A Robust Spatiotemporal Forecasting Framework for Photovoltaic Generation

Abstract

Deployment of PV generation has been recognized as one of the promising measures taken for mitigating the environmental issues worldwide. To seamlessly integrate PV and other renewables, accurate prediction is imperative to ensure the reliability and economy of the power system. Distinguished from most existing methods, this work presents a novel robust spatiotemporal deep learning framework that can generate the PV forecasts for multiple regions and horizons simultaneously considering corrupted samples. Within this framework, the Convolutional Long Short-Term Memory Neural Network is employed to exploit the temporal trends and spatial correlations of the PV measurements. Besides, given the collected PV measurements might be subject to various data contaminations, the correntropy criterion is integrated to give the unbiased parameter estimation and robust spatiotemporal forecasts. The performance of the proposed correntropy-based deep convolutional recurrent model is evaluated on the synthetic solar PV dataset recorded in 56 locations in U.S. offered by NREL. The comparative study is conducted against benchmarks over different sample contamination types and levels. Experimental results show that the proposed model can achieve the highest robustness among the rivals.