IDEAS home Printed from https://ideas.repec.org/a/eee/renene/v102y2017ipbp390-405.html
   My bibliography  Save this article

A unified high-resolution wind and solar dataset from a rapidly updating numerical weather prediction model

Author

Listed:
  • James, Eric P.
  • Benjamin, Stanley G.
  • Marquis, Melinda

Abstract

A new gridded dataset for wind and solar resource estimation over the contiguous United States has been derived from hourly updated 1-h forecasts from the National Oceanic and Atmospheric Administration High-Resolution Rapid Refresh (HRRR) 3-km model composited over a three-year period (approximately 22 000 forecast model runs). The unique dataset features hourly data assimilation, and provides physically consistent wind and solar estimates for the renewable energy industry. The wind resource dataset shows strong similarity to that previously provided by a Department of Energy-funded study, and it includes estimates in southern Canada and northern Mexico. The solar resource dataset represents an initial step towards application-specific fields such as global horizontal and direct normal irradiance. This combined dataset will continue to be augmented with new forecast data from the advanced HRRR atmospheric/land-surface model.

Suggested Citation

  • James, Eric P. & Benjamin, Stanley G. & Marquis, Melinda, 2017. "A unified high-resolution wind and solar dataset from a rapidly updating numerical weather prediction model," Renewable Energy, Elsevier, vol. 102(PB), pages 390-405.
    • Handle: RePEc:eee:renene:v:102:y:2017:i:pb:p:390-405
    DOI: 10.1016/j.renene.2016.10.059
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0960148116309363
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.renene.2016.10.059?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Draxl, Caroline & Clifton, Andrew & Hodge, Bri-Mathias & McCaa, Jim, 2015. "The Wind Integration National Dataset (WIND) Toolkit," Applied Energy, Elsevier, vol. 151(C), pages 355-366.
    2. Carta, José A. & Velázquez, Sergio & Cabrera, Pedro, 2013. "A review of measure-correlate-predict (MCP) methods used to estimate long-term wind characteristics at a target site," Renewable and Sustainable Energy Reviews, Elsevier, vol. 27(C), pages 362-400.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Pearre, Nathaniel S. & Swan, Lukas G., 2018. "Spatial and geographic heterogeneity of wind turbine farms for temporally decoupled power output," Energy, Elsevier, vol. 145(C), pages 417-429.
    2. Ren, Zhengru & Verma, Amrit Shankar & Li, Ye & Teuwen, Julie J.E. & Jiang, Zhiyu, 2021. "Offshore wind turbine operations and maintenance: A state-of-the-art review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 144(C).
    3. Nguyen Gia Minh Thao & Kenko Uchida, 2018. "An Improved Interval Fuzzy Modeling Method: Applications to the Estimation of Photovoltaic/Wind/Battery Power in Renewable Energy Systems," Energies, MDPI, vol. 11(3), pages 1-26, February.
    4. Ouyang, Tinghui & Zha, Xiaoming & Qin, Liang & He, Yusen & Tang, Zhenhao, 2019. "Prediction of wind power ramp events based on residual correction," Renewable Energy, Elsevier, vol. 136(C), pages 781-792.
    5. Hodge, Bri-Mathias & Brancucci Martinez-Anido, Carlo & Wang, Qin & Chartan, Erol & Florita, Anthony & Kiviluoma, Juha, 2018. "The combined value of wind and solar power forecasting improvements and electricity storage," Applied Energy, Elsevier, vol. 214(C), pages 1-15.
    6. Gil-García, Isabel C. & Ramos-Escudero, Adela & García-Cascales, M.S. & Dagher, Habib & Molina-García, A., 2022. "Fuzzy GIS-based MCDM solution for the optimal offshore wind site selection: The Gulf of Maine case," Renewable Energy, Elsevier, vol. 183(C), pages 130-147.
    7. deCastro, M. & Salvador, S. & Gómez-Gesteira, M. & Costoya, X. & Carvalho, D. & Sanz-Larruga, F.J. & Gimeno, L., 2019. "Europe, China and the United States: Three different approaches to the development of offshore wind energy," Renewable and Sustainable Energy Reviews, Elsevier, vol. 109(C), pages 55-70.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Zhang, Jie & Draxl, Caroline & Hopson, Thomas & Monache, Luca Delle & Vanvyve, Emilie & Hodge, Bri-Mathias, 2015. "Comparison of numerical weather prediction based deterministic and probabilistic wind resource assessment methods," Applied Energy, Elsevier, vol. 156(C), pages 528-541.
    2. Munir Ali Elfarra & Mustafa Kaya, 2018. "Comparison of Optimum Spline-Based Probability Density Functions to Parametric Distributions for the Wind Speed Data in Terms of Annual Energy Production," Energies, MDPI, vol. 11(11), pages 1-15, November.
    3. Zimmerman, Ryan & Panda, Anurag & Bulović, Vladimir, 2020. "Techno-economic assessment and deployment strategies for vertically-mounted photovoltaic panels," Applied Energy, Elsevier, vol. 276(C).
    4. McManamay, Ryan A. & DeRolph, Christopher R. & Surendran-Nair, Sujithkumar & Allen-Dumas, Melissa, 2019. "Spatially explicit land-energy-water future scenarios for cities: Guiding infrastructure transitions for urban sustainability," Renewable and Sustainable Energy Reviews, Elsevier, vol. 112(C), pages 880-900.
    5. Koo, Junmo & Han, Gwon Deok & Choi, Hyung Jong & Shim, Joon Hyung, 2015. "Wind-speed prediction and analysis based on geological and distance variables using an artificial neural network: A case study in South Korea," Energy, Elsevier, vol. 93(P2), pages 1296-1302.
    6. Tanner, Sophia & Burnett, Wesley & Maguire, Karen & Winikoff, Justin, 2024. "Blown Away: The Influence of Wind Farms on Agricultural Land Values," 2024 Annual Meeting, July 28-30, New Orleans, LA 343970, Agricultural and Applied Economics Association.
    7. Ritter, Matthias & Shen, Zhiwei & López Cabrera, Brenda & Odening, Martin & Deckert, Lars, 2015. "Designing an index for assessing wind energy potential," Renewable Energy, Elsevier, vol. 83(C), pages 416-424.
    8. He, J.Y. & Chan, P.W. & Li, Q.S. & Huang, Tao & Yim, Steve Hung Lam, 2024. "Assessment of urban wind energy resource in Hong Kong based on multi-instrument observations," Renewable and Sustainable Energy Reviews, Elsevier, vol. 191(C).
    9. Denholm, Paul & Nunemaker, Jacob & Gagnon, Pieter & Cole, Wesley, 2020. "The potential for battery energy storage to provide peaking capacity in the United States," Renewable Energy, Elsevier, vol. 151(C), pages 1269-1277.
    10. Kresning, Boma & Hashemi, M. Reza & Shirvani, Amin & Hashemi, Javad, 2024. "Uncertainty of extreme wind and wave loads for marine renewable energy farms in hurricane-prone regions," Renewable Energy, Elsevier, vol. 220(C).
    11. Howard, B. & Waite, M. & Modi, V., 2017. "Current and near-term GHG emissions factors from electricity production for New York State and New York City," Applied Energy, Elsevier, vol. 187(C), pages 255-271.
    12. Mike Ludkovski & Glen Swindle & Eric Grannan, 2022. "Large Scale Probabilistic Simulation of Renewables Production," Papers 2205.04736, arXiv.org.
    13. Soukissian, Takvor H. & Papadopoulos, Anastasios, 2015. "Effects of different wind data sources in offshore wind power assessment," Renewable Energy, Elsevier, vol. 77(C), pages 101-114.
    14. Olaofe, Z.O., 2019. "Quantification of the near-surface wind conditions of the African coast: A comparative approach (satellite, NCEP CFSR and WRF-based)," Energy, Elsevier, vol. 189(C).
    15. Vinel, Alexander & Mortaz, Ebrahim, 2019. "Optimal pooling of renewable energy sources with a risk-averse approach: Implications for US energy portfolio," Energy Policy, Elsevier, vol. 132(C), pages 928-939.
    16. Yuan, Ran & Wang, Bo & Mao, Zhixin & Watada, Junzo, 2021. "Multi-objective wind power scenario forecasting based on PG-GAN," Energy, Elsevier, vol. 226(C).
    17. Cheng-Dar Yue & Che-Chih Liu & Chien-Cheng Tu & Ta-Hui Lin, 2019. "Prediction of Power Generation by Offshore Wind Farms Using Multiple Data Sources," Energies, MDPI, vol. 12(4), pages 1-24, February.
    18. Harrison-Atlas, Dylan & Murphy, Caitlin & Schleifer, Anna & Grue, Nicholas, 2022. "Temporal complementarity and value of wind-PV hybrid systems across the United States," Renewable Energy, Elsevier, vol. 201(P1), pages 111-123.
    19. Sun, Mucun & Feng, Cong & Zhang, Jie, 2020. "Multi-distribution ensemble probabilistic wind power forecasting," Renewable Energy, Elsevier, vol. 148(C), pages 135-149.
    20. Boffino, Luigi & Conejo, Antonio J. & Sioshansi, Ramteen & Oggioni, Giorgia, 2019. "A two-stage stochastic optimization planning framework to decarbonize deeply electric power systems," Energy Economics, Elsevier, vol. 84(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:renene:v:102:y:2017:i:pb:p:390-405. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/renewable-energy .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.