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Energy x AI

AI Agents in
Energy & Power
From LLMs to Embodied Intelligence
A Full-Scene Revolution

From hundred-billion-parameter LLMs to quadruped robot dogs, from neuromorphic chips to multi-agent coordinated dispatch — AI is reshaping every link of the "source-grid-load-storage" chain.

100B+
Parameter-Level LLMs
99.7%
Robotic Operation Accuracy
99%
Power Consumption Reduction

"QingYuan" LLM — World's First 100B-Parameter Power Generation AI Foundation

In 2024, China Energy Investment Corporation released the world's first 100-billion-parameter AI LLM for the power generation industry — "QingYuan." Trained on decades of massive power generation operational data, the model covers thermal, hydro, wind, and solar power generation.

"QingYuan" delivers core capabilities in equipment fault prediction and diagnosis, combustion optimization, real-time carbon emission monitoring, and economic unit operation. In deployments, it reduced coal consumption by ~2g/kWh, decreased unplanned outages by over 30%, saving hundreds of millions annually.

"QingYuan" adopts a "foundation model + industry fine-tuning" pathway, enabling rapid cross-plant deployment and lowering AI adoption barriers.

~2g

Coal Consumption Reduction

~2g/kWh supply coal consumption reduction


Hundreds of millions in annual savings
30%+

Unplanned Outages Down

Unplanned outages decreased by over 30%


Covering thermal, hydro, wind, solar

"DaWaTe" & Embodied Robots — Brain + Body Collaboration

China Southern Power Grid released "DaWaTe" AI LLM, the power industry's first full-stack domestically controlled AI infrastructure platform. Covering power dispatch, equipment maintenance, and customer service, it provides a unified AI capability foundation.

Deeply coordinated with "DaWaTe" are self-developed embodied manipulation robot systems. These robots perform high-risk tasks such as substation switching operations, breaker operations, and relay plate insertion/removal.

Each robot integrates multimodal visual models, natural language instruction understanding, and 7-axis robotic arm precision control — forming a "DaWaTe brain commands, robot body executes" collaborative architecture.

99.7%
Operation Accuracy
12min
Per Switching Operation (manual 30min)
0
Personnel Safety Risk
220kV+
Pilot Substation Voltage

"GuangMing" LLM x "TianShu" Robot Dogs — Cloud-Edge Collaborative Inspection

State Grid's "GuangMing" LLM is China's first AI LLM covering the full spectrum of power business scenarios. It excels in power knowledge Q&A, dispatch decision assistance, equipment defect grading, and maintenance strategy recommendation.

Forming a "cloud-edge" synergy with "GuangMing" is the "TianShu" series quadruped robot dogs. These dogs autonomously traverse complex terrain, equipped with IR cameras, ultrasonic PD detectors, and gas sensors for full-coverage inspection.

Cloud Intelligence

"GuangMing" LLM provides real-time decision inference for "TianShu" — when a robot dog detects abnormal equipment heating, the cloud model immediately determines the defect grade.

Edge Execution

The robot dog performs real-time obstacle avoidance and path planning locally, while critical inference relies on the cloud model.

The Power Industry's First Brain-Inspired Computing Platform

The power industry has built its first brain-inspired AI computing platform using neuromorphic chips and spiking neural networks, achieving accuracy comparable to deep learning while consuming only 1% of traditional GPU power.

This is transformative for the industry — substations and transmission towers face power supply and heat dissipation challenges. The brain-inspired platform enables high-performance AI inference locally on low-power embedded devices.

The platform has been deployed in partial discharge detection and transformer acoustic analysis at multiple substations.

1%

Power of Traditional GPU

99% power reduction at equivalent accuracy


Neuromorphic chips + SNN
Edge · Local inference · Zero cloud

Smart Grid Dispatch & Renewable Forecasting — AI's Core Battlefield

Large-scale renewable integration poses unprecedented challenges to grid dispatch. AI technology is tackling this from both prediction and decision dimensions.

5%

Ultra-Short-Term Forecast Error

Fusing numerical weather prediction, satellite imagery, and real-time stations, AI forecasting reduces short-term power forecast error to under 5%.

Millisecond

Deep RL Optimal Dispatch

Deep reinforcement learning delivers N-1 security-constrained optimal dispatch in milliseconds.

AgentSH in Energy — Multi-Agent Collaboration Practice

AgentSH brings a new paradigm of multi-agent collaboration to energy dispatch, deploying multiple specialized AI agents connected through a unified message bus, building a closed-loop autonomous dispatch system.

Generation Dispatch Agent

Optimizes day-ahead unit commitment, economic dispatch, and reserve capacity allocation.

Grid Safety Agent

Performs real-time N-1 security checks and power flow calculations.

Market Trading Agent

Handles electricity spot market clearing and optimizes trading strategies.

Renewable Forecast Agent

Provides ultra-short-term wind/solar forecasts.

Fault Response Agent

Autonomous grid accident identification and recovery strategy generation.

Message Bus

AgentSH core — all agents communicate in real-time through the unified message bus.

The AI-Driven Smart Energy Era Is Accelerating

AI agents are evolving from auxiliary tools to irreplaceable core components of the power system. From 100-billion-parameter LLMs to quadruped robot dogs, AI is reshaping the entire "Sense-Cognize-Decide-Act" chain in the power industry.

LLMs deliver unprecedented knowledge understanding, embodied intelligence extends AI to the physical world, and platforms like AgentSH integrate isolated capabilities into collaborative solutions.

An AI-driven green, efficient, resilient smart energy era is accelerating toward us.