Abstract
This paper addresses wind power prediction which is known to be a key technology in EMS(Energy Management Systems). In this paper, 24 hours ahead power prediction method using a filtering 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 predicted values. In the data processing, in order to get the correlative data from the database, we employ JIT(Just-In-Time) Modeling. In the calculation of predicted value, we propose the regression model for wind speed and wind power, and the unknown parameters are estimated via constrained kalman filter. Moreover, in a procedure of estimation of the unknown parameters, reduction and the convergence of them 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) | 81-89 |
| Number of pages | 9 |
| Journal | IEEJ Transactions on Electronics, Information and Systems |
| Volume | 135 |
| Issue number | 1 |
| DOIs | |
| Publication status | Published - 2015 |
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Keywords
- Constrained kalman filter
- Energy management systems (EMS)
- JIT modeling
- Short-term prediction
- Wind power
ASJC Scopus subject areas
- 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: IEEJ Transactions on Electronics, Information and Systems, Vol. 135, No. 1, 2015, p. 81-89.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 - 2015
Y1 - 2015
N2 - This paper addresses wind power prediction which is known to be a key technology in EMS(Energy Management Systems). In this paper, 24 hours ahead power prediction method using a filtering 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 predicted values. In the data processing, in order to get the correlative data from the database, we employ JIT(Just-In-Time) Modeling. In the calculation of predicted value, we propose the regression model for wind speed and wind power, and the unknown parameters are estimated via constrained kalman filter. Moreover, in a procedure of estimation of the unknown parameters, reduction and the convergence of them 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 EMS(Energy Management Systems). In this paper, 24 hours ahead power prediction method using a filtering 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 predicted values. In the data processing, in order to get the correlative data from the database, we employ JIT(Just-In-Time) Modeling. In the calculation of predicted value, we propose the regression model for wind speed and wind power, and the unknown parameters are estimated via constrained kalman filter. Moreover, in a procedure of estimation of the unknown parameters, reduction and the convergence of them 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 systems (EMS)
KW - JIT modeling
KW - Short-term prediction
KW - Wind power
UR - http://www.scopus.com/inward/record.url?scp=84920381789&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84920381789&partnerID=8YFLogxK
U2 - 10.1541/ieejeiss.135.81
DO - 10.1541/ieejeiss.135.81
M3 - Article
AN - SCOPUS:84920381789
VL - 135
SP - 81
EP - 89
JO - IEEJ Transactions on Electronics, Information and Systems
JF - IEEJ Transactions on Electronics, Information and Systems
SN - 0385-4221
IS - 1
ER -