Solar power forecasting with sparse deep learning for fast frequency response ancillary services

Driven by the rapid increase in variable renewable generation, ancillary services are an integral part of the transition to carbon-neutral and cost-effective energy systems. Ancillary services maintain continuous and discrete balance between generation and load aggregates through frequency and voltage control. As a primary source of renewable energy, the accurate prediction of solar photovoltaic generation is crucial for the effective operation of Frequency Control Ancillary Services (FCAS). Although Deep Learning (DL) algorithms have been used to improve solar power forecasting accuracy, the computational complexity of DL is a technical limitation for the fast response time expected in FCAS. This article presents a novel DL approach composed of sparsity-inducing techniques that reduce the computational complexity of solar power forecasting without loss of forecasting accuracy. This approach applies sparsity within the forecasting models providing a balance between computational efficiency and interpretability, since model capacity is concentrated onto a smaller set of influential connections. The approach is empirically evaluated on two real-world solar power forecasting datasets from diverse geographical regions, followed by performance comparisons with dense DL models. The results confirm and validate the proposed approach in providing fast and accurate forecasts for the decision-making needs of FCAS. • Accurate solar forecasting is crucial for Frequency Control Ancillary Services (FCAS). • Computational complexity of deep learning is a limitation for expected FCAS speed. • Sparsity-inducing techniques can address this complexity without loss of accuracy. • We apply sparsity on critical data features of the forecasting task.