Search for: All records

ABSTRACT Accurate wind power forecasts are essential for energy management and resource allocation. However, because of complex weather dynamics and other nonlinearities, it is exceedingly difficult to forecast wind power on the multisite level for dozens of wind farms at once. This paper proposes a hybridized approach that leverages deep learning to predict future forecast errors from physics‐based numerical weather prediction (NWP) model estimates. Utilizing errors from NWP forecasts allows integration of critical atmospheric and meteorological dynamics into the forecasting model, and we demonstrate the importance of post‐calibration based on the physics versus pure data‐driven wind power prediction. This post‐calibration approach is enabled by the inverted transformer architecture, which efficiently and effectively learns meaningful wind farm variate representations, resulting in accurate spatiotemporal corrections to the forecasts. We also investigate modifying the iTransformer with a new embedding approach, named SpaceEmbed, that explicitly encodes spatial distance information into the network. The proposed approach is validated with a case study using real‐world data and forecasts from the Electric Reliability Council of Texas (ERCOT) in 2015 for 74 wind farms in Texas at different time scales. Using the high sustained limit as the metric for power generation, the iTransformer outperforms other state‐of‐the‐art deep learning forecasting methods, succeeding at the post‐calibration task by reducing NWP forecast error by up to 33% on average. 

Terrestrial ecosystems encompass a vast and vital component of Earth's biodiversity and ecosystem services. The effect of increased anthropogenic dominance on terrestrial communities defines major challenges for ecosystem conservation, including habitat destruction and fragmentation, climate change, species invasions and extinctions, and disease spread. Here, we integrate fossil, historical, and present-day organismal and ecological data to investigate how conservation paleobiology provides deep-time perspectives on terrestrial organisms, populations, communities, and ecosystems impacted by anthropogenic processes. We relate research tools to conservation outputs and highlight gaps that currently limit conservation paleobiology from reaching its full impact on conservation practice and management. In doing so, we also highlight how the colonial legacies of conservation biology and paleobiology confound our understanding of present-day biodiversity, ecosystem processes, and conservation outlooks, and we make recommendations for more inclusive and ethical practices moving forward.