Chinese Scientists Develop AI Tool for Optimal Placement of Double-Faced Solar Panels

In this post:

  • AI tool by Chinese scientists locates prime spots for dual-sided solar panels, boosting solar energy efficiency.
  • Dual-sided panels in the Tibetan Plateau & Xinjiang deserts optimize solar output, addressing data gaps in renewable energy.
  • AI-driven insights offer global scalability, aiding solar industry decisions and future energy planning.

A team of Chinese scientists has unveiled an innovative artificial intelligence (AI) tool designed to pinpoint the most effective locations for the installation of double-sided solar panels. This breakthrough addresses a critical data gap within the renewable energy sector, offering the potential for significant advancements in solar energy utilization.

Maximizing solar energy output with dual-sided panels

Dual-sided solar panels have demonstrated superior power generation capabilities compared to their single-faced counterparts. By strategically placing these panels in regions with optimal sunlight conditions, such as the eastern Tibetan Plateau and the deserts of Xinjiang in northwestern China, researchers aim to maximize energy output from photovoltaic (PV) modules.

China, a global leader in solar PV module production, faces a shortage of data necessary for determining ideal locations for dual-sided solar panel installation. With just 17 radiation stations across the country providing limited information on solar power availability, researchers sought alternative methods to address this challenge.

To bridge this data gap, scientists from Tsinghua University and the National Tibetan Plateau Data Centre developed an AI model leveraging sunshine data from 2,500 weather stations nationwide. By training the AI on solar radiation and surface meteorological data, researchers could predict the amount of direct and diffuse radiation at any given location.

Application and implications of AI-driven insights

The AI model offers a scalable solution that can be applied globally without the need for additional local data training. By harnessing satellite data and ground observation, decision-makers within the solar industry and governmental authorities can now make informed choices regarding the deployment of solar panels.

According to Professor Yang Kun, the lack of comprehensive radiation data previously hindered effective planning for solar panel installations. However, with the AI model’s output supported by satellite data, stakeholders can now optimize panel deployment to harness the full potential of solar power.

Through extensive analysis, the research team identified the Tibetan Plateau and the Taklamakan Desert in Xinjiang as prime locations for dual-sided solar panels. These regions boast high levels of direct solar radiation due to their altitude and thin atmosphere, coupled with substantial diffuse solar radiation resulting from complex landscapes and cloud coverage.

Shao Changchun, a PhD candidate at Tsinghua University and the study’s first author, emphasized the significant solar potential of these remote areas. The AI-driven insights not only benefit the solar industry but also offer valuable data for future research and policy planning, particularly in regions lacking power infrastructure.

Prospects and global applications

The AI model’s accuracy was validated through comparisons with radiation data from around the world, highlighting its potential for global application. By integrating meteorological data from other countries, the system could further enhance solar radiation projections on a worldwide scale.

Beyond the realm of renewable energy, the data generated by the AI model holds implications for various sectors, including agriculture. Research indicates that plants exhibit more efficient photosynthesis under diffuse light conditions, underscoring the broader utility of AI-driven insights.

The development of an AI tool for optimizing the placement of double-sided solar panels represents a significant advancement in renewable energy technology. With its ability to fill critical data gaps and facilitate informed decision-making, this innovation has the potential to accelerate the transition toward sustainable energy solutions on a global scale.

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