Sub-seasonal forecasts of demand and wind power and solar power generation for 28 European countries

Abstract

Abstract. Electricity systems are becoming increasingly exposed to weather. The need for high-quality meteorological forecasts for managing risk across all timescales has therefore never been greater. This paper seeks to extend the uptake of meteorological data in the power systems modelling community to include probabilistic meteorological forecasts at sub-seasonal lead times. Such forecasts are growing in skill and are receiving considerable attention in power system risk management and energy trading. Despite this interest, these forecasts are rarely evaluated in power system terms, and technical barriers frequently prohibit use by non-meteorological specialists. This paper therefore presents data produced through a new EU climate services programme Subseasonal-to-seasonal forecasting for Energy (S2S4E). The data correspond to a suite of well-documented, easy-to-use, self-consistent daily and nationally aggregated time series for wind power, solar power and electricity demand across 28 European countries. The data are accessible from https://doi.org/10.17864/1947.275 (Gonzalez et al., 2020). The data include a set of daily ensemble reforecasts from two leading forecast systems spanning 20 years (ECMWF, an 11-member ensemble, with twice-weekly starts for 1996–2016, totalling 22 880 forecasts) and 11 years (NCEP, a 12-member lagged-ensemble, constructed to match the start dates from the ECMWF forecast from 1999–2010, totalling 14 976 forecasts). The reforecasts contain multiple plausible realisations of daily weather and power data for up to 6 weeks in the future. To the authors’ knowledge, this is the first time a fully calibrated and post-processed daily power system forecast set has been published, and this is the primary purpose of this paper. A brief review of forecast skill in each of the individual primary power system properties and a composite property is presented, focusing on the winter season. The forecast systems contain additional skill over climatological expectation for weekly-average forecasts at extended lead times, though this skill depends on the nature of the forecast metric considered. This highlights the need for greater collaboration between the energy and meteorological research communities to develop applications, and it is hoped that publishing these data and tools will support this.

Revealing the Statistics of Extreme Events Hidden in Short Weather Forecast Data

“…SSWs are a demonstrated source of surface weather predictability on the subseasonal‐to‐seasonal (S2S) timescale (Butler et al., 2019; Scaife et al., 2022; Sigmond et al., 2013). Pushing this “frontier” of weather forecasting can improve disaster preparation and resource management in the face of meteorological extremes (Bloomfield et al., 2021; White et al., 2017). For these reasons, there is keen interest in improving (a) the prediction of SSW itself beyond the horizon of ∼10 days that marks the current state‐of‐the‐art (Domeisen et al., 2020; Tripathi et al., 2016) and (b) understanding of the long‐term frequency and other climatological statistics of SSWs (Butler et al., 2015; Gerber et al., 2022).…”

Skillful prediction of UK seasonal energy consumption based on surface climate information

“…By convolving the weather sensitivity of an impacted system with atmospheric circulation, TCTs offer stronger ability in explaining power system variability and extreme events. In a related study, Bloomfield et al (2021) investigated high-quality meteorological forecasts in power system terms and presented an extensive power system forecast dataset with daily ensemble reforecasts and brief reviews of forecast skill. Ghalehkhondabi et al (2017) provided an overview of methods used to predict energy consumption from 2005-2015, including both traditional (e.g.…”

A probabilistic view on modelling weather regimes

“…Indeed, windows of opportunities can emerge when large-scale atmospheric drivers are active (e.g., during a polar vortex disruption [Büeler et al, 2020]), likely reflected in largescale phenomena like weather regimes. (White et al, 2017) give some examples of applications and opportunities for the adoption of these forecasts, while (Soret et al, 2019;Bloomfield et al, 2021) elaborate a specific focus on the energy sector, which is valuably impacted by the information of these sub-seasonal models. For this reason, there comes the necessity for accurate weather information and quantification of weather events (Bloomfield et al, 2016), which can be pursued through a better understanding and estimation of weather regimes (Jerez et al, 2013;de Felice et al, 2018;van der Wiel, Bloomfield, et al, 2019;van der Wiel, Stoop, et al, 2019).…”

“…In the recent years, weather regimes have gathered growing attention, both due to the development and increasing accuracy of sub‐seasonal models, and the growing applications in the industry of weather‐related information at different time and spatial scales in line with weather regimes' evolutions (i.e., weekly average and continental scales). Examples in the literature are (Bloomfield et al, 2021) who show that sub‐seasonal forecasts can provide useful information when averaged over periods beyond the 5 days, even though the precision of the forecasts can likely decrease. Indeed, windows of opportunities can emerge when large‐scale atmospheric drivers are active (e.g., during a polar vortex disruption [Büeler et al, 2020]), likely reflected in large‐scale phenomena like weather regimes.…”