Artificial intelligence (AI) is reportedly helping solar panels find their place in the world after a team of Chinese scientists was able to develop an AI model that predicts and locates the most optimum spots to place doubled-sided solar panels, maximizing their solar energy output.
Using the model, the researchers have already established that the eastern Tibetan Plateau and other regions of northwest China are prime locations for maximizing solar energy output.
The amount of diffuse solar radiation that reaches the back of a dual-sided photovoltaic (PV) panel determines how much electricity it can produce, as the team described in a paper published last month in the peer-reviewed Journal of Remote Sensing.
(Photo: American Public Power Association from Unsplash) According to Glocalities's recent survey, more people worldwide prefer to use solar power over fossil fuels.
When exposed to optimal sunshine, two-sided solar panels have the potential to provide higher power outputs than single-sided ones.
Finding the optimum site for them is essential to guarantee the most efficient use of resources because they are challenging to maintain and transport.
China is the largest producer of solar PV modules, producing over 80% of the world's supply. However, it does not contain the information needed to identify the ideal locations for two-sided solar panels.
Researchers from Tsinghua University in Beijing and the National Tibetan Plateau Data Centre developed an artificial intelligence model based on sunshine data from 2,453 weather stations throughout China to get around the paucity of field data.
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A New AI-Powered Approach
The researchers used the LightGBM machine learning model in conjunction with data augmentation to estimate both diffuse and direct sun radiation.
This research reportedly solves the limitations of limited and unevenly distributed ground-based observations by utilizing sunshine duration data gathered from over 2,453 meteorological stations across China.
This method cleverly avoids the conventional challenges of sparse and unevenly distributed ground-based observations by leveraging sunshine duration data collected from over 2,453 meteorological stations.
Record Accuracy for Optimum Solar Panel Spots
This work's main contribution is the innovative use of machine learning algorithms to predict solar radiation components with previously unheard-of accuracy.
These algorithms are trained on supplemented datasets. Because the approach is globally adaptable and does not depend on local ground truth data for calibration, it is especially revolutionary.
The primary investigator from Tsinghua University, Professor Kun Yang, praised their work and said that their approach greatly improves the precision and usefulness of solar radiation component estimates, opening the door for optimal solar energy use in China and possibly globally.
This novel method not only sets a new benchmark for solar radiation estimation but also offers a globally scalable solution, indicating a revolutionary change in the study and application of solar energy.
The recently created satellite-based dataset provides a comprehensive spatial study of solar radiation components and outperforms earlier datasets in terms of precision.
This development is critical to the solar energy industry because it allows for strategic site selection and system optimization, particularly in regions with significant solar energy potential.
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(Photo: Tech Times)
