Short-term prediction of power fluctuations in photovoltaic systems using chaos theory

Kohei Shibata; Akiko Takahashi; Jim Imai; Shigeyuki Funabiki

doi:10.1109/PESMG.2013.6672364

Short-term prediction of power fluctuations in photovoltaic systems using chaos theory

Kohei Shibata, Akiko Takahashi, Jim Imai, Shigeyuki Funabiki

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

8 Citations (Scopus)

Abstract

This paper proposes a novel short-term prediction method for power fluctuations in a photovoltaic (PV) system based on chaos theory. A data vector composed of n data points is embedded in an n-dimensional vector space by Takens' embedding theorem. The time delay is determined at 300 s by autocorrelation analysis, and the embedding dimension is determined to be 5 by the Grassberger-Procaccia algorithm. Next, the PV power in the near feature is predicted by using the local fuzzy reconstruction method. A simulation of the smoothing control of power in a PV system with an energy storage device consisting of electric double layer capacitors (EDLCs) is carried out. The largest reduction in the EDLCs' capacity is 3.81 kJ, which can be achieved on a clear day.

Original language	English
Title of host publication	2013 IEEE Power and Energy Society General Meeting, PES 2013
DOIs	https://doi.org/10.1109/PESMG.2013.6672364
Publication status	Published - Dec 1 2013
Event	2013 IEEE Power and Energy Society General Meeting, PES 2013 - Vancouver, BC, Canada Duration: Jul 21 2013 → Jul 25 2013

Publication series

Name	IEEE Power and Energy Society General Meeting
ISSN (Print)	1944-9925
ISSN (Electronic)	1944-9933

Other

Other	2013 IEEE Power and Energy Society General Meeting, PES 2013
Country/Territory	Canada
City	Vancouver, BC
Period	7/21/13 → 7/25/13

Keywords

chaos
power smoothing
prediction method
solar power generation
supercapacitors

ASJC Scopus subject areas

Energy Engineering and Power Technology
Nuclear Energy and Engineering
Renewable Energy, Sustainability and the Environment
Electrical and Electronic Engineering

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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10.1109/PESMG.2013.6672364

Cite this

Shibata, K, Takahashi, A, Imai, J & Funabiki, S 2013, Short-term prediction of power fluctuations in photovoltaic systems using chaos theory. in 2013 IEEE Power and Energy Society General Meeting, PES 2013., 6672364, IEEE Power and Energy Society General Meeting, 2013 IEEE Power and Energy Society General Meeting, PES 2013, Vancouver, BC, Canada, 7/21/13. https://doi.org/10.1109/PESMG.2013.6672364

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title = "Short-term prediction of power fluctuations in photovoltaic systems using chaos theory",

abstract = "This paper proposes a novel short-term prediction method for power fluctuations in a photovoltaic (PV) system based on chaos theory. A data vector composed of n data points is embedded in an n-dimensional vector space by Takens' embedding theorem. The time delay is determined at 300 s by autocorrelation analysis, and the embedding dimension is determined to be 5 by the Grassberger-Procaccia algorithm. Next, the PV power in the near feature is predicted by using the local fuzzy reconstruction method. A simulation of the smoothing control of power in a PV system with an energy storage device consisting of electric double layer capacitors (EDLCs) is carried out. The largest reduction in the EDLCs' capacity is 3.81 kJ, which can be achieved on a clear day.",

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author = "Kohei Shibata and Akiko Takahashi and Jim Imai and Shigeyuki Funabiki",

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AU - Shibata, Kohei

AU - Takahashi, Akiko

AU - Imai, Jim

AU - Funabiki, Shigeyuki

PY - 2013/12/1

Y1 - 2013/12/1

N2 - This paper proposes a novel short-term prediction method for power fluctuations in a photovoltaic (PV) system based on chaos theory. A data vector composed of n data points is embedded in an n-dimensional vector space by Takens' embedding theorem. The time delay is determined at 300 s by autocorrelation analysis, and the embedding dimension is determined to be 5 by the Grassberger-Procaccia algorithm. Next, the PV power in the near feature is predicted by using the local fuzzy reconstruction method. A simulation of the smoothing control of power in a PV system with an energy storage device consisting of electric double layer capacitors (EDLCs) is carried out. The largest reduction in the EDLCs' capacity is 3.81 kJ, which can be achieved on a clear day.

AB - This paper proposes a novel short-term prediction method for power fluctuations in a photovoltaic (PV) system based on chaos theory. A data vector composed of n data points is embedded in an n-dimensional vector space by Takens' embedding theorem. The time delay is determined at 300 s by autocorrelation analysis, and the embedding dimension is determined to be 5 by the Grassberger-Procaccia algorithm. Next, the PV power in the near feature is predicted by using the local fuzzy reconstruction method. A simulation of the smoothing control of power in a PV system with an energy storage device consisting of electric double layer capacitors (EDLCs) is carried out. The largest reduction in the EDLCs' capacity is 3.81 kJ, which can be achieved on a clear day.

KW - chaos

KW - power smoothing

KW - prediction method

KW - solar power generation

KW - supercapacitors

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BT - 2013 IEEE Power and Energy Society General Meeting, PES 2013

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Y2 - 21 July 2013 through 25 July 2013

ER -

Short-term prediction of power fluctuations in photovoltaic systems using chaos theory

Abstract

Publication series

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Keywords

ASJC Scopus subject areas

UN SDGs

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