Multi 2D-CNN-based model for short-term PV power forecast embedded with Laplacian Attention

Amid the bloom of Renewable energy (RE) integrated into the grid, an accurate Photovoltaic(PV) power forecast is considered to be a crucial task in maintaining the reliability and stability of the power systems since this technology strongly depends on various external factors, causing the fluctuation in the output power. However, the poor quality of input data, which is very common in practical circumstances owing to the low-cost measurement and data acquisition devices, poses an enormous challenge for the predictive model to deeply extract the spatial and temporal correlation of the input data. This study proposes a Multi Two-Dimensional Convolutional Neural Network (2D-CNN) for short-term PV power forecast embedded with Laplacian Attention mechanism. By viewing the input sequences in a 2D form, the input map is constructed, and the interconnected feature among variables can be captured by convolution operation. Moreover, with the multiple CNN layers working in parallel architecture, different representations hidden inside the input map can be detected, enabling the proposed model to bring out promising performance across forecast time-step without modifying its initial parameters. In order to reduce the decay impact of irrelevant variables existing inside the input data, the Laplacian Attention mechanism is employed. The Attention matrix is dynamically modified during the training process to produce an accurate attention matrix, which represents the correlation between variables. Therefore, the model is able to focus on informative features and ignore negative ones. The experiments conducted on two different datasets with opposite characteristics provide deep insights into the strength of the proposed model over the baseline model, which strongly demonstrates the efficiency of the proposed model, especially when dealing with datasets bearing tough characteristics.