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Evaluating the wind speed persistence for several wind stations in Peninsular Malaysia

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  • Masseran, N.
  • Razali, A.M.
  • Ibrahim, K.
  • Wan Zin, W.Z.

Abstract

An important factor to consider when evaluating wind energy potential is the wind speed persistence. In this study, persistence of the wind speed in Peninsular Malaysia is investigated based on the hourly data available at 10 wind stations from 2007 to 2009. To determine the degree of persistence in the data for each station, stationarity and variability are investigated using unit-root tests and the test for equality of variance respectively. Results from the unit-root tests indicated that the hourly wind speed for each station exhibits stationarity. The test for equality of variance, based on Levene’s test, shows that there exists a significant difference in the variability of wind speed between the different stations. Because the variance of the hourly wind speeds for the Chuping station is the smallest observed, the wind speed observed at this location is the most persistent compared to other locations. However, it is more meaningful to measure the persistence at a particular level of speed, one suitable to generate energy. Accordingly, the wind speed duration curve method is applied to the observed data for each station. Consequently, the wind speed at Mersing is found to be the most persistent, and, consequently, this location has the most potential for energy production compared to other locations.

Suggested Citation

  • Masseran, N. & Razali, A.M. & Ibrahim, K. & Wan Zin, W.Z., 2012. "Evaluating the wind speed persistence for several wind stations in Peninsular Malaysia," Energy, Elsevier, vol. 37(1), pages 649-656.
    • Handle: RePEc:eee:energy:v:37:y:2012:i:1:p:649-656
    DOI: 10.1016/j.energy.2011.10.035
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    1. Noriega, Antonio E. & Ramos-Francia, Manuel, 2009. "The dynamics of persistence in US inflation," Economics Letters, Elsevier, vol. 105(2), pages 168-172, November.
    2. Elliott, Graham & Rothenberg, Thomas J & Stock, James H, 1996. "Efficient Tests for an Autoregressive Unit Root," Econometrica, Econometric Society, vol. 64(4), pages 813-836, July.
    3. Lu, Lin & Yang, Hongxing & Burnett, John, 2002. "Investigation on wind power potential on Hong Kong islands—an analysis of wind power and wind turbine characteristics," Renewable Energy, Elsevier, vol. 27(1), pages 1-12.
    4. Andrews, Donald W K, 1991. "Heteroskedasticity and Autocorrelation Consistent Covariance Matrix Estimation," Econometrica, Econometric Society, vol. 59(3), pages 817-858, May.
    5. Cellura, M. & Cirrincione, G. & Marvuglia, A. & Miraoui, A., 2008. "Wind speed spatial estimation for energy planning in Sicily: Introduction and statistical analysis," Renewable Energy, Elsevier, vol. 33(6), pages 1237-1250.
    6. Cancino-Solórzano, Yoreley & Gutiérrez-Trashorras, Antonio J. & Xiberta-Bernat, Jorge, 2010. "Analytical methods for wind persistence: Their application in assessing the best site for a wind farm in the State of Veracruz, Mexico," Renewable Energy, Elsevier, vol. 35(12), pages 2844-2852.
    7. Dias, Daniel A. & Marques, Carlos Robalo, 2010. "Using mean reversion as a measure of persistence," Economic Modelling, Elsevier, vol. 27(1), pages 262-273, January.
    8. Fletcher, Jonathan & Forbes, David, 2002. "An exploration of the persistence of UK unit trust performance," Journal of Empirical Finance, Elsevier, vol. 9(5), pages 475-493, December.
    9. Ilkiliç, Cumali & Türkbay, Ismail, 2010. "Determination and utilization of wind energy potential for Turkey," Renewable and Sustainable Energy Reviews, Elsevier, vol. 14(8), pages 2202-2207, October.
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    6. Ho, Lip-Wah, 2016. "Wind energy in Malaysia: Past, present and future," Renewable and Sustainable Energy Reviews, Elsevier, vol. 53(C), pages 279-295.
    7. Aliashim Albani & Mohd Zamri Ibrahim, 2017. "Wind Energy Potential and Power Law Indexes Assessment for Selected Near-Coastal Sites in Malaysia," Energies, MDPI, vol. 10(3), pages 1-21, March.
    8. Jiang, Lei, 2018. "Mean wind speed persistence over China," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 502(C), pages 211-217.
    9. Masseran, Nurulkamal, 2015. "Evaluating wind power density models and their statistical properties," Energy, Elsevier, vol. 84(C), pages 533-541.
    10. Masseran, Nurulkamal, 2016. "Modeling the fluctuations of wind speed data by considering their mean and volatility effects," Renewable and Sustainable Energy Reviews, Elsevier, vol. 54(C), pages 777-784.
    11. Amar Azhar & Huzaifa Hashim, 2023. "A Review of Wind Clustering Methods Based on the Wind Speed and Trend in Malaysia," Energies, MDPI, vol. 16(8), pages 1-24, April.
    12. Hamdan, A. & Mustapha, F. & Ahmad, K.A. & Mohd Rafie, A.S., 2014. "A review on the micro energy harvester in Structural Health Monitoring (SHM) of biocomposite material for Vertical Axis Wind Turbine (VAWT) system: A Malaysia perspective," Renewable and Sustainable Energy Reviews, Elsevier, vol. 35(C), pages 23-30.
    13. Scholz, Teresa & Lopes, Vitor V. & Estanqueiro, Ana, 2014. "A cyclic time-dependent Markov process to model daily patterns in wind turbine power production," Energy, Elsevier, vol. 67(C), pages 557-568.
    14. Nor, Khalid Mohamed & Shaaban, Mohamed & Abdul Rahman, Hasimah, 2014. "Feasibility assessment of wind energy resources in Malaysia based on NWP models," Renewable Energy, Elsevier, vol. 62(C), pages 147-154.
    15. José Carlos Palomares-Salas & Agustín Agüera-Pérez & Juan José González de la Rosa & José María Sierra-Fernández & Antonio Moreno-Muñoz, 2013. "Exogenous Measurements from Basic Meteorological Stations for Wind Speed Forecasting," Energies, MDPI, vol. 6(11), pages 1-19, November.
    16. Yu, Jie & Chen, Kuilin & Mori, Junichi & Rashid, Mudassir M., 2013. "A Gaussian mixture copula model based localized Gaussian process regression approach for long-term wind speed prediction," Energy, Elsevier, vol. 61(C), pages 673-686.

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