@Article{ee.2025.062627,
AUTHOR = {Penghui Liu, Tianyu Yang, Peng Zhang, Peiyuan Zou},
TITLE = {Photovoltaic Power Prediction Cosidering Mode Switching and Parallel Weight Adjustment},
JOURNAL = {Energy Engineering},
VOLUME = {122},
YEAR = {2025},
NUMBER = {4},
PAGES = {1387--1402},
URL = {http://www.techscience.com/energy/v122n4/60193},
ISSN = {1546-0118},
ABSTRACT = {The photovoltaic (PV) output process is inherently complex, often disrupted by a multitude of meteorological factors, while conventional detection methods at PV power stations prove inadequate, compromising prediction accuracy. To address this challenge, this paper introduces a power prediction method that leverages modal switching (MS), weight factor adjustment (WFA), and parallel long short-term memory (PALSTM). Initially, historical PV power station data is categorized into distinct modes based on global horizontal irradiance and converted solar angles. Correlation analysis is then employed to evaluate the impact of various meteorological factors on PV power, selecting those with strong correlations for each specific mode. Subsequently, the weights of meteorological parameters are optimized and adjusted, and a PALSTM neural network is constructed, with its parallel modal parameters refined through training. Depending on the prediction time and input data mode characteristics, the appropriate mode channel is selected to forecast PV power station generation. Ultimately, the feasibility of this method is validated through an illustrative analysis of measured data from an Australian PV power station. Comparative test results underscore the method’s advantages, particularly in scenarios where existing detection methods are lacking and meteorological factors frequently fluctuate, demonstrating its superior prediction accuracy and stability.},
DOI = {10.32604/ee.2025.062627}
}