In an exclusive interview, we spoke with Professor Yu Lijun from Shanghai Jiao Tong University, who leads a research group at the National Energy Investment Corporation’s Intelligent Energy Innovation Institute. They have transported their cutting-edge research on intelligent low-carbon energy systems directly to the forefront of renewable energy generation in Xinjiang, where wind and solar power is being harnessed.
As we learned on October 27, efficient electricity generation in this region is no simple task, necessitating “smart forecasting.” Although Xinjiang’s weather is primarily sunny and stable, the diverse terrains—including basins, mountains, and deserts—complicate weather patterns significantly. Professor Yu’s team is addressing this challenge by enhancing local weather forecasting accuracy through advanced technological methods. The energy transition in western China is still confronted with hurdles such as insufficient infrastructure and limited technological capabilities. The team is particularly focused on integrating artificial intelligence with existing technologies to improve the accuracy of wind and solar power output predictions.
The research group is developing a big data platform for wind and solar power forecasting and intelligent operation and maintenance. Liu Yuhang, a second-year master’s student, explained that this platform integrates data monitoring, analysis, and processing from various wind and solar power generation sources. “With this platform, we can provide accurate power forecasts and facilitate intelligent maintenance through real-time monitoring and adjustment of parameters based on weather data,” he noted.
Additionally, Professor Yu’s team has created a prototype for a device that employs state awareness and intelligent decision-making to manage wind and solar energy systems. PhD student Liu Xinyi stated, “By integrating AI with traditional operation and maintenance practices, we are ushering in a new phase of development for the wind and solar energy sectors.” As technology evolves rapidly, large model architectures are emerging as key players in AI development, creating transformative opportunities for traditional energy production.
We also explored how high-precision light sensors can enhance solar power generation. Xu Shuai, another second-year student, explained, “We typically use photovoltaic sensors to measure light intensity from various angles and feed that data back to control systems. This allows us to adjust the angle of solar panels to optimize their exposure to sunlight, thereby increasing energy efficiency.”
Security in power generation facilities, whether for wind or solar, remains a top priority. First-year student Zhen Zheng shared, “We utilize online smart detection systems with drones to monitor equipment like solar panels and wind turbines, ensuring the safety of our generation sites with high-definition surveillance.” This technology will cover various areas, including solar and wind energy as well as energy storage.
Professor Yu emphasized that in this era of constant technological updates, digital transformation is essential for all industries, including traditional power plants. The application of AI and other technologies not only enhances data accuracy and production efficiency but also ensures worker safety. In the future, this intelligent system will enable automated maintenance and immediate alerts during system failures, promoting safe operations while reducing costs.
During our interview, we learned that this research group has traveled extensively this year across six different locations to study energy transitions and the current state of high-quality development in western China. They are gathering insights into existing traditional energy adjustment strategies and advancements in emerging energy technologies. The team believes that transitioning the energy structure will significantly propel high-quality development in western regions, leveraging energy internet and new power systems to fuse energy and information, ultimately paving the way for a more efficient and low-carbon energy industry and contributing to sustainable development goals.
