Multibranch Attentive Gated ResNet for Short-Term Spatio-Temporal Solar Irradiance Forecasting

Abstract

The increasing penetration of solar generation into power grids has promoted the need for accurate and reliable short-term solar irradiance forecasting. This article introduces a novel multibranch attentive gated recurrent residual network (ResAttGRU) consisting of multiple branches of residual networks, gated recurrent units (GRUs), and the attention mechanism. The proposed multibranch ResAttGRU is capable of modeling data at various resolutions, extracting hierarchical features, and capturing short- and long-term dependencies. Moreover, this network also presents a strong multitimescale representative within the proposed architecture, while GRUs can exploit temporal information at less computational cost than the popular long short-term memory (LSTM). The novelty of the proposed architecture is to employ multiple convolutional-based branches with different filter lengths to learn multitimescale features jointly, accelerate the learning process, and reduce overfitting by leveraging shared representations as the auxiliary information. This study also compares the multibranch ResAttGRU networks with state-of-the-art deep learning methods using 18 years of NSRDB data at 12 solar sites. Finally, the proposed multibranch ResAttGRU requires 7.1% fewer parameters than multibranch residual LSTM while achieving similar average RMSE, MAE, and R-squared values.