### Abstract

This paper presents possibility of voltage deviations based on Fuzzy-neuro Autoregressive model. Under the situation that deregulation and the liberalization advance to, it becomes difficult to predict an electricity demand precisely. Furthermore, it is concerned about voltage deviation due to increase of electricity demand deviation. Therefore, in this paper, the power demand of next day is predicted using neural network from data observed constant periods in the past. In the next, the uncertainly of predicted power demand is showed using fuzzy model. Therefore, it enables to predict not only one power demand but also both upper and lower appearance probability by this proposal method. Then, the optimal power flow calculation is solved to predict the voltage deviation possibility. Moreover, in this paper voltage setting is done by arranging the voltage control equipment based on the requested voltage possibility forecast value.

Original language | English |
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Title of host publication | Proceedings of the Eighth IASTED International Conference on Intelligent Systems and Control, ISC 2005 |

Editors | M.H. Hamza |

Pages | 222-227 |

Number of pages | 6 |

Publication status | Published - 2005 |

Event | Eighth IASTED International Conference on Intelligent Systems and Control, ISC 2005 - Cambridge, MA Duration: 2005 Oct 31 → 2005 Nov 2 |

### Other

Other | Eighth IASTED International Conference on Intelligent Systems and Control, ISC 2005 |
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City | Cambridge, MA |

Period | 05/10/31 → 05/11/2 |

### Fingerprint

### Keywords

- Autoregressive model
- Fuzzy
- Neural network
- Optimal power flow
- Possibility forecasting
- Voltage deviation

### ASJC Scopus subject areas

- Engineering(all)

### Cite this

*Proceedings of the Eighth IASTED International Conference on Intelligent Systems and Control, ISC 2005*(pp. 222-227)

**Possibility forecasting of voltage deviations based on Fuzzy-neuro Autoregressive model.** / Utsumi, Tetsuya; Sugimoto, Junjiro; Yokoyama, Ryuichi; Niimura, Takahide.

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

*Proceedings of the Eighth IASTED International Conference on Intelligent Systems and Control, ISC 2005.*pp. 222-227, Eighth IASTED International Conference on Intelligent Systems and Control, ISC 2005, Cambridge, MA, 05/10/31.

}

TY - GEN

T1 - Possibility forecasting of voltage deviations based on Fuzzy-neuro Autoregressive model

AU - Utsumi, Tetsuya

AU - Sugimoto, Junjiro

AU - Yokoyama, Ryuichi

AU - Niimura, Takahide

PY - 2005

Y1 - 2005

N2 - This paper presents possibility of voltage deviations based on Fuzzy-neuro Autoregressive model. Under the situation that deregulation and the liberalization advance to, it becomes difficult to predict an electricity demand precisely. Furthermore, it is concerned about voltage deviation due to increase of electricity demand deviation. Therefore, in this paper, the power demand of next day is predicted using neural network from data observed constant periods in the past. In the next, the uncertainly of predicted power demand is showed using fuzzy model. Therefore, it enables to predict not only one power demand but also both upper and lower appearance probability by this proposal method. Then, the optimal power flow calculation is solved to predict the voltage deviation possibility. Moreover, in this paper voltage setting is done by arranging the voltage control equipment based on the requested voltage possibility forecast value.

AB - This paper presents possibility of voltage deviations based on Fuzzy-neuro Autoregressive model. Under the situation that deregulation and the liberalization advance to, it becomes difficult to predict an electricity demand precisely. Furthermore, it is concerned about voltage deviation due to increase of electricity demand deviation. Therefore, in this paper, the power demand of next day is predicted using neural network from data observed constant periods in the past. In the next, the uncertainly of predicted power demand is showed using fuzzy model. Therefore, it enables to predict not only one power demand but also both upper and lower appearance probability by this proposal method. Then, the optimal power flow calculation is solved to predict the voltage deviation possibility. Moreover, in this paper voltage setting is done by arranging the voltage control equipment based on the requested voltage possibility forecast value.

KW - Autoregressive model

KW - Fuzzy

KW - Neural network

KW - Optimal power flow

KW - Possibility forecasting

KW - Voltage deviation

UR - http://www.scopus.com/inward/record.url?scp=33244473472&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=33244473472&partnerID=8YFLogxK

M3 - Conference contribution

AN - SCOPUS:33244473472

SN - 0889865191

SP - 222

EP - 227

BT - Proceedings of the Eighth IASTED International Conference on Intelligent Systems and Control, ISC 2005

A2 - Hamza, M.H.

ER -