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Creators/Authors contains: "Kosut, O"

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  1. ; ; ; ; ; (, PMLR)
    Accepted Manuscript Full Text Available
  2. ; ; (, IEEE International Symposium on Information Theory (ISIT))
    null (Ed.)
    Accepted Manuscript Full Text Available
  3. ; ; (, 2021 IEEE Power & Energy Society General Meeting (PESGM))
    null (Ed.)
    A framework for the generation of synthetic time-series transmission-level load data is presented. Conditional generative adversarial networks are used to learn the patterns of a real dataset of hourly-sampled week-long load profiles and generate unique synthetic profiles on demand, based on the season and type of load required. Extensive testing of the generative model is performed to verify that the synthetic data fully captures the characteristics of real loads and that it can be used for downstream power system and/or machine learning applications. 
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    Accepted Manuscript Full Text Available