Intelligent Prediction of Photovoltaic Power Generation Considering Uncertainty Measurement

Title:Intelligent Prediction of Photovoltaic Power Generation Considering Uncertainty Measurement

Volume: 2

Author(s): Guo-Feng Fan, Qing-Yi Ge, Si-Jie Ren, Shao-Xiang Hu and Wei-Chiang Hong*

Affiliation:

• Department of Information Management, Asia Eastern University of Science and Technology, New Taipei, 22064, Taiwan

Keywords: PSOBOA-LSTM model, photovoltaic power, power generation forecast, stability analysis, KSPA inspection, uncertainty measurement.

Abstract:

Background: As a renewable energy, solar energy has the advantages of being nonpolluting, 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 (PSOBOALSTM) 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.

Cite this article as:

Fan Guo-Feng, Ge Qing-Yi, Ren Si-Jie, Hu Shao-Xiang and Hong Wei-Chiang*, Intelligent Prediction of Photovoltaic Power Generation Considering Uncertainty Measurement, The Chinese Journal of Artificial Intelligence 2024; 2 : e100223213573 . https://dx.doi.org/10.2174/2666782702666230210113724