Supporting renewable energy planning and operation with data-driven high-resolution ensemble weather forecast

TL;DR

This paper introduces a data-driven, high-resolution ensemble weather forecasting method that improves accuracy and reduces computational costs for renewable energy planning, especially wind power, by combining learned climatological distributions with coarse forecasts.

Contribution

The authors develop a novel approach that integrates high-resolution climatological priors with coarse-scale forecasts, significantly enhancing forecast accuracy and efficiency for renewable energy applications.

Findings

Achieves high-resolution 10-day wind forecasts with < 1 hour computation on a GPU.

Outperforms existing downscaling methods in deterministic and probabilistic skills.

Reduces computational costs from thousands of CPU hours to under an hour.

Abstract

The planning and operation of renewable energy, especially wind power, depend crucially on accurate, timely, and high-resolution weather information. Coarse-grid global numerical weather forecasts are typically downscaled to meet these requirements, introducing challenges of scale inconsistency, process representation error, computation cost, and entanglement of distinct uncertainty sources from chaoticity, model bias, and large-scale forcing. We address these challenges by learning the climatological distribution of a target wind farm using its high-resolution numerical weather simulations. An optimal combination of this learned high-resolution climatological prior with coarse-grid large scale forecasts yields highly accurate, fine-grained, full-variable, large ensemble of weather pattern forecasts. Using observed meteorological records and wind turbine power outputs as references, the proposed methodology verifies advantageously compared to existing numerical/statistical forecasting-downscaling pipelines, regarding either deterministic/probabilistic skills or economic gains. Moreover, a 100-member, 10-day forecast with spatial resolution of 1 km and output frequency of 15 min takes < 1 hour on a moderate-end GPU, as contrast to $\mathcal{O}(10^3)$ CPU hours for conventional numerical simulation. By drastically reducing computational costs while maintaining accuracy, our method paves the way for more efficient and reliable renewable energy planning and operation.