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

被引:2
|
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
  • [41] Continuous wind speed models based on stochastic differential equations
    Zarate-Minano, Rafael
    Anghel, Marian
    Milano, Federico
    APPLIED ENERGY, 2013, 104 : 42 - 49
  • [42] Hourly Average Wind Speed Simulation and Forecast Based on ARMA Model in Jeju Island, Korea
    Do, Duy-Phuong N.
    Lee, Yeonchan
    Choi, Jaeseok
    JOURNAL OF ELECTRICAL ENGINEERING & TECHNOLOGY, 2016, 11 (06) : 1548 - 1555
  • [43] Wind speed prediction based on simple meteorological data using artificial neural network
    Ghanbarzadeh, A.
    Noghrehabadi, A. R.
    Behrang, M. A.
    Assareh, E.
    2009 7TH IEEE INTERNATIONAL CONFERENCE ON INDUSTRIAL INFORMATICS, VOLS 1 AND 2, 2009, : 664 - +
  • [44] Vine Copula-Based Multivariate Distribution of Rainfall Intensity, Wind Speed, and Wind Direction for Optimizing Qatari Meteorological Stations
    Qasem, Hassan
    Joergensen, Niels-Erik
    Rahman, Ataur
    Samman, Husam Abdullah
    Al Malki, Sharouq
    Al Ansari, Abdulrahman Saleh
    WATER, 2024, 16 (09)
  • [45] Comparative analysis of the wind characteristics of three landfall typhoons based on stationary and nonstationary wind models
    Quan, Yong
    Fu, Guo Qiang
    Huang, Zi Feng
    Gu, Ming
    WIND AND STRUCTURES, 2020, 31 (03) : 269 - 285
  • [46] Research and application of a Model selection forecasting system for wind speed and theoretical power generation in wind farms based on classification and wind conversion
    Huang, Xiaojia
    Wang, Chen
    Zhang, Shenghui
    ENERGY, 2024, 293
  • [47] Comparison of LSSVR, M5RT, NF-GP, and NF-SC Models for Predictions of Hourly Wind Speed and Wind Power Based on Cross-Validation
    Adnan, Rana Muhammad
    Liang, Zhongmin
    Yuan, Xiaohui
    Kisi, Ozgur
    Akhlaq, Muhammad
    Li, Binquan
    ENERGIES, 2019, 12 (02)
  • [48] Short-term power output forecasting of hourly operation in power plant based on climate factors and effects of wind direction and wind speed
    Dadkhah, Mojtaba
    Rezaee, Mustafa Jahangoshai
    Chavoshi, Ahmad Zare
    ENERGY, 2018, 148 : 775 - 788
  • [49] Estimation of the Monthly Based Hourly Wind Speed Characteristics and the Generated Power Characteristics for Developing Bidding Strategies in an Actual Wind Farm: A Case Study
    E. Akyuz
    D. Demiral
    C. Coskun
    Z. Oktay
    Arabian Journal for Science and Engineering, 2013, 38 : 263 - 275