China Unveils AI Model Extending Hydropower Forecasting From 10 to 60 Days

China has unveiled its first AI-powered operation model for a 10-million-kilowatt-class integrated clean energy base combining hydropower, wind and solar, developed by Yalong River Hydropower Development Company for the Yalong River basin in Sichuan Province. The model extends effective river runoff forecasting from roughly 10 days to 60 days, adjusts reservoir operations in real time, and achieves a fault-identification accuracy rate above 96 percent for photovoltaic equipment. The Yalong River basin is one of China's largest integrated clean energy bases, with total installed clean energy capacity projected to reach 78 million kilowatts by 2035.

 

Why Forecasting Range Matters for Coordinating Three Power Sources

 

The core technical achievement is extending the forecasting window for river runoff sixfold, from about 10 days to 60. That extension matters because hydropower output depends directly on how much water is available and flowing through a river system, and a longer forecast horizon lets operators plan reservoir releases and power generation schedules weeks further in advance rather than reacting to conditions as they emerge day by day.

That longer runway becomes especially valuable when hydropower is managed alongside wind and solar rather than in isolation. Wind and solar generation fluctuate with weather on short timescales, while hydropower can be stored and released more flexibly, acting as a buffer that smooths out the gaps left by variable renewable sources. Coordinating all three effectively requires forecasting far enough ahead to plan how much hydropower capacity to hold in reserve for periods when wind and solar output is expected to dip, which is precisely the function a 60-day runoff forecast is designed to support.

 

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What the AI Model Actually Does

 

Academician Chen Deliang of the Chinese Academy of Sciences described the model's core contribution as connecting resource forecasting directly with power operation decisions, shifting energy management from reactive adjustment toward scientific prediction. In practice, that means the system does not simply forecast water, wind and solar conditions separately and leave human operators to interpret the implications; it integrates forecasting, power dispatching, operations and market trading into a single coordinated process for managing all three resources together.

The reported 96 percent fault-identification accuracy for photovoltaic equipment adds an operational reliability dimension alongside the forecasting improvements. Detecting equipment faults quickly and accurately reduces downtime and prevents small malfunctions from cascading into larger generation losses, which matters increasingly as the scale of integrated renewable installations grows and manual inspection of every component becomes less practical.

 

The Infrastructure Behind the Model

 

The system runs on domestic computing infrastructure and is supported by what is described as the country's first high-altitude cavern-based intelligent computing center, a facility built into elevated terrain rather than a conventional data center structure. That location choice likely reflects proximity to the Yalong River hydropower installations themselves, reducing the distance between where data is generated and where it is processed, alongside potential cooling advantages from the high-altitude environment.

Building the underlying compute infrastructure domestically also fits a broader pattern in China's technology strategy, prioritising self-sufficient computing capacity for critical infrastructure applications rather than relying on foreign-supplied systems, a consideration that extends beyond energy management into wider questions of technological and industrial independence.

 

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Why This Model Is Meant to Be Replicated

 

Zhao Zenghai of the China Renewable Energy Engineering Institute described the system as the first full-chain intelligent solution tailored specifically for a 10-million-kilowatt-class clean energy base, framing it as a replicable pathway for similar projects rather than a bespoke solution for the Yalong River site alone. That framing matters because China has numerous large-scale integrated clean energy bases at various stages of development, and a proven, transferable AI operations model could accelerate deployment across those sites without each requiring years of independent development.

The unveiling follows China's announcement in May of 51 high-value "AI+Energy" application scenarios, part of a broader national push to scale AI use across the energy sector. Whether this specific model proves genuinely transferable to other integrated hydropower, wind and solar bases at similar scale, and whether the forecasting and fault-detection improvements hold up as reliably across different river basins and climate conditions, will determine how far this approach spreads across China's expanding clean energy infrastructure.