A group of Chinese researchers has created an AI tool that could assist in identifying the optimal locations for installing double-sided solar panels, addressing a key data deficiency in the renewable energy sector.
Dual-faced panels have the capacity to generate more electricity compared to their single-faced counterparts. The researchers suggested that siting these panels in the eastern Tibetan Plateau and other areas in northwestern China could enhance solar energy production.
The effectiveness of a dual-sided photovoltaic (PV) panel largely hinges on the amount of diffuse solar radiation that reaches its rear surface, as detailed in a recent article published in the peer-reviewed Journal of Remote Sensing.
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In favorable sunlight conditions, two-sided solar panels can outperform traditional ones in power generation. However, due to challenges in transportation and maintenance, pinpointing the ideal installation sites becomes pivotal in resource optimization.
Nevertheless, the availability of data crucial for determining the most suitable locations for dual-sided solar panels is limited.
China currently operates only 17 radiation stations that monitor the quantity and quality of solar energy in specific areas. This data encompasses direct radiation, which directly impacts the front surface of a solar panel, and diffuse radiation, which scatters and is more likely to reach the panel’s rear surface.
Maintaining the equipment at these stations necessitates regular adjustments and upkeep, resulting in significant operational expenses.
To address the data insufficiency, a team from Tsinghua University and the National Tibetan Plateau Data Centre developed an AI model using sunshine data from 2,500 weather stations nationwide.
The AI algorithm was trained on solar radiation data gathered through ground observation or satellite remote sensing, along with surface meteorological information, to forecast the levels of direct and diffuse radiation at any given location.
“This model has the potential for global application without the need for additional training with local data,” the researchers stated.
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Yang Kun, a professor at Tsinghua University’s earth system science department and senior author of the study, highlighted that the lack of comprehensive radiation data hinders effective planning for solar panel installations by authorities and the industry.
“By leveraging the AI model supported by satellite data, decision-makers can now determine the optimal locations and types of panels to harness solar power efficiently,” Yang emphasized.
Solar energy contributed to nearly 5% of China’s electricity output in 2022.
Moreover, the AI system unveiled the solar potential in remote regions of China lacking power infrastructure, offering insights for future research and policy formulation.
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Shao Changkun, a PhD candidate at Tsinghua and the study’s lead author, identified the surroundings of the Taklamakan Desert in Xinjiang and the eastern Tibetan Plateau as prime sites for dual-sided panels.
“The high-altitude plateau with thin air experiences substantial direct solar radiation, coupled with significant diffuse solar radiation due to its intricate terrain and extensive cloud cover,” Shao explained.
“In these regions, both sides of the solar panels receive ample radiation.”
The researchers validated their estimates against global radiation data, affirming the high accuracy of their AI model. Shao suggested that integrating their model with meteorological data from other countries could facilitate global solar radiation projections.
Yang also highlighted the model’s applicability beyond the energy sector, citing its potential in fields like agriculture, where plants exhibit enhanced photosynthesis under diffuse light conditions.
