Researchers from China and Malaysia have explored the impact of artificial intelligence on photovoltaic power generation systems and related applications from a global perspective. They focused on reviewing AI applications in maximum power point (MPP) tracking, power forecasting, and fault detection in photovoltaic systems.
The researchers analyzed publications showing how AI techniques solve tracking problems, compiling individual and hybrid AI methods and examining their advantages and disadvantages. For power forecasting, AI algorithms can detect deviations from normal operations that may indicate faults or anomalies, where manual inspection may not be sufficient.
“The overall message is an optimistic outlook on how artificial intelligence can lead to more sustainable and efficient energy solutions,” said Xiaoyun Tian, a researcher at the Beijing University of Technology. “By improving the efficiency and deployment of renewable energy through artificial intelligence, there is significant potential to reduce global carbon emissions and make clean energy more accessible and reliable for a wider population.”
The paper also examines the challenges that arise as the operational performance of photovoltaic systems continues to improve, such as revised standards for achieving carbon neutrality, interdisciplinary collaboration, and emerging smart grids.
The researchers said that next steps should focus on advancing AI techniques for photovoltaic systems, applying AI solutions to existing photovoltaic infrastructure, scaling up successful AI integration, developing supportive policies, raising awareness of the benefits of AI, and aligning progress with global sustainability goals.
“AI-based techniques are essential for the future development and widespread adoption of solar energy technologies worldwide,” Tian added.
The research paper, “A Comprehensive Review of Artificial Intelligence Applications in the Photovoltaic Systems,” is available in the latest edition of CAAI Artificial Intelligence Research. The team consists of researchers from Beijing University of Technology, the Chinese Academy of Sciences, Hebei University, and Universiti Tunku Abdul Rahman.
Source: EnergyMag
Source: EnergyMag
