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

Raksha Ramakrishna; Anna Scaglione

doi:10.1109/ACSSC.2016.7869048

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

Raksha Ramakrishna, Anna Scaglione

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

8 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 language	English (US)
Title of host publication	Conference Record of the 50th Asilomar Conference on Signals, Systems and Computers, ACSSC 2016
Publisher	IEEE Computer Society
Pages	308-312
Number of pages	5
ISBN (Electronic)	9781538639542
DOIs	https://doi.org/10.1109/ACSSC.2016.7869048
State	Published - Mar 1 2017
Event	50th Asilomar Conference on Signals, Systems and Computers, ACSSC 2016 - Pacific Grove, United States Duration: Nov 6 2016 → Nov 9 2016

Other

Other	50th Asilomar Conference on Signals, Systems and Computers, ACSSC 2016
Country/Territory	United States
City	Pacific Grove
Period	11/6/16 → 11/9/16

ASJC Scopus subject areas

Signal Processing
Computer Networks and Communications

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10.1109/ACSSC.2016.7869048

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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., 7869048, IEEE Computer Society, pp. 308-312, 50th Asilomar Conference on Signals, Systems and Computers, ACSSC 2016, Pacific Grove, United States, 11/6/16. https://doi.org/10.1109/ACSSC.2016.7869048

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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.",

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N2 - 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.

AB - 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.

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Y2 - 6 November 2016 through 9 November 2016

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

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