Dual stream network with attention mechanism for photovoltaic power forecasting

The operations of renewable power generation systems highly depend on precise Photovoltaic (PV) power forecasting, providing significant economic, and environmental advantages for energy efficient buildings and urban energy systems. However, precise PV power forecasting, particularly, solar power is more challenging due to solar energy intermittence, instability, and randomness. These challenges hinder the integration of PV into smart grids, where accurate power forecasting is a promising solution in this direction, providing effective planning and management services. Therefore, in this work, we introduce a dual-stream network for accurate PV forecasting. The proposed network parallelly learns spatial patterns using convolutional network and temporal representations via sequential learning algorithm. These features are then integrated together to form a single, yet representative feature vector used as an input to self-attention mechanism to further select the optimal features for PV power forecasting. To the best of our knowledge, the proposed dual stream network with advanced features selection mechanism is a pioneering approach for time series analysis, narrowed towards PV power forecasting. We derive our network after a series of experimentations involving solo and hybrid models, resulting in higher forecasting accuracy against state-of-the-art models.