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Short-Term Photovoltaic Power Generation Based on MVMD Feature Extraction and Informer Model

Short-Term Photovoltaic Power Generation Based on MVMD Feature Extraction and Informer Model

Authors :
Ruilin Xu
Jianyong Zheng
Fei Mei
Xie Yang
Yue Wu
Heng Zhang
Source :
Applied Sciences, Vol 14, Iss 14, p 6279 (2024)
Publication Year :
2024
Publisher :
MDPI AG, 2024.

Abstract

Photovoltaic (PV) power fluctuates with weather changes, and traditional forecasting methods typically decompose the power itself to study its characteristics, ignoring the impact of multidimensional weather conditions on the power decomposition. Therefore, this paper proposes a short-term PV power generation method based on MVMD (multivariate variational mode decomposition) feature extraction and the Informer model. First, MIC correlation analysis is used to extract weather features most related to PV power. Next, to more comprehensively describe the relationship between PV power and environmental conditions, MVMD is used for time–frequency synchronous analysis of the PV power time series combined with the highest MIC correlation weather data, obtaining frequency-aligned multivariate intrinsic modes. These modes incorporate multidimensional weather factors into the data-decomposition-based forecasting method. Finally, to enhance the model’s learning capability, the Informer neural network model is employed in the prediction phase. Based on the input PV IMF time series and associated weather mode components, the Informer prediction model is constructed for training and forecasting. The predicted results of different PV IMF modes are then superimposed to obtain the total PV power generation. Experiments show that this method improves PV power generation accuracy, with an MAPE value of 4.31%, demonstrating good robustness. In terms of computational efficiency, the Informer model’s ability to handle long sequences with sparse attention mechanisms reduces training and prediction times by approximately 15%, making it faster than conventional deep learning models.

Details

Language :
English
ISSN :
20763417
Volume :
14
Issue :
14
Database :
Directory of Open Access Journals
Journal :
Applied Sciences
Publication Type :
Academic Journal
Accession number :
edsdoj.1ac01f74f524343ae276e845566ce67
Document Type :
article
Full Text :
https://doi.org/10.3390/app14146279