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Volume 2, Issue 1
  • ISSN: 2950-3752
  • E-ISSN: 2950-3760

Abstract

Background

As a renewable energy, solar energy has the advantages of being non-polluting, clean, and renewable. With the variability of solar radiation, the complexity of meteorological factors, and other uncertain changes, how to measure its comprehensive uncertainty is very important.

Objective

The inherent regularity of the uncertainty behavior of photovoltaic power generation is revealed by the change process of photovoltaic power generation.

Methods

Using the empirical wavelet transform (EWT), the uncertainty measurement was studied from the perspective of social physics, and an optimized intelligent prediction model (PSOBOA-LSTM) was proposed based on the uncertainty.

Results

The intelligent prediction model (PSOBOA-LSTM) has a better prediction effect and higher accuracy than other models, and reveals the internal mechanism of the photovoltaic power generation system from the perspective of physics and sociology.

Conclusion

Using the PSOBOA-LSTM model can better facilitate the power dispatching department to reasonably arrange conventional power generation, coordinate operations and make maintenance arrangements based on the predicted photovoltaic power generation, and solve problems arising from the connection between grid dispatching and photovoltaic power generation forecasting.

© 2024 Bentham Science Publishers
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/content/journals/cai/10.2174/2666782702666230210113724
2023-03-14
2025-01-18

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/content/journals/cai/10.2174/2666782702666230210113724
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Intelligent Prediction of Photovoltaic Power Generation Considering Uncertainty Measurement
Curr Arti Intell 2, E100223213573 (2024); https://doi.org/10.2174/2666782702666230210113724
/content/journals/cai/10.2174/2666782702666230210113724
/content/journals/cai/10.2174/2666782702666230210113724
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  • Article Type:     Research Article
Keyword(s): KSPA inspection; photovoltaic power; power generation forecast; PSOBOA-LSTM model; stability analysis; uncertainty measurement

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