Abstract
This paper addresses wind power prediction, which is known to be a key technology in energy management systems. In this paper, a 24-h-ahead power prediction method using a filter theory is proposed for wind power generation. The prediction method is a simple algorithm. The procedure of prediction consists of two steps: the data processing and the calculation of the predicted values. In data processing, in order to obtain the correlative data from the database, we employ just-in-time modeling. In the calculation of the predicted values, we propose a regression model for wind speed and wind power, and the unknown parameters are estimated using a constrained Kalman filter. Moreover, in the procedure used to estimate the unknown parameters, reduction and convergence of the variables are also guaranteed. Finally, the advantages of the proposed method over the conventional method are shown through actual prediction evaluations.
| Original language | English |
|---|---|
| Pages (from-to) | 86-96 |
| Number of pages | 11 |
| Journal | Electrical Engineering in Japan (English translation of Denki Gakkai Ronbunshi) |
| Volume | 198 |
| Issue number | 3 |
| DOIs | |
| Publication status | Published - 2017 Feb 1 |
Fingerprint
Keywords
- constrained Kalman filter
- energy management system (EMS)
- JIT modeling
- short-term prediction
- wind power
ASJC Scopus subject areas
- Energy Engineering and Power Technology
- Electrical and Electronic Engineering
Cite this
Short-Term Wind Power Prediction for Wind Turbine via Kalman Filter Based on JIT Modeling. / Ishikawa, Tomoki; Kojima, Takaaki; Namerikawa, Toru.
In: Electrical Engineering in Japan (English translation of Denki Gakkai Ronbunshi), Vol. 198, No. 3, 01.02.2017, p. 86-96.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - Short-Term Wind Power Prediction for Wind Turbine via Kalman Filter Based on JIT Modeling
AU - Ishikawa, Tomoki
AU - Kojima, Takaaki
AU - Namerikawa, Toru
PY - 2017/2/1
Y1 - 2017/2/1
N2 - This paper addresses wind power prediction, which is known to be a key technology in energy management systems. In this paper, a 24-h-ahead power prediction method using a filter theory is proposed for wind power generation. The prediction method is a simple algorithm. The procedure of prediction consists of two steps: the data processing and the calculation of the predicted values. In data processing, in order to obtain the correlative data from the database, we employ just-in-time modeling. In the calculation of the predicted values, we propose a regression model for wind speed and wind power, and the unknown parameters are estimated using a constrained Kalman filter. Moreover, in the procedure used to estimate the unknown parameters, reduction and convergence of the variables are also guaranteed. Finally, the advantages of the proposed method over the conventional method are shown through actual prediction evaluations.
AB - This paper addresses wind power prediction, which is known to be a key technology in energy management systems. In this paper, a 24-h-ahead power prediction method using a filter theory is proposed for wind power generation. The prediction method is a simple algorithm. The procedure of prediction consists of two steps: the data processing and the calculation of the predicted values. In data processing, in order to obtain the correlative data from the database, we employ just-in-time modeling. In the calculation of the predicted values, we propose a regression model for wind speed and wind power, and the unknown parameters are estimated using a constrained Kalman filter. Moreover, in the procedure used to estimate the unknown parameters, reduction and convergence of the variables are also guaranteed. Finally, the advantages of the proposed method over the conventional method are shown through actual prediction evaluations.
KW - constrained Kalman filter
KW - energy management system (EMS)
KW - JIT modeling
KW - short-term prediction
KW - wind power
UR - http://www.scopus.com/inward/record.url?scp=84994177737&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84994177737&partnerID=8YFLogxK
U2 - 10.1002/eej.22888
DO - 10.1002/eej.22888
M3 - Article
AN - SCOPUS:84994177737
VL - 198
SP - 86
EP - 96
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 - 3
ER -