Approximate String Matching of Power System Substation Names

Guo, Shi; Jiang, Xiaoqian; Thornton, Troy; Saunders, D. Curtis

doi:10.1109/peci.2019.8698905

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Managing Energy Systems Data1

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2021

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“…In some cases, ML can facilitate the data cleaning process. For instance, when merging different data records with potentially inconsistent data labeling schemes (particularly at scale), it may be necessary to automatically infer which records should be merged (129). As a result, projects such as the Public Utility Data Liberation Project (130) have used clustering and other ML techniques to automatically match records across different energy systems datasets.…”

Section: Managing Energy Systems Datamentioning

confidence: 99%

Machine Learning for Sustainable Energy Systems

Donti

Kolter

2021

Annu. Rev. Environ. Resour.

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In recent years, machine learning has proven to be a powerful tool for deriving insights from data. In this review, we describe ways in which machine learning has been leveraged to facilitate the development and operation of sustainable energy systems. We first provide a taxonomy of machine learning paradigms and techniques, along with a discussion of their strengths and limitations. We then provide an overview of existing research using machine learning for sustainable energy production, delivery, and storage. Finally, we identify gaps in this literature, propose future research directions, and discuss important considerations for deployment. Expected final online publication date for the Annual Review of Environment and Resources, Volume 46 is October 2021. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

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Section: Managing Energy Systems Datamentioning

confidence: 99%