Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Evaluation of performance for day-ahead solar irradiance forecast using numerical weather prediction
Numerical weather prediction (NWP) is widely used for day-ahead solar irradiance forecast, which is essential for applications in day-ahead energy market and energy management of different scales ranging from public level to civil level. In the literature, many NWP correction methods have been proposed to obtain more accurate solar irradiance forecast. However, when facing different real-world scenarios, it is crucial to efficiently design corresponding correction schemes, which require a detailed and reliable error evaluation foundation. To solve this problem, the performance for day-ahead NWP Global Horizontal Irradiance (GHI) forecast is evaluated under different weather conditions and seasons. The statistical analysis was conducted at each time of day and each NWP GHI forecast level with both publicly available datasets and actual field dataset, aiming to explore the detailed error characteristics of NWP GHI forecasts. The results demonstrate variations in NWP GHI error across diverse weather conditions and seasons, which indicates that future NWP GHI corrections should be developed under different weather conditions and seasons. For weather conditions, NWP GHI forecasts have the lowest accuracy during overcast conditions, followed by cloudy conditions, while the highest accuracy is observed during sunny conditions. Moreover, overestimations are more likely to occur during overcast and cloudy conditions. For seasons, the accuracy of NWP GHI forecasts is generally highest during winter. Additionally, we have summarized some common error characteristics under different weather conditions and seasons. This study provides useful information for improving the accuracy and efficiency of NWP correction works and for the stable operation of power systems.
Evaluation of performance for day-ahead solar irradiance forecast using numerical weather prediction
Numerical weather prediction (NWP) is widely used for day-ahead solar irradiance forecast, which is essential for applications in day-ahead energy market and energy management of different scales ranging from public level to civil level. In the literature, many NWP correction methods have been proposed to obtain more accurate solar irradiance forecast. However, when facing different real-world scenarios, it is crucial to efficiently design corresponding correction schemes, which require a detailed and reliable error evaluation foundation. To solve this problem, the performance for day-ahead NWP Global Horizontal Irradiance (GHI) forecast is evaluated under different weather conditions and seasons. The statistical analysis was conducted at each time of day and each NWP GHI forecast level with both publicly available datasets and actual field dataset, aiming to explore the detailed error characteristics of NWP GHI forecasts. The results demonstrate variations in NWP GHI error across diverse weather conditions and seasons, which indicates that future NWP GHI corrections should be developed under different weather conditions and seasons. For weather conditions, NWP GHI forecasts have the lowest accuracy during overcast conditions, followed by cloudy conditions, while the highest accuracy is observed during sunny conditions. Moreover, overestimations are more likely to occur during overcast and cloudy conditions. For seasons, the accuracy of NWP GHI forecasts is generally highest during winter. Additionally, we have summarized some common error characteristics under different weather conditions and seasons. This study provides useful information for improving the accuracy and efficiency of NWP correction works and for the stable operation of power systems.
Evaluation of performance for day-ahead solar irradiance forecast using numerical weather prediction
Dou, Weijing (Autor:in) / Wang, Kai (Autor:in) / Shan, Shuo (Autor:in) / Li, Chenxi (Autor:in) / Wen, Jiahao (Autor:in) / Zhang, Kanjian (Autor:in) / Wei, Haikun (Autor:in) / Sreeram, Victor (Autor:in)
01.07.2024
18 pages
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
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