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

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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. 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.
3. 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.
4. 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).
5. 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.
6. 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.
7. 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.

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