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Applications of Dimensionality Reduction to the Diagnosis of Energy Systems

In: Nonlinear Dimensionality Reduction Techniques

Author

Listed:
  • Sylvain Lespinats

    (Grenoble Alpes University, National Institute of Solar Energy (INES))

  • Benoit Colange

    (Grenoble Alpes University, National Institute of Solar Energy (INES))

  • Denys Dutykh

    (Université Grenoble Alpes, Université Savoie Mont Blanc, Campus Scientifique, CNRS - LAMA UMR 5127)

Abstract

This Chapter presents a few examples of applications of dimensionality reduction for the analysis of data towards the diagnosis of energy systems. These systems encompass smart-buildings (Sect. 8.1), photovoltaic systems (Sect. 8.2) and batteries (Sect. 8.3). Diagnosis aims at identifying the occurrence of faults in a system. These faults are characterized by their signatures, that is their effects on the system and the monitored variables. The discriminability between the signatures of different faults is a necessary condition for the possibility of diagnostic. In that regard, Dimensionality Reduction (DR) may allow to compare different signatures, provided for instance by I–V curves for photovoltaic systems and by Power Spectral Density of acoustic signals for Li-ion batteries.

Suggested Citation

  • Sylvain Lespinats & Benoit Colange & Denys Dutykh, 2022. "Applications of Dimensionality Reduction to the Diagnosis of Energy Systems," Springer Books, in: Nonlinear Dimensionality Reduction Techniques, chapter 0, pages 177-192, Springer.
    • Handle: RePEc:spr:sprchp:978-3-030-81026-9_8
    DOI: 10.1007/978-3-030-81026-9_8
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