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Downscaled energy demand projection at the local level using the Iterative Proportional Fitting procedure

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  • Ahn, Young-Hwan
  • Woo, Jung-Hun
  • Wagner, Fabian
  • Yoo, Seung Jick

Abstract

In this paper we forecast energy demand at the local level using a new two-dimensional downscaling methodology that respects regional and subnational variation and, as well as consistency at the aggregate level. This methodology combines the one-dimensional statistical downscaling and the Iterative Proportional Fitting (IPF) procedure. Previous projection studies based on downscaling methods have focused on allocating one-dimensional quantity indicators such as GDP, population, and emissions, into geographically or administratively lower levels. In case of energy demand projections, however, downscaling involves not only regional allocation of energy demand quantities but also energy mixes of the demands in the regions. Our novel two-dimensional downscaling methodology allows us to downscale national energy projections in both demand quantities and their energy mix to regional levels by combining the statistical downscaling and the IPF procedure. We illustrate the methodology by deriving energy demand projections for provinces and metropolitan cities in Korea from national scenarios. In addition, we provide two more simple applications of the IPF to show utilization potentials of the IPF in energy demand forecasting and statistics.

Suggested Citation

  • Ahn, Young-Hwan & Woo, Jung-Hun & Wagner, Fabian & Yoo, Seung Jick, 2019. "Downscaled energy demand projection at the local level using the Iterative Proportional Fitting procedure," Applied Energy, Elsevier, vol. 238(C), pages 384-400.
    • Handle: RePEc:eee:appene:v:238:y:2019:i:c:p:384-400
    DOI: 10.1016/j.apenergy.2019.01.051
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    References listed on IDEAS

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    Cited by:

    1. Angreine Kewo & Pinrolinvic D. K. Manembu & Per Sieverts Nielsen, 2020. "Synthesising Residential Electricity Load Profiles at the City Level Using a Weighted Proportion (Wepro) Model," Energies, MDPI, vol. 13(14), pages 1-28, July.
    2. McGookin, Connor & Ó Gallachóir, Brian & Byrne, Edmond, 2021. "An innovative approach for estimating energy demand and supply to inform local energy transitions," Energy, Elsevier, vol. 229(C).

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