Browse > Article
http://dx.doi.org/10.5467/JKESS.2017.38.1.11

A Study of Improvement of a Prediction Accuracy about Wind Resources based on Training Period of Bayesian Kalman Filter Technique  

Lee, Soon-Hwan (Department of Earth Science Education, Pusan National University)
Publication Information
Journal of the Korean earth science society / v.38, no.1, 2017 , pp. 11-23 More about this Journal
Abstract
The short term predictability of wind resources is an important factor in evaluating the economic feasibility of a wind power plant. As a method of improving the predictability, a Bayesian Kalman filter is applied as the model data postprocessing. At this time, a statistical training period is needed to evaluate the correlation between estimated model and observation data for several Kalman training periods. This study was quantitatively analyzes for the prediction characteristics according to different training periods. The prediction of the temperature and wind speed with 3-day short term Bayesian Kalman training at Taebaek area is more reasonable than that in applying the other training periods. In contrast, it may produce a good prediction result in Ieodo when applying the training period for more than six days. The prediction performance of a Bayesian Kalman filter is clearly improved in the case in which the Weather Research Forecast (WRF) model prediction performance is poor. On the other hand, the performance improvement of the WRF prediction is weak at the accurate point.
Keywords
Bayesian Kalman filter; wind resources; numerical model; training period; WRF;
Citations & Related Records
Times Cited By KSCI : 6  (Citation Analysis)
연도 인용수 순위
1 Kim, K.D., Choi, D.H., Sim, J., and Kim, K.C., 2011, Development and design of offshore wind turbine support structures. Korean Society of Civil Engineers Journal of Civil Engineering, 59(5), 28-37. (in Korean)
2 Kim, Y.D., Jeong, Y.M., and Lee, D.D., 2014, Technical trend of radar radio interference reduction relating to construction of the offshore wind farm. Journal of the Korean Institute of Electrical and Electronic Material Engineers, 27(4), 250-256. (in Korean)   DOI
3 Lee, S.H., Lee, H.W., Kim, D.H., Kim, M.J., and Kim, H.G., 2011, Analytic study on the variation of regional wind resources associated with the Change of El Nino/La Nina Intensity, The Journal of The Korean Earth Science Society, 32, 180-189. (in Korean)   DOI
4 Lee, S.H., 2012, Analysis of the impact of QuikSCAT and ASCAT sea wind data assimilation on the prediction of regional wind field near coastal Area. The Journal of The Korean Earth Science Society, 33, 309-319. (in Korean)   DOI
5 Lee, S.H., Park, S.Y., Lee, H.W., and Kim, D.H., 2012, Charateristics of the estimation of wind energy according to temporal resolution of wind resources map. Journal of Wind Energy, 3(2), 67-73. (in Korean)
6 Li G., Shi J., and Zhou J., 2011, Bayesian adaptive combination of short-term wind speed forecasts from neural network models. Renewable Energy, 36, 352-359.   DOI
7 Kalman, R.E., 1960, A new approach to linear filtering and prediction problems. Transactions of the ASME-Journal of Basic Engineering, 82, 35-45.   DOI
8 Louka, P., Galanis, G., Siebert, N., Kariniotakis, G., Katsafados, P., Pytharoulis, I., and Kallos, G., 2008, Improvements in wind speed forecasts for wind power prediction purposes using Kalman filtering. Journal of Wind Engineering and Industrial Aerodynamics, 96(12), 2348-2362.   DOI
9 Lynch, C., OMahony, M.J., and Scully, T., 2014, Simplified method to derive the Kalman filter covariance matrices to predict wind speeds from a NWP model. Energy procedia, 62, 676-685.   DOI
10 Monfared, M., Rastegar, H., and Kojabadi, H.M., 2009, A new strategy for wind speed forecasting using artificial intelligent methods. Renewable Energy, 34, 845-848.   DOI
11 Cheruy, F., Speranza, A., Sutera, A., and Tartaglione, N., 2004, Surface winds in the Euro-Mediterranean area: the real resolution of numerical grids. Annales Geophysicae, 22, 4043-4048.   DOI
12 Yoo, J.W., Lee, H.W., Lee, S.H., and Kim, D.H., 2012, Characteristics of vertical variation of wind resources in planetary boundary layer in coastal area using tall tower observation. Journal of Korean Society for Atmospheric Environment, 28(6), 632-643. (in Korean)   DOI
13 Yoo, J.W., Lee, S.H., and Lee, H.W., 2014, Numerical study on the characteristics of TKE in coastal area for offshore wind power. Journal of Environmental Science International, 23(9), 1551-1562. (in Korean)   DOI
14 West, M. and Harrison, J., 1997, Bayesian forecasting and dynamic models (second edition). Springer-Verlag, New York, USA, 680 p.
15 Accadia, C., Zecchetto, S., Lavagnini, A., and Speranza A., 2007, Comparison of 10-m wind forecasts from a regional area model and QuikSCAT scatterometer wind observations over the Mediterranean Sea. Monthly Weather Review, 135, 1945-1960.   DOI
16 Cassola, F. and Burlando, M., 2012, Wind speed and wind energy forecast through Kalman filtering of numerical weather prediction model output. Applied Energy, 99, 154-166.   DOI
17 Giebel, G., Brownsword, R., Kariniotakis, G., Denhard, M., and Drax, C., 2011, The State-Of-The-Art in Short-Term Prediction of Wind Power: A literature overview, 2nd ed., Project report for the Anemos. plus and Safe Wind projects, Riso, Roskilde, Denmark, 109 p.
18 Kim, H.G., Lee, H.W., and Lee, S.H., 2011, Development of the Korea wind resource map and suitability assessment system for offshore wind farm. Journal of Wind Energy, 2(2), 17-23. (in Korean)
19 Kim, H.G., Hwang, H.j., and Kang, Y.H., 2013, Evaluation of onshore wind resource potential according to environmental conservation value assessment. Journal of Environmental Science International, 22(6), 717-721. (in Korean)   DOI