend has been propelled by Beijing's policy emphasis on leveraging advanced technologies to counter economic challenges and intensifying technological competition with the United States [para. 5][para. 6].

AI is anticipated to lead the evolution of the energy sector into a more data-driven industry. Key players, such as China Gas and China Southern Power Grid Co. Ltd., attribute the recent acceleration of AI adoption to the emergence of open-source models from startups like DeepSeek. DeepSeek’s release of its RI large language model in January—boasting performance on par with global leaders at a significantly lower cost—has made sophisticated AI tools more accessible to a broader range of businesses [para. 7][para. 8][para. 9].

In a February meeting, the State-owned Assets Supervision and Administration Commission (SASAC) announced ambitions for greater AI development and utilization among state-owned enterprises (SOEs). By late March, SOEs had deployed AI in over 500 different operational scenarios, reflecting the scale and speed of AI integration in China’s energy landscape [para. 10][para. 11][para. 12].

The transition to AI in China’s energy sector is seen as inevitable by industry leaders, who stress the importance of embracing and rapidly integrating the technology [para. 13]. In the context of the broader green energy transition, Southern Power Grid leverages AI to balance supply-demand variations due to renewable sources like solar and wind. This operator, serving 272 million people, now achieves forecasting speeds ten times faster and 98.4 percent accuracy rates in predictions, while also improving the detection of equipment issues and inspection efficiency [para. 14][para. 15][para. 16][para. 17].

Mining is another key sector where AI is streamlining operations. For example, CMOC Group Ltd.’s molybdenum mine has shifted from manual ore tracking to using drones and AI-powered image recognition, reducing errors and improving processing efficiency [para. 18][para. 19].

While the benefits of AI adoption are significant, industry experts urge caution regarding the technology’s current limitations—especially in high-risk environments like mining—and warn that rapid AI adoption by SOEs may negatively impact employment, underlining the need for a prudent approach. Nevertheless, industries that rely heavily on data and analytics are considered ideally suited for AI-driven growth, innovation, and cost reductions [para. 20][para. 21][para. 22][para. 23].