Browse > Article
http://dx.doi.org/10.7849/ksnre.2022.0002

Accurate Wind Speed Prediction Using Effective Markov Transition Matrix and Comparison with Other MCP Models  

Kang, Minsang (Wind Energy Research Team, Korea Institute of Energy Research)
Son, Eunkuk (Wind Energy Research Team, Korea Institute of Energy Research)
Lee, Jinjae (Wind Energy Research Team, Korea Institute of Energy Research)
Kang, Seungjin (Wind Energy Research Team, Korea Institute of Energy Research)
Publication Information
New & Renewable Energy / v.18, no.1, 2022 , pp. 17-28 More about this Journal
Abstract
This paper presents an effective Markov transition matrix (EMTM), which will be used to calculate the wind speed at the target site in a wind farm to accurately predict wind energy production. The existing MTS prediction method using a Markov transition matrix (MTM) exhibits a limitation where significant prediction variations are observed owing to random selection errors and its bin width. The proposed method selects the effective states of the MTM and refines its bin width to reduce the error of random selection during a gap filling procedure in MTS. The EMTM reduces the level of variation in the repeated prediction of wind speed by using the coefficient of variations and range of variations. In a case study, MTS exhibited better performance than other MCP models when EMTM was applied to estimate a one-day wind speed, by using mean relative and root mean square errors.
Keywords
Wind speed prediction; Measure-correlate-predict; Matrix time series; Markov transition matrix; Markov-based reconstruction mechanism;
Citations & Related Records
Times Cited By KSCI : 1  (Citation Analysis)
연도 인용수 순위
1 Ali, S., Lee, S.M., and Jang, C.M., 2018, "Forecasting the long-term wind data via Measure-Correlate-Predict (MCP) methods", Energies, 11(6), 1541.   DOI
2 Ryu, K.W., 2021, "Use of Markov chains for synthetic wind data generation and its statistical verification", Wind. Energy, 12(3), 13-18.
3 Mifsud, M.D., Sant, T., and Farrugia, R.N., 2019, "Analysing uncertainties in offshore wind farm power output using measure correlate predict methodologies", Wind Energ. Sci., 5, 601-621   DOI
4 Son, J.H., Ko, K.N., Huh, J.C., and Kim, I.H., 2017, "Mutual application of Met-Masts wind data on simple terrain for wind resource assessment", The J. of thr Korean Soc. for Power Syst. Eng., 21(6), 31-39.
5 Lambert, T., 2019, "Windographer (version 4.2) S/W manual".
6 Lee, J.J., Kang, S.J., Lee, G.S., Kim, H.W., Kim, S.O., Ahn, Y.O., and Kyong, N.H., 2020, "Validation of floating LiDAR system for development of offshore wind farms", New. Renew. Energy, 16(3), 35-41.
7 Liu, X., Lai, X., and Zou, J., 2017, "A new MCP method of wind speed temporal interpolation and extrapolation considering wind speed mixed uncertainty", Energies, 10(8), 1231.   DOI
8 Carta, J.A., and Velazquez, S., 2011, "A new probabilistic method to estimate the long-term wind speed characteristics at a potential wind energy conversion site", Energy, 36(5), 2671-2685.   DOI
9 Rogers, A.L., Rogers, J.W., and Manwell, J.F., 2005, "Comparison of the performance of four measure-correlate-predict algorithms", J. Wind Eng. Ind. Aerodyn., 93(3), 243-264.   DOI