Abstract
This paper describes a wind power forecasting method and its confidence interval estimation. Recently, flat control of wind power generators using various batteries has been required. In flat control, accurate wind power forecasts and their error confidence intervals are needed. In this paper, wind speed forecasts are calculated by regression models using Grid Point Value (GPV) weather forecasts. The forecasts are adjusted by fuzzy inference using the latest errors. The wind power forecasts are translated from the wind speed forecasts using two power curves. The power curves are selected or combined by fuzzy inference depending on the wind direction. The error confidence interval models are generated for each forecasting target time. Each confidence interval is combined by the other fuzzy inference. The proposed methods were applied to actual wind power generators, and it was found that the forecasting errors were smaller than in the conventional methods. Almost all of the forecasts can be within the error confidence intervals estimated by the proposed methods. The results show the effectiveness of the proposed methods.
| Original language | English |
|---|---|
| Pages (from-to) | 52-60 |
| Number of pages | 9 |
| Journal | Electrical Engineering in Japan (English translation of Denki Gakkai Ronbunshi) |
| Volume | 186 |
| Issue number | 2 |
| DOIs | |
| Publication status | Published - 2014 |
| Externally published | Yes |
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Keywords
- confidence interval
- forecasting
- fuzzy inference
- wind power generation
ASJC Scopus subject areas
- Electrical and Electronic Engineering
- Energy Engineering and Power Technology
Cite this
A wind power forecasting method and its confidence interval estimation. / Iizaka, Tatsuya; Jintsugawa, Toru; Kondo, Hideyuki; Nakanishi, Yosuke; Fukuyama, Yoshikazu; Mori, Hiroyuki.
In: Electrical Engineering in Japan (English translation of Denki Gakkai Ronbunshi), Vol. 186, No. 2, 2014, p. 52-60.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - A wind power forecasting method and its confidence interval estimation
AU - Iizaka, Tatsuya
AU - Jintsugawa, Toru
AU - Kondo, Hideyuki
AU - Nakanishi, Yosuke
AU - Fukuyama, Yoshikazu
AU - Mori, Hiroyuki
PY - 2014
Y1 - 2014
N2 - This paper describes a wind power forecasting method and its confidence interval estimation. Recently, flat control of wind power generators using various batteries has been required. In flat control, accurate wind power forecasts and their error confidence intervals are needed. In this paper, wind speed forecasts are calculated by regression models using Grid Point Value (GPV) weather forecasts. The forecasts are adjusted by fuzzy inference using the latest errors. The wind power forecasts are translated from the wind speed forecasts using two power curves. The power curves are selected or combined by fuzzy inference depending on the wind direction. The error confidence interval models are generated for each forecasting target time. Each confidence interval is combined by the other fuzzy inference. The proposed methods were applied to actual wind power generators, and it was found that the forecasting errors were smaller than in the conventional methods. Almost all of the forecasts can be within the error confidence intervals estimated by the proposed methods. The results show the effectiveness of the proposed methods.
AB - This paper describes a wind power forecasting method and its confidence interval estimation. Recently, flat control of wind power generators using various batteries has been required. In flat control, accurate wind power forecasts and their error confidence intervals are needed. In this paper, wind speed forecasts are calculated by regression models using Grid Point Value (GPV) weather forecasts. The forecasts are adjusted by fuzzy inference using the latest errors. The wind power forecasts are translated from the wind speed forecasts using two power curves. The power curves are selected or combined by fuzzy inference depending on the wind direction. The error confidence interval models are generated for each forecasting target time. Each confidence interval is combined by the other fuzzy inference. The proposed methods were applied to actual wind power generators, and it was found that the forecasting errors were smaller than in the conventional methods. Almost all of the forecasts can be within the error confidence intervals estimated by the proposed methods. The results show the effectiveness of the proposed methods.
KW - confidence interval
KW - forecasting
KW - fuzzy inference
KW - wind power generation
UR - http://www.scopus.com/inward/record.url?scp=84886777019&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84886777019&partnerID=8YFLogxK
U2 - 10.1002/eej.22326
DO - 10.1002/eej.22326
M3 - Article
AN - SCOPUS:84886777019
VL - 186
SP - 52
EP - 60
JO - Electrical Engineering in Japan (English translation of Denki Gakkai Ronbunshi)
JF - Electrical Engineering in Japan (English translation of Denki Gakkai Ronbunshi)
SN - 0424-7760
IS - 2
ER -