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Apriori and K-Means algorithms of machine learning for spatio-temporal solar generation balancing

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  • Yürüşen, Nurseda Y.
  • Uzunoğlu, Bahri
  • Talayero, Ana P.
  • Estopiñán, Andrés Llombart

Abstract

The number of grid-connected large-scale solar photovoltaic (PV) power plants has increased significantly in the last 10 years, which results in high PV power penetration into the grid. Especially for the wide-area spatially distributed countries, power ramp in one PV plant can be balanced with another PV power plant generation. This has been studied in the literature for short term horizons for high-frequency data. In this study, hourly simulation data are analysed by Kendall's correlation coefficient, unsupervised and rule-based machine learning algorithms for spatio-temporal operational balancing constraints. Association rules generated by using the Apriori algorithm provide power ramp direction maps for Spatio-Temporal analysis. The K-means clustering (based on the Hartigan-Wong algorithm) is used for unsupervised learning application for the spatio-temporal relations for solar PV ramp zones.

Suggested Citation

  • Yürüşen, Nurseda Y. & Uzunoğlu, Bahri & Talayero, Ana P. & Estopiñán, Andrés Llombart, 2021. "Apriori and K-Means algorithms of machine learning for spatio-temporal solar generation balancing," Renewable Energy, Elsevier, vol. 175(C), pages 702-717.
    • Handle: RePEc:eee:renene:v:175:y:2021:i:c:p:702-717
    DOI: 10.1016/j.renene.2021.04.098
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