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Statistically adjusted engineering (SAE) models of end-use load curves

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  • Train, Kenneth
  • Herriges, Joseph
  • Windle, Robert

Abstract

We develop and demonstrate models that combine engineering and statistical approaches to estimating customer-specific end-use load curves. Simulated end-use loads from engineering methods enter as explanatory variables in statistical models, and estimated parameters adjust the engineering loads on the basis of customers' observed loads. The resulting end-use loads, called statistically adjusted engineering (SAE) loads, depend on a variety of conditioning variables, including weather and the size and type of the customer's dwelling (which enter the engineering simulations) and the income and other characteristics of the household (which enter the statistical adjustment). Using data from a Los Angeles sample of households, several SAE models are estimated that differ in the flexibility that they allow in the adjustment of the engineering loads.

Suggested Citation

  • Train, Kenneth & Herriges, Joseph & Windle, Robert, 1985. "Statistically adjusted engineering (SAE) models of end-use load curves," Energy, Elsevier, vol. 10(10), pages 1103-1111.
    • Handle: RePEc:eee:energy:v:10:y:1985:i:10:p:1103-1111
    DOI: 10.1016/0360-5442(85)90025-8
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    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).

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