InfoCAVB-MemoryFormer: Forecasting of wind and photovoltaic power through the interaction of data reconstruction and data augmentation

Abstract

Rare or missing data pose significant challenges in the prediction of wind power (WP) and photovoltaic power (PV). Many methods address the data scarcity issue solely through augmentation techniques, often neglecting the impact of missing data on the augmentation process. When data augmentation is performed on missing datasets, the prediction accuracy cannot be further improved, and this is called as an extreme data scarcity problem. To solve this problem, we introduce two methods, called Information Maximizing Collaborative Adversarial Variational Bayes (InfoCAVB) and MemoryFormer, to achieve data augmentation based on missing data reconstruction. In this paper, the augmentation and reconstruction process are performed at the same time. When the reconstruction process is performed, InfoCAVB utilizes adversarial training to construct variational bayes that approximates the posterior distribution of real data to recover missing data. Meanwhile, in the augmentation process, InfoCAVB maximizes the mutual information between real data and reconstruction data to establish correspondences between specific dimensions of augmentation data and the features of real and reconstruction data, respectively. The advantage of InfoCAVB lies in incorporating data augmentation into data reconstruction through the information maximizing principle. Finally, we propose a MemoryFormer adapted for InfoCAVB to predict WP and PV, and the benefit of MemoryFormer lies in excavating the potential temporal correlations between reconstructed and augmented data. MemoryFormer embeds the distribution of real data into the generated data through memory units, ensuring consistent distribution during the training process of discrete reconstructed and augmented data. Experimental results indicate that the proposed InfoCAVB-MemoryFormer reduces the RMSE averages by 15.7%, 14.1%, and 5.92% for WP prediction and 28.23%, 22.67%, and 12.21% for PV prediction compared to other state-of-the-art model models, demonstrating the effectiveness of the proposed approach in extreme data scarcity scenarios.