IDEAS home Printed from https://ideas.repec.org/p/isu/genres/10795.html
   My bibliography  Save this paper

Statistically Adjusted Engineering (Sae) Models of End Use Load Curves

Author

Listed:
  • Herriges, Joseph A.
  • Train, K.
  • Windle, R. J.

Abstract

Currently Unavailable.

Suggested Citation

  • Herriges, Joseph A. & Train, K. & Windle, R. J., 1985. "Statistically Adjusted Engineering (Sae) Models of End Use Load Curves," Staff General Research Papers Archive 10795, Iowa State University, Department of Economics.
    • Handle: RePEc:isu:genres:10795
    as

    Download full text from publisher

    To our knowledge, this item is not available for download. To find whether it is available, there are three options:
    1. Check below whether another version of this item is available online.
    2. Check on the provider's web page whether it is in fact available.
    3. Perform a search for a similarly titled item that would be available.

    Other versions of this item:

    Citations

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


    Cited by:

    1. Horowitz, Marvin J. & Bertoldi, Paolo, 2015. "A harmonized calculation model for transforming EU bottom-up energy efficiency indicators into empirical estimates of policy impacts," Energy Economics, Elsevier, vol. 51(C), pages 135-148.
    2. DeBenedictis, A. & Hoff, T.E. & Price, S. & Woo, C.K., 2010. "Statistically adjusted engineering (SAE) modeling of metered roof-top photovoltaic (PV) output: California evidence," Energy, Elsevier, vol. 35(10), pages 4178-4183.
    3. Grandjean, A. & Adnot, J. & Binet, G., 2012. "A review and an analysis of the residential electric load curve models," Renewable and Sustainable Energy Reviews, Elsevier, vol. 16(9), pages 6539-6565.
    4. Mehrnaz Anvari & Elisavet Proedrou & Benjamin Schäfer & Christian Beck & Holger Kantz & Marc Timme, 2022. "Data-driven load profiles and the dynamics of residential electricity consumption," Nature Communications, Nature, vol. 13(1), pages 1-12, December.
    5. Többen, Johannes & Schröder, Thomas, 2018. "A maximum entropy approach to the estimation of spatially and sectorally disaggregated electricity load curves," Applied Energy, Elsevier, vol. 225(C), pages 797-813.
    6. Zúñiga, K.V. & Castilla, I. & Aguilar, R.M., 2014. "Using fuzzy logic to model the behavior of residential electrical utility customers," Applied Energy, Elsevier, vol. 115(C), pages 384-393.
    7. Dong, Ming & Shi, Jian & Shi, Qingxin, 2020. "Multi-year long-term load forecast for area distribution feeders based on selective sequence learning," Energy, Elsevier, vol. 206(C).

    More about this item

    Statistics

    Access and download statistics

    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:isu:genres:10795. 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.

    We have no bibliographic references for this item. You can help adding them by using 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: Curtis Balmer (email available below). General contact details of provider: https://edirc.repec.org/data/deiasus.html .

    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.