A compressive sensing framework for the analysis of solar Photo-Voltaic power

Raksha Ramakrishna, Anna Scaglione

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

4 Scopus citations

Abstract

In this paper we propose a new stochastic model for solar Photo-Voltaic (PV) power that explicitly models the effect of cloud coverage as an attenuation of the two components that make up the deterministic solar irradiation pattern. Relying on compressive sensing methods we are able to fit a set of solar PV power data from California with the components of this stochastic model and extract the parameters of such a process thus effectively capturing the variability of solar power production. One can leverage the rich information coming from this parametric model for stochastic optimization and decision making.

Original languageEnglish (US)
Title of host publicationConference Record of the 50th Asilomar Conference on Signals, Systems and Computers, ACSSC 2016
PublisherIEEE Computer Society
Pages308-312
Number of pages5
ISBN (Electronic)9781538639542
DOIs
StatePublished - Mar 1 2017
Event50th Asilomar Conference on Signals, Systems and Computers, ACSSC 2016 - Pacific Grove, United States
Duration: Nov 6 2016Nov 9 2016

Other

Other50th Asilomar Conference on Signals, Systems and Computers, ACSSC 2016
CountryUnited States
CityPacific Grove
Period11/6/1611/9/16

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ASJC Scopus subject areas

  • Signal Processing
  • Computer Networks and Communications

Cite this

Ramakrishna, R., & Scaglione, A. (2017). A compressive sensing framework for the analysis of solar Photo-Voltaic power. In Conference Record of the 50th Asilomar Conference on Signals, Systems and Computers, ACSSC 2016 (pp. 308-312). [7869048] IEEE Computer Society. https://doi.org/10.1109/ACSSC.2016.7869048