Big data and deep learning platform for terabyte-scale renewable datasets

Yang Weng, Abhishek Kumar, Muhammad B. Saleem, Baosen Zhang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations

Abstract

Many renewable resources cover diverse geographical areas and it is increasingly important to analyze large sets of data to understand their spatial and temporal behaviors. In this paper, we propose and demonstrate a data platform to efficiently manipulate and visualize data on the scale of terabytes. As the application of interest, we focus on visualization and forecasting of wind power over large geographic areas at various different spatial and temporal resolutions. In particular, we show how to balance the amount of data used and the need for computational efficiency in real-time applications. The main data set we use is the recently released terabyte wind dataset by NREL.

Original languageEnglish (US)
Title of host publication20th Power Systems Computation Conference, PSCC 2018
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Print)9781910963104
DOIs
StatePublished - Aug 20 2018
Event20th Power Systems Computation Conference, PSCC 2018 - Dublin, Ireland
Duration: Jun 11 2018Jun 15 2018

Other

Other20th Power Systems Computation Conference, PSCC 2018
CountryIreland
CityDublin
Period6/11/186/15/18

Keywords

  • Big data analytics
  • Cloud platform
  • Feature extraction
  • Forecasting
  • Renewable generation

ASJC Scopus subject areas

  • Energy Engineering and Power Technology
  • Computer Networks and Communications
  • Safety, Risk, Reliability and Quality

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  • Cite this

    Weng, Y., Kumar, A., Saleem, M. B., & Zhang, B. (2018). Big data and deep learning platform for terabyte-scale renewable datasets. In 20th Power Systems Computation Conference, PSCC 2018 [8442536] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.23919/PSCC.2018.8442536