Application of neural network to one-day-ahead 24 hours generating power forecasting for photovoltaic system

Atsushi Yona; Tomonobu Senjyu; Ahmed Yousuf Saber; Toshihisa Funabashi; Hideomi Sekine; Chul Hwan Kim

doi:10.1109/ISAP.2007.4441657

Application of neural network to one-day-ahead 24 hours generating power forecasting for photovoltaic system

Atsushi Yona
, Tomonobu Senjyu
, Ahmed Yousuf Saber
, Toshihisa Funabashi
, Hideomi Sekine
, Chul Hwan Kim

Department of Electronic and Electrical Engineering

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

110 Scopus citations

Abstract

In recent years, introduction of an alternative energy source such as solar energy is expected. However, insolation is not constant and output of photovoltaic (PV) system is influenced by meteorological conditions. In order to predict the power output for PV system as accurate as possible, it requires method of insolation estimation. In this paper, the authors take the insolation of each month into consideration, and confirm the validity of using neural network to predict one-day-ahead 24 hours insolation by computer simulations. The proposed method in this paper does not require complicated calculation and mathematical model with only meteorological data.

Original language	English
Title of host publication	2007 International Conference on Intelligent Systems Applications to Power Systems, ISAP
DOIs	https://doi.org/10.1109/ISAP.2007.4441657
State	Published - 2007

Publication series

Name	2007 International Conference on Intelligent Systems Applications to Power Systems, ISAP

Keywords

24 hours ahead forecasting
Insolation forecasting
Neural network
Power output for PV system

UN SDGs

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

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10.1109/ISAP.2007.4441657

Cite this

Yona, A., Senjyu, T., Saber, A. Y., Funabashi, T., Sekine, H., & Kim, C. H. (2007). Application of neural network to one-day-ahead 24 hours generating power forecasting for photovoltaic system. In 2007 International Conference on Intelligent Systems Applications to Power Systems, ISAP Article 4441657 (2007 International Conference on Intelligent Systems Applications to Power Systems, ISAP). https://doi.org/10.1109/ISAP.2007.4441657

@inproceedings{ad8e18504eb145fbaeb4d33b7d210943,

title = "Application of neural network to one-day-ahead 24 hours generating power forecasting for photovoltaic system",

abstract = "In recent years, introduction of an alternative energy source such as solar energy is expected. However, insolation is not constant and output of photovoltaic (PV) system is influenced by meteorological conditions. In order to predict the power output for PV system as accurate as possible, it requires method of insolation estimation. In this paper, the authors take the insolation of each month into consideration, and confirm the validity of using neural network to predict one-day-ahead 24 hours insolation by computer simulations. The proposed method in this paper does not require complicated calculation and mathematical model with only meteorological data.",

keywords = "24 hours ahead forecasting, Insolation forecasting, Neural network, Power output for PV system",

author = "Atsushi Yona and Tomonobu Senjyu and Saber, \{Ahmed Yousuf\} and Toshihisa Funabashi and Hideomi Sekine and Kim, \{Chul Hwan\}",

year = "2007",

doi = "10.1109/ISAP.2007.4441657",

language = "English",

isbn = "9860130868",

series = "2007 International Conference on Intelligent Systems Applications to Power Systems, ISAP",

booktitle = "2007 International Conference on Intelligent Systems Applications to Power Systems, ISAP",

}

Yona, A, Senjyu, T, Saber, AY, Funabashi, T, Sekine, H & Kim, CH 2007, Application of neural network to one-day-ahead 24 hours generating power forecasting for photovoltaic system. in 2007 International Conference on Intelligent Systems Applications to Power Systems, ISAP., 4441657, 2007 International Conference on Intelligent Systems Applications to Power Systems, ISAP. https://doi.org/10.1109/ISAP.2007.4441657

Application of neural network to one-day-ahead 24 hours generating power forecasting for photovoltaic system. / Yona, Atsushi; Senjyu, Tomonobu; Saber, Ahmed Yousuf et al.
2007 International Conference on Intelligent Systems Applications to Power Systems, ISAP. 2007. 4441657 (2007 International Conference on Intelligent Systems Applications to Power Systems, ISAP).

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

TY - GEN

T1 - Application of neural network to one-day-ahead 24 hours generating power forecasting for photovoltaic system

AU - Yona, Atsushi

AU - Senjyu, Tomonobu

AU - Saber, Ahmed Yousuf

AU - Funabashi, Toshihisa

AU - Sekine, Hideomi

AU - Kim, Chul Hwan

PY - 2007

Y1 - 2007

N2 - In recent years, introduction of an alternative energy source such as solar energy is expected. However, insolation is not constant and output of photovoltaic (PV) system is influenced by meteorological conditions. In order to predict the power output for PV system as accurate as possible, it requires method of insolation estimation. In this paper, the authors take the insolation of each month into consideration, and confirm the validity of using neural network to predict one-day-ahead 24 hours insolation by computer simulations. The proposed method in this paper does not require complicated calculation and mathematical model with only meteorological data.

AB - In recent years, introduction of an alternative energy source such as solar energy is expected. However, insolation is not constant and output of photovoltaic (PV) system is influenced by meteorological conditions. In order to predict the power output for PV system as accurate as possible, it requires method of insolation estimation. In this paper, the authors take the insolation of each month into consideration, and confirm the validity of using neural network to predict one-day-ahead 24 hours insolation by computer simulations. The proposed method in this paper does not require complicated calculation and mathematical model with only meteorological data.

KW - 24 hours ahead forecasting

KW - Insolation forecasting

KW - Neural network

KW - Power output for PV system

UR - https://www.scopus.com/pages/publications/50249170424

U2 - 10.1109/ISAP.2007.4441657

DO - 10.1109/ISAP.2007.4441657

M3 - Conference contribution

AN - SCOPUS:50249170424

SN - 9860130868

SN - 9789860130867

T3 - 2007 International Conference on Intelligent Systems Applications to Power Systems, ISAP

BT - 2007 International Conference on Intelligent Systems Applications to Power Systems, ISAP

ER -

Yona A, Senjyu T, Saber AY, Funabashi T, Sekine H, Kim CH. Application of neural network to one-day-ahead 24 hours generating power forecasting for photovoltaic system. In 2007 International Conference on Intelligent Systems Applications to Power Systems, ISAP. 2007. 4441657. (2007 International Conference on Intelligent Systems Applications to Power Systems, ISAP). doi: 10.1109/ISAP.2007.4441657

Application of neural network to one-day-ahead 24 hours generating power forecasting for photovoltaic system

Abstract

Publication series

Keywords

UN SDGs

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