Analog versus multi-model ensemble forecasting: A comparison for renewable energy resources

2021 AlSkaif, 2020, A systematic analysis of meteorological variables for pv output power estimation, Renew. Energy, 153, 12, 10.1016/j.renene.2020.01.150 Drisya, 2018, Diverse dynamical characteristics across the frequency spectrum of wind speed fluctuations, Renew. Energy, 119, 540, 10.1016/j.renene.2017.12.024 Zhang, 2022, Solar forecasting with hourly updated numerical weather prediction, Renew. Sustain. Energy Rev., 154, 10.1016/j.rser.2021.111768 Verbois, 2022, Statistical learning for NWP post-processing: a benchmark for solar irradiance forecasting, Sol. Energy, 238, 132, 10.1016/j.solener.2022.03.017 Verbois, 2018, Probabilistic forecasting of day-ahead solar irradiance using quantile gradient boosting, Sol. Energy, 173, 313, 10.1016/j.solener.2018.07.071 Kioutsioukis, 2014, De praeceptis ferendis: good practice in multi-model ensembles, Atmos. Chem. Phys., 14, 11791, 10.5194/acp-14-11791-2014 Kalnay, 2003, 341 Kioutsioukis, 2016, Insights into the deterministic skill of air quality ensembles from the analysis of AQMEII data, Atmos. Chem. Phys., 16, 15629, 10.5194/acp-16-15629-2016 Potempski, 2009, Est modus in rebus: analytical properties of multi-model ensembles, Atmos. Chem. Phys., 9, 9471, 10.5194/acp-9-9471-2009 Yang, 2021, Post-processing in solar forecasting: ten overarching thinking tools, Renew. Sustain. Energy Rev., 140, 10.1016/j.rser.2021.110735 Schulz, 2021, Post-processing numerical weather prediction ensembles for probabilistic solar irradiance forecasting, Sol. Energy, 220, 1016, 10.1016/j.solener.2021.03.023 Delle Monache, 2013, Probabilistic weather prediction with an analog ensemble, Mon. Weather Rev., 141, 3498, 10.1175/MWR-D-12-00281.1 Alessandrini, 2015, A novel application of an analog ensemble for short-term wind power forecasting, Renew. Energy, 76, 768, 10.1016/j.renene.2014.11.061 Shahriari, 2020, Using the analog ensemble method as a proxy measurement for wind power predictability, Renew. Energy, 146, 789, 10.1016/j.renene.2019.06.132 Alessandrini, 2019, Improving the analog ensemble wind speed forecasts for rare events, Mon. Weather Rev., 147, 2677, 10.1175/MWR-D-19-0006.1 Alessandrini, 2015, An analog ensemble for short-term probabilistic solar power forecast, Appl. Energy, 157, 95, 10.1016/j.apenergy.2015.08.011 Zhang, 2019, A Solar Time Based Analog Ensemble Method for Regional Solar Power Forecasting," in, IEEE Transactions on Sustainable Energy, 10, 268, 10.1109/TSTE.2018.2832634 Alessandrini, 2022, Predicting rare events of solar power production with the analog ensemble, Sol. Energy, 231, 72, 10.1016/j.solener.2021.11.033 Junk, 2015, Predictor-weighting strategies for probabilistic wind power forecasting with an analog ensemble, Meteorol. Z., 24, 361, 10.1127/metz/2015/0659 Davò, 2016, Post-processing techniques and principal component analysis for regional wind power and solar irradiance forecasting, Sol. Energy, 134, 327, 10.1016/j.solener.2016.04.049 Cervone, 2017, Short-term photovoltaic power forecasting using artificial neural networks and an analog ensemble, Renew. Energy, 108, 274, 10.1016/j.renene.2017.02.052 Brunner, 2015, Comparative analysis of meteorological performance of coupled chemistry-meteorology models in the context of AQMEII phase 2, Atmos. Environ., 115, 470, 10.1016/j.atmosenv.2014.12.032 OGIMET, https://www.ogimet.com/, 2023 (accessed 13 January 2023). BSRN, https://bsrn.awi.de/, 2023 (accessed 13 January 2023). AQMEII, http://aqmeii.jrc.ec.europa.eu, 2023 (accessed 13 January 2023). Buizza, 2005, A comparison of the ECMWF, MSC, and NCEP global ensemble prediction systems, Mon. Weather Rev., 133, 1076, 10.1175/MWR2905.1 Perez, 2013, Comparison of numerical weather prediction solar irradiance forecasts in the US Canada and Europe, Sol. Energy, 94, 305, 10.1016/j.solener.2013.05.005 Hacker, 2011, The U.S. Air Force Weather Agency’s mesoscale ensemble: scientific description and performance results: AFWA MESOSCALE ENSEMBLE, Tellus A, 63, 625, 10.1111/j.1600-0870.2010.00497.x Galmarini, 2004, Ensemble dispersion forecasting, part 1: concept, approach and indicators, Atmos. Environ., 38, 4607, 10.1016/j.atmosenv.2004.05.030 Galmarini, 2013, E pluribus unum*: ensemble air quality predictions, Atmos. Chem. Phys., 13, 7153, 10.5194/acp-13-7153-2013 Solazzo, 2012, Ensemble modelling of surface level ozone in Europe and North America in the context of AQMEI, Atmos. Environ., 53, 60, 10.1016/j.atmosenv.2012.01.003 Im, 2015, Evaluation of operational online-coupled regional air quality models over Europe and North America in the context of AQMEII phase 2. Part II: Particulate matter, Atmospheric Environment, 115, 421, 10.1016/j.atmosenv.2014.08.072 ECMWF, https://www.ecmwf.int/, 2023 (accessed 13 January 2023).