A review of distributed solar forecasting with remote sensing and deep learning

Abstract

The rapidly growing capacity of globally distributed solar generation systems (DSGs) has imposed new challenges for solar forecasting research: the need for high-fidelity spatial solar forecasts across utility-scale areas with minimized capital, generalization, and maintenance costs. The majority of solar forecasting approaches were developed for centralized solar power plants, which only concern one or a few locations. Therefore, this work reviews the state-of-the-art methods for spatial solar forecasting that integrate deep learning and remote sensing, potentially capable of serving numerous DSGs simultaneously. This work has four missions: (1) provide a review of available remote-sensing- and deep-learning-based spatial solar forecasting methods; (2) provide suggestions of practical tools to accelerate the research and deployment of spatial solar forecasting methods; (3) identify challenges of spatial solar forecasting for sparsely distributed DSGs; and (4) discuss prospective approaches to further enhance both the performance and value of spatial solar forecasts, such as the attention mechanism, sequence analysis, or probabilistic forecasts. This work reveals that practical spatial solar forecasting for DSGs is still in its infancy, thus more research efforts should be involved to develop a new generation of forecasting engines, which could cost-effectively address the real-time needs of integrating massive regional DSGs.