Hourly disaggregation of daily wind projections: an analogue-based, spatially coherent approach to support energy applications

Climate projections often lack the high temporal resolution required to inform robust system planning and risk assessment in power grids with high variable renewable energy (VRE) generation. In this work, we present a novel and computationally inexpensive temporal disaggregation approach to generate plausible hourly time series from coarse daily climate model projections over multiple sites, with a focus on wind power generation. For each candidate day to disaggregate, the approach picks an analogue day from a historical hourly record, based on multi-site squared Euclidean distance between each candidate day and historical days, while also accounting for inter-day continuity. Hourly wind speed values from the analogue day are then rescaled across sites to match the daily data to disaggregate and converted into hourly capacity factor time series. We validate the framework using a 71 years open-source ERA5 reanalysis record for onshore wind speed and wind power generation across the twelve NUTS1 regions of the United Kingdom, which we split between training and test data sets (15 years). Our approach requires less than one minute to disaggregate 15 years daily mean data into hourly series. It successfully captures the full probability distribution of the test hourly data. It also addresses a longstanding limitation of disaggregation methods by preserving high hourly autocorrelation—up to 0.95—at midnight when the analogue day changes. The resulting hourly wind power time series also successfully reproduce key energy-modelling-relevant characteristics, including (1) the event-duration distribution of droughts, particularly the longer, system-critical events, and (2) the test data’s wind power ramp frequency and magnitude. Therefore, our analogue-based approach provides an efficient, reliable, and statistically consistent tool for generating plausible high-resolution VRE time series needed to inform critical investment and policy decisions for future decarbonised.