Benchmark probabilistic solar forecasts: Characteristics and recommendations

被引:36
作者
Doubleday, Kate [1 ,2 ,3 ]
Hernandez, Vanessa Van Scyoc [1 ,2 ,3 ]
Hodge, Bri-Mathias [1 ,2 ,3 ]
机构
[1] Natl Renewable Energy Lab, Power Syst Engn Ctr, Golden, CO 80401 USA
[2] Univ Colorado, Dept Elect Comp & Energy Engn, Boulder, CO 80309 USA
[3] Univ Colorado, Renewable & Sustainable Energy Inst, Boulder, CO 80309 USA
基金
美国国家科学基金会;
关键词
Solar power; Irradiance; Solar forecasts; Probabilistic forecasts; Benchmarking; WIND POWER; PREDICTION INTERVALS; PHOTOVOLTAIC POWER; BAYESIAN METHOD; IRRADIANCE; ENSEMBLE; GENERATION; MODEL; VERIFICATION; RADIATION;
D O I
10.1016/j.solener.2020.05.051
中图分类号
TE [石油、天然气工业]; TK [能源与动力工程];
学科分类号
0807 ; 0820 ;
摘要
We illustrate and compare commonly used benchmark, or reference, methods for probabilistic solar forecasting that researchers use to measure the performance of their proposed techniques. A thorough review of the literature indicates wide variation in the benchmarks implemented in probabilistic solar forecast studies. To promote consistent and sensible methodological comparisons, we implement and compare ten variants from six common benchmark classes at two temporal scales: intra-hourly forecasts and hourly resolution forecasts. Using open-source Surface Radiation Budget Network (SURFRAD) data from 2018, these benchmark methods are compared using proper probabilistic metrics and common diagnostic tools. Practical implementation issues, such as the impact of missing data and applicability for operational forecasting, are also discussed. We make recommendations for practitioners on the appropriate selection of benchmark methods to properly showcase stateof-the-art improvements in forecast reliability and sharpness. All code and open-source data are available on Github for reproducibility and for other researchers to apply the same benchmark methods to their own data.
引用
收藏
页码:52 / 67
页数:16
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