Wind Speed Forecasts of a Mesoscale Ensemble for Large-Scale Wind Farms in Northern China: Downscaling Effect of Global Model Forecasts

Jianqiu Shi, Yubao Liu, Yang Li, Yuewei Liu, Gregory Roux, Lan Shi and Xiaowei Fan
Additional contact information
Jianqiu Shi: Key Laboratory for Aerosol-Cloud-Precipition of China Meteorological Administration, Nanjing University of Information Science and Technology, Nanjing 210044, China
Yubao Liu: Key Laboratory for Aerosol-Cloud-Precipition of China Meteorological Administration, Nanjing University of Information Science and Technology, Nanjing 210044, China
Yang Li: Key Laboratory for Aerosol-Cloud-Precipition of China Meteorological Administration, Nanjing University of Information Science and Technology, Nanjing 210044, China
Yuewei Liu: National Center for Atmospheric Research, Boulder, CO 80307, USA
Gregory Roux: National Center for Atmospheric Research, Boulder, CO 80307, USA
Lan Shi: Inner-Mongolia Meteorological Bureau, Huhhot 010051, China
Xiaowei Fan: Electric Power Dispatch Center, Jibei Electric Power Company, Beijing 100054, China

Energies, 2022, vol. 15, issue 3, 1-18

Abstract: To facilitate wind power integration for the electric power grid operated by the Inner Mongolia Electric Power Corporation—a major electric power grid in China—a high-resolution (of 2.7 km grid intervals) mesoscale ensemble prediction system was developed that forecasts winds for 130 wind farms in the Inner Mongolia Autonomous Region. The ensemble system contains 39 forecasting members that are divided into 3 groups; each group is composed of the NCAR (National Center for Atmospheric Research) real-time four-dimensional data assimilation and forecasting model (RTFDDA) with 13 physical perturbation members, but driven by the forecasts of the GFS (Global Forecast System), GEM (Global Environmental Multiscale Model), and GEOS (Goddard Earth Observing System), respectively. The hub-height wind predictions of these three sub-ensemble groups at selected wind turbines across the region were verified against the hub-height wind measurements. The forecast performance and variations with lead time, wind regimes, and diurnal and regional changes were analyzed. The results show that the GFS group outperformed the other two groups with respect to correlation coefficient and mean absolute error. The GFS group had the most accurate forecasts in ~59% of sites, while the GEOS and GEM groups only performed the best on 34% and 2% of occasions, respectively. The wind forecasts were most accurate for wind speeds ranging from 3 to 12 m/s, but with an overestimation for low speeds and an underestimation for high speeds. The GEOS-driven members obtained the least bias error among the three groups. All members performed rather accurately in daytime, but evidently overestimated the winds during nighttime. The GFS group possessed the fewest diurnal errors, and the bias of the GEM group grew significantly during nighttime. The wind speed forecast errors of all three ensemble members increased with the forecast lead time, with the average absolute error increasing by ~0.3 m/s per day during the first 72 h of forecasts.

Keywords: mesoscale ensemble system; wind power prediction; wind speed; diurnal variation; global forecast downscaling (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
References: Add references at CitEc
Citations: View citations in EconPapers (3)

Downloads: (external link)
https://www.mdpi.com/1996-1073/15/3/896/pdf (application/pdf)
https://www.mdpi.com/1996-1073/15/3/896/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jeners:v:15:y:2022:i:3:p:896-:d:734853

Access Statistics for this article

Energies is currently edited by Ms. Agatha Cao

More articles in Energies from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().