A wind power forecasting method with confidence interval

Tatsuya Iizaka; Toru Jintsugawa; Hideyuki Kondo; Yousuke Nakanishi; Yoshikazu Fukuyama; Hiroyuki Mori

doi:10.1109/MWSCAS.2011.6026398

A wind power forecasting method with confidence interval

Tatsuya Iizaka, Toru Jintsugawa, Hideyuki Kondo, Yousuke Nakanishi, Yoshikazu Fukuyama, Hiroyuki Mori

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

1 Citation (Scopus)

Abstract

This paper presents a wind power forecasting method with confidence interval. Wind speed forecasts are calculated by regression models using GPV (Grid Point Vale) weather forecasts. The forecasts are adjusted by fuzzy inference using the latest error. The wind power forecasts are translated from the wind speed forecasts using two power-curves. They are combined by fuzzy inference depending on wind direction. The error confidence interval models are generated for each forecasting target time. Each confidence interval is combined by another fuzzy inference. The proposed method has been applied to actual power generators, and found that forecasting errors are better than the conventional methods. The results show the effectiveness of the proposed method.

Original language	English
Title of host publication	54th IEEE International Midwest Symposium on Circuits and Systems, MWSCAS 2011
DOIs	https://doi.org/10.1109/MWSCAS.2011.6026398
Publication status	Published - 2011 Oct 13
Externally published	Yes
Event	54th IEEE International Midwest Symposium on Circuits and Systems, MWSCAS 2011 - Seoul, Korea, Republic of Duration: 2011 Aug 7 → 2011 Aug 10

Publication series

Name	Midwest Symposium on Circuits and Systems
ISSN (Print)	1548-3746

Other

Other	54th IEEE International Midwest Symposium on Circuits and Systems, MWSCAS 2011
Country	Korea, Republic of
City	Seoul
Period	11/8/7 → 11/8/10

ASJC Scopus subject areas

Electronic, Optical and Magnetic Materials
Electrical and Electronic Engineering

Access to Document

10.1109/MWSCAS.2011.6026398

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A wind power forecasting method with confidence interval. / Iizaka, Tatsuya; Jintsugawa, Toru; Kondo, Hideyuki; Nakanishi, Yousuke; Fukuyama, Yoshikazu; Mori, Hiroyuki.

54th IEEE International Midwest Symposium on Circuits and Systems, MWSCAS 2011. 2011. 6026398 (Midwest Symposium on Circuits and Systems).

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

Iizaka, T, Jintsugawa, T, Kondo, H, Nakanishi, Y, Fukuyama, Y & Mori, H 2011, A wind power forecasting method with confidence interval. in 54th IEEE International Midwest Symposium on Circuits and Systems, MWSCAS 2011., 6026398, Midwest Symposium on Circuits and Systems, 54th IEEE International Midwest Symposium on Circuits and Systems, MWSCAS 2011, Seoul, Korea, Republic of, 11/8/7. https://doi.org/10.1109/MWSCAS.2011.6026398

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abstract = "This paper presents a wind power forecasting method with confidence interval. Wind speed forecasts are calculated by regression models using GPV (Grid Point Vale) weather forecasts. The forecasts are adjusted by fuzzy inference using the latest error. The wind power forecasts are translated from the wind speed forecasts using two power-curves. They are combined by fuzzy inference depending on wind direction. The error confidence interval models are generated for each forecasting target time. Each confidence interval is combined by another fuzzy inference. The proposed method has been applied to actual power generators, and found that forecasting errors are better than the conventional methods. The results show the effectiveness of the proposed method.",

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AU - Jintsugawa, Toru

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AU - Mori, Hiroyuki

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AB - This paper presents a wind power forecasting method with confidence interval. Wind speed forecasts are calculated by regression models using GPV (Grid Point Vale) weather forecasts. The forecasts are adjusted by fuzzy inference using the latest error. The wind power forecasts are translated from the wind speed forecasts using two power-curves. They are combined by fuzzy inference depending on wind direction. The error confidence interval models are generated for each forecasting target time. Each confidence interval is combined by another fuzzy inference. The proposed method has been applied to actual power generators, and found that forecasting errors are better than the conventional methods. The results show the effectiveness of the proposed method.

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