国产热热热精品,亚洲视频久久】日韩,三级婷婷在线久久,99人妻精品视频,精品九热人人肉肉在线,AV东京热一区二区,91po在线视频观看,久久激情宗合,青青草黄色手机视频

Global EditionASIA 中文雙語Fran?ais
Business
Home / Business / Industries

AI integration plan for energy sector

By ZHENG XIN | CHINA DAILY | Updated: 2026-05-30 06:31
Share
Share - WeChat
High-power broadband equipment is deployed at a 3D intelligent full-waveform inversion data acquisition site of China National Petroleum Corp. CHINA DAILY

China is stepping up efforts to integrate artificial intelligence into its energy sector, leveraging cutting-edge technology to drive industrial modernization, boost operational efficiency, and secure its energy future, according to industry reports and corporate milestones.

According to an action plan recently issued by the National Energy Administration, by 2030, the clean energy supply capacity for AI computing power infrastructure in China will be significantly increased, while the application of AI in the energy sector will also be considerably improved.

It also seeks to open up high-value application scenarios for AI in the energy sector, unlock the value of energy data, and strengthen AI model innovation within the energy field, it said.

According to the NEA, in 2025, China established 42 massive-scale intelligent computing clusters, pushing the total electricity consumption of national computing centers to 170 billion kilowatt-hours.

Globally, data center power consumption is projected to nearly double by 2030 compared to 2025, highlighting the urgent need for AI-driven optimization.

While this surging electricity demand presents a formidable grid challenge, China's robust energy infrastructure and rapid expansion of renewable capacity position the country uniquely to convert this power challenge into a global competitive advantage in the AI race, said Lin Boqiang, head of the China Institute for Studies in Energy Policy at Xiamen University.

"The interplay of AI and the energy sector has moved from one-way support to deep integration, becoming the core pathway for cultivating new quality productive forces and building an energy powerhouse," he said.

According to the NEA, to manage this surging demand and streamline complex industrial operations, the domestic energy sector has successfully rolled out dozens of industry-specific large AI models.

These advanced platforms cover a wide spectrum of fields, including power grids, renewables, thermal and nuclear power, coal, as well as oil and gas, it said.

Exemplifying this trend, China National Petroleum Corp announced a major breakthrough on Thursday with the latest iteration of its "Kunlun" large model. The upgraded platform marks a crucial leap from passive question-answering to "active intelligence," allowing the AI to autonomously plan, dispatch tools, analyze data, and execute tasks across the production line.

As the first large model in the domestic energy and chemical industry to achieve large-scale, full-chain application, Kunlun is now deployed across 152 scenarios.

The practical impact is already reshaping traditional operations. In addition to slashing the processing cycle for three-dimensional acoustic wave inversion from 20 days to just three, cutting costs by over 30 percent, the model also boasts a drilling risk warning system with an accuracy rate exceeding 85 percent, issuing over 300 early warnings in the past six months to prevent accidents, it said.

Such breakthroughs underscore a broader transformation across China's industrial landscape, said Lin.

By shifting from traditional manual oversight to active, AI-driven management, the country's energy sector is building a more resilient, efficient, and secure foundation to power its digital future, he said.

Top
BACK TO THE TOP
English
Copyright 1994 - . All rights reserved. The content (including but not limited to text, photo, multimedia information, etc) published in this site belongs to China Daily Information Co (CDIC). Without written authorization from CDIC, such content shall not be republished or used in any form. Note: Browsers with 1024*768 or higher resolution are suggested for this site.
License for publishing multimedia online 0108263

Registration Number: 130349
FOLLOW US
CLOSE
 
隆尧县| 邹城市| 泰宁县| 南昌市| 金山区| 秦安县| 湛江市| 鹰潭市| 乐东| 宜都市| 襄垣县| 鹤庆县| 全椒县| 刚察县| 进贤县| 德令哈市| 仪陇县| 宝坻区| 登封市| 新和县| 龙游县| 璧山县| 斗六市| 嘉定区| 民权县| 东乡族自治县| 玛曲县| 万全县| 民县| 上林县| 贺兰县| 大港区| 徐州市| 河南省| 鹿邑县| 华容县| 荔波县| 陆良县| 甘泉县| 洛隆县| 镇坪县|