Dual-dimension Time-GGAN data augmentation method for improving the performance of deep learning models for PV power forecasting

Abstract

Solar energy has intermittency and volatility, which poses a serious challenge to the stable and safe operation of the power grid. Accurate photovoltaic power prediction is one of the key technologies to solve this problem. Due to the larger volume of data collected by photovoltaic systems, deep learning methods are more in-depth in the field of photovoltaic power prediction. However, photovoltaic power generation data also has more diversity and lower value density, which brings more severe challenges to the feature extraction ability of prediction models. Photovoltaic power prediction methods with deep data mining capabilities are urgently needed to address these issues. This paper proposes a dual-dimensional time series adversarial neural network based on gated recurrent neural network (Dd_Time_GGAN) to enhance low-value-density photovoltaic data by two dimensions (time dimension and feature dimension) and obtain high-value-density feature data. by replacing the generator and optimizer to improve the performance of network-enhanced data, the training time of the network was reduced by 26% by adjusting the iteration times of each stage of the network. Then, combining the two mainstream deep neural networks LSTM and CNN models in the current photovoltaic forecast field to achieve ultra-short-term photovoltaic power prediction. The experimental data is two-year operation data of a photovoltaic station in China. The experimental results show that compared with only using raw sample data, using enhanced data proposed by the data augmentation method combined with raw sample data as input, both types of deep learning prediction models have higher prediction accuracy. The accuracy of the LSTM prediction model improved by 3.1%, and the CNN prediction model improved by 2.3%. It proves that through data enhancement, the prediction accuracy of the photovoltaic power prediction model can be improved.