Summary of Key Points
This news article focuses on the "pain points" and "solutions" in the field of artificial intelligence (AI) for the power industry. During the 2026 World Artificial Intelligence Conference, a seminar hosted by the National Artificial Intelligence Application Pilot Base (in the energy and power sector) discussed the application of AI across the entire power chain, including generation, transmission, transformation, distribution, and consumption. As a key platform connecting research and industry, the pilot base helps companies transition AI technology from the laboratory to the market by providing computing resources and virtual simulation services. Participants identified challenges such as fragmented scenarios and data barriers in the widespread adoption of AI in the power sector and proposed solutions, including creating "four lists," coordinating resources, and building an industrial ecosystem, with the goal of advancing AI from individual breakthroughs to widespread industry-wide use.
1. The Pilot Base: The Converter from Laboratory Ideas to Practical Applications
Many AI technologies perform well in laboratories but fail to be effective in real-world power scenarios due to mismatches with actual operations or high testing costs. The pilot base serves as a bridge between research and industry:
- Connecting Research and Industry: It takes AI研究成果 from the laboratory and helps implement them in practical applications.
- Turning Technology into Profitable Products: It introduces mature technologies to companies, transforming them into profitable products.
For example, the base offers "digital twins"—virtual replicas of real power grids where companies can train AI models to test their performance under extreme weather conditions or equipment failures without risking the actual grid. The base also provides computing resources, data samples (which are difficult to obtain), training programs for professionals, and funding support, lowering the barriers to innovation.
2. Barriers to the Widespread Adoption of Power AI
Despite some individual successes, the widespread adoption of AI in the power industry faces several common issues:
- Fragmented Scenarios: The needs of different stages (generation, transmission, distribution) are vastly different, making it difficult to develop universal AI models and increasing development costs.
- Lack of Data Sharing: Power companies and departments often operate as isolated systems, limiting the availability of data needed for effective AI model training.
- Limited Reusability: AI solutions developed in one context may not work in another due to differences in grid infrastructure.
- Security Concerns with Network Access: The power system has secure internal networks, and transferring data across these networks poses risks.
- Disconnection between Technology and Business: Experts from AI and power sectors often lack mutual understanding, resulting in impractical technologies.
Experts noted that current AI in the power industry is still at a basic level of data processing and needs to integrate with the physical principles of electricity transmission while ensuring security and reliability.
3. Solutions Proposed by the Seminar
To address these challenges, participants suggested the following approaches:
- The Pilot Base's Five-Step Approach: Identify industry needs, conduct pilot projects, validate solutions within the base, implement them on a small scale, and then promote them on a larger scale.
- Huawei's Recommendations: Plan for long-term computing and talent resources to develop industry-specific large models (similar to ChatGPT but tailored for the power sector) that can be universally applied.
- South China Power Grid's "Four Lists": Compile high-quality datasets, standard interfaces for system interoperability, essential AI development tools, and ready-made solutions to facilitate direct use by companies. It also recommends collaborative efforts between AI and power experts.
- Ecosystem Building: Utilize open-source communities and trusted data platforms to create an efficient and open environment where data, computing resources, models, and scenarios work together.
4. The Future of Power AI
Power AI is moving from individual trials to systematic implementation, with a shift from technology-driven approaches to solutions driven by real-world needs. In the future, pilot bases will play a crucial role in bringing together resources from companies, research institutions, and governments to address key issues such as computing resource adaptation, data security, and integration of technology and business practices. The ultimate goal is to enable AI to cover the entire power chain, improving efficiency and safety in power systems.
This analysis not only captures the main points of the news but also explains complex concepts in plain language, making it accessible to non-experts.