China has unveiled one of the most ambitious AI-powered energy projects ever attempted, using artificial intelligence to create a complete high-resolution map of the country’s renewable energy infrastructure.

Researchers from Peking University and Alibaba Group’s DAMO Academy developed a deep-learning system capable of identifying nearly 320,000 solar facilities and more than 91,000 wind turbines across China using satellite imagery and AI analysis.

The breakthrough comes at a critical moment for the global economy, as rising electricity demand driven by artificial intelligence, data centres and digital infrastructure places increasing pressure on national power grids worldwide.

According to the International Energy Agency, global data-centre electricity consumption could approach 1,000 terawatt-hours by the end of the decade. While renewable energy production continues to expand, many countries still struggle with coordinating energy distribution efficiently on a national scale.

China’s new AI energy mapping system could change that.

The research team processed more than 7.5 terabytes of satellite imagery to build what experts describe as the first complete AI-generated inventory of a nation’s wind and solar infrastructure. The system allows operators to analyse how renewable energy sources complement one another across different regions.

One of the study’s key findings is that combining geographically distant wind and solar facilities significantly reduces fluctuations in energy generation. For example, cloudy conditions affecting solar farms in one province may be offset by stronger wind production elsewhere.

Researchers argue that China’s current province-based coordination model limits the efficiency of renewable energy management. A more unified national system could help stabilise electricity supply, reduce energy waste and improve long-term grid resilience.

The project also highlights the growing role of AI in managing modern infrastructure.

China’s rapid expansion of artificial intelligence services and large-scale computing facilities has dramatically increased electricity consumption. According to the China Electricity Council, power demand from the sector surged by 44% year-on-year during the first quarter of 2026 alone.

Many of the country’s new data centres are being built in northern and western provinces, where renewable energy resources are strongest and electricity costs are lower. These regions also show some of the highest levels of solar-wind complementarity identified by the AI system.

Experts say the achievement demonstrates how geospatial AI can transform the management of complex national infrastructure networks. The technology could eventually serve as a model for other countries seeking to modernise renewable energy systems while balancing rapidly growing electricity demand.

China’s clean energy sector generated an estimated 15.4 trillion yuan in economic output last year, underlining the enormous scale of the infrastructure now being coordinated through AI-driven analysis.

The study and its underlying dataset have been made publicly available for researchers and policymakers worldwide.

Author: Editorial Team
Source: AI News