Deep Convolutional Graph Rough Variational Auto-Encoder for Short-Term Photovoltaic Power Forecasting

Abstract

Photovoltaic (PV) power is considered as one of the most promising sustainable energy resources in recent years. However, the existing intermittency in the nature of solar energy is a significant problem for the optimization of smart grids. In this paper, to overcome PV generation uncertainty and provide an accurate spatio-temporal (ST) PV forecast, we propose a novel deep generative convolutional graph rough variational autoencoder (CGRVAE) that captures each PV site's probability distribution functions (PDFs) of future PV generation in a modeled weighted graph. Having the learned PDFs enables CGRVAE to accurately generate the future values of PV power time series. To train and evaluate our model, we used the measurements of a set of PV sites in California, US. The sites are modeled as a weighted graph where each node represents PV measurements at each site while edges reflect their correlations. Using graph spectral convolutions the proposed model extracts the most relevant information of the graph to estimate the future PV given the historical time series for each node in the modeled graph. Experimental results show the superiority of CGRVAE over state-of-the-art forecasting approaches in terms of the root mean square error (RMSE) and mean absolute error (MAE) metric.