A database of hourly wind speed and modeled generation for US wind plants based on three meteorological models

被引:7
|
作者
Millstein, Dev [1 ]
Jeong, Seongeun [1 ]
Ancell, Amos [1 ]
Wiser, Ryan [1 ]
机构
[1] Lawrence Berkeley Natl Lab, Energy Anal & Environm Impacts Div, Berkeley, CA 94720 USA
关键词
FARM FLOW-CONTROL; REANALYSIS; SOLAR; ASSIMILATION; PERFORMANCE; STRATEGIES; DATASET; OUTPUT; ERA5;
D O I
10.1038/s41597-023-02804-w
中图分类号
O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];
学科分类号
07 ; 0710 ; 09 ;
摘要
In 2022, wind generation accounted for similar to 10% of total electricity generation in the United States. As wind energy accounts for a greater portion of total energy, understanding geographic and temporal variation in wind generation is key to many planning, operational, and research questions. However, in-situ observations of wind speed are expensive to make and rarely shared publicly. Meteorological models are commonly used to estimate wind speeds, but vary in quality and are often challenging to access and interpret. The Plant-Level US multi-model WIND and generation (PLUSWIND) data repository helps to address these challenges. PLUSWIND provides wind speeds and estimated generation on an hourly basis at almost all wind plants across the contiguous United States from 2018-2021. The repository contains wind speeds and generation based on three different meteorological models: ERA5, MERRA2, and HRRR. Data are publicly accessible in simple csv files. Modeled generation is compared to regional and plant records, which highlights model biases and errors and how they differ by model, across regions, and across time frames.
引用
收藏
页数:15
相关论文
共 50 条
  • [31] Generation and Validation of Spatial Distribution of Hourly Wind Speed Time-Series using Machine Learning
    Veronesi, F.
    Grassi, S.
    WINDEUROPE SUMMIT 2016, 2016, 749
  • [32] Forecasting of Wind Speed by Using Three Different Techniques of Prediction Models
    Verma M.
    Ghritlahre H.K.
    Annals of Data Science, 2023, 10 (03) : 679 - 711
  • [33] New Methodology for the Generation of Hourly Wind Speed Data Applied to the Optimization of Stand-Alone Systems
    Dufo-Lopez, Rodolfo
    Bernal-Agustin, Jose L.
    2011 2ND INTERNATIONAL CONFERENCE ON ADVANCES IN ENERGY ENGINEERING (ICAEE), 2012, 14 : 1973 - 1978
  • [34] Optimal Generation Research of Wind Turbine Based on Power Decoupling Near Rated Wind Speed
    Lou Y.
    Cai X.
    Ye H.
    He L.
    Dianwang Jishu/Power System Technology, 2019, 43 (03): : 879 - 886
  • [35] SVR-based wind speed estimation for power control of wind energy generation system
    Abo-Khalil, Ahmed G.
    Lee, Dong-Choon
    2007 POWER CONVERSION CONFERENCE - NAGOYA, VOLS 1-3, 2007, : 1387 - +
  • [36] Long-Term Wind Speed Variations for Three Midwestern US Cities
    Abhishek, A.
    Lee, Joo-Youp
    Keener, Tim C.
    Yang, Y. Jeffery
    JOURNAL OF THE AIR & WASTE MANAGEMENT ASSOCIATION, 2010, 60 (09) : 1057 - 1064
  • [37] Application of Wavelet and Neural Network Models for Wind Speed and Power Generation Forecasting in a Brazilian Experimental Wind Park
    de Aquino, Ronaldo R. B.
    Lira, Milde M. S.
    de Oliveira, Josinaldo B.
    Carvalho, Manoel A., Jr.
    Neto, Otoni N.
    de Almeida, Givanildo J.
    IJCNN: 2009 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS, VOLS 1- 6, 2009, : 1479 - +
  • [38] Simulation method of semi-physical wind power generation based on combined wind speed and tip speed ratio
    Liu, Xin
    Zhao, Qijie
    Lu, Jianxia
    2020 CHINESE AUTOMATION CONGRESS (CAC 2020), 2020, : 1805 - 1809
  • [39] Wind speed scenario generation based on dependency structure analysis
    Borujeni, Masoud Salehi
    Foroud, Asghar Akbari
    Dideban, Abbas
    JOURNAL OF WIND ENGINEERING AND INDUSTRIAL AERODYNAMICS, 2018, 172 : 453 - 465
  • [40] Wind speed and power forecasting based on spatial correlation models
    Alexiadis, MC
    Dokopoulos, PS
    Sahsamanoglou, HS
    IEEE TRANSACTIONS ON ENERGY CONVERSION, 1999, 14 (03) : 836 - 842