Data Augmentation-Based Photovoltaic Power Prediction

Xifeng Wang, Yijun Shen (), Haiyu Song and Shichao Liu
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Xifeng Wang: Department of Automation, Zhejiang University of Technology, Hangzhou 310023, China
Yijun Shen: Department of Automation, Zhejiang University of Technology, Hangzhou 310023, China
Haiyu Song: School of Information Technology and Artificial Intelligence, Zhejiang University of Finance and Economics, Hangzhou 310018, China
Shichao Liu: Department of Electronics, Carleton University, Ottawa, ON K1S 5B6, Canada

Energies, 2025, vol. 18, issue 3, 1-15

Abstract: In recent years, as the grid-connected installed capacity of photovoltaic (PV) power generation has increased by leaps and bounds, it has assumed considerable importance in predicting PV power output. However, the power prediction of newly built small-scale PV power plants often suffers from many unexpected problems, such as data redundancy, data noise, data sample imbalance, or even missing key data. Motivated by the above facts, this paper proposes a data augmentation-based prediction framework for PV power. Firstly, the daily power distance measurement is used to analyze feature correlation and filter erroneous data. Then, the autoencoder network trained based on the random discarding mechanism is used to restore the PV power generation data. At the same time, a specific data augmentation method for the PV power curve is designed to eliminate the influence of data sample imbalance. In the final experimental section, compared with the latest method, this method achieved the highest MAE accuracy of 8.26% and RMSE accuracy of 10.96%, which proves the effectiveness of this method.

Keywords: prediction; photovoltaic power; autoencoder; data augmentation (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2025
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