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Spatial–Temporal Estimation and Analysis of Japan Onshore and Offshore Wind Energy Potential

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  • Rémi Delage

    (Department of Management Science and Technology, Graduate School of Engineering, Tohoku University, Sendai 980-8579, Japan)

  • Taichi Matsuoka

    (Department of Management Science and Technology, Graduate School of Engineering, Tohoku University, Sendai 980-8579, Japan)

  • Toshihiko Nakata

    (Department of Management Science and Technology, Graduate School of Engineering, Tohoku University, Sendai 980-8579, Japan)

Abstract

In the carbon-neutral scenarios fixed by most developed countries, wind and solar resources play a significant role due to their substantial potential. Their instability can be mitigated through smarter designs of energy systems, including sector coupling and cross-border interconnections, which require detailed information on the spatial and temporal evolution of these intermittent resources. The present study aims at estimating the spatial–temporal energy potential of wind in Japan based on meteorological weather data. These data allow to analyze the potential of resources sharing to reduce power generation’s lack and excess, even in such an isolated country due to its variety of climate conditions and local energy demand. The correlation skewness is introduced as a measure of the sites’ uniqueness to identify important sites for the spatial distribution of capacity toward the efficient stabilization of supply at a national scale in a model-free fashion.

Suggested Citation

  • Rémi Delage & Taichi Matsuoka & Toshihiko Nakata, 2021. "Spatial–Temporal Estimation and Analysis of Japan Onshore and Offshore Wind Energy Potential," Energies, MDPI, vol. 14(8), pages 1-12, April.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:8:p:2168-:d:535465
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    Cited by:

    1. Eugen Rusu, 2022. "Marine Renewable Energy: An Important Direction in Taking the Green Road towards a Low Carbon Future," Energies, MDPI, vol. 15(15), pages 1-3, July.
    2. Kusumoto, Yoshiki & Delage, Rémi & Nakata, Toshihiko, 2024. "Machine learning application for estimating electricity demand by municipality," Energy, Elsevier, vol. 296(C).
    3. Rémi Delage & Toshihiko Nakata, 2022. "Multivariate Empirical Mode Decomposition and Recurrence Quantification for the Multiscale, Spatiotemporal Analysis of Electricity Demand—A Case Study of Japan," Energies, MDPI, vol. 15(17), pages 1-17, August.

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