A methodology for forecasting wind speed of wind power plants considering spatial and temporal correlations

Abstract: 23 | PDF: 40

##plugins.themes.academic_pro.article.main##

Author

  • Le Dinh Duong

Keywords:

Forecast
wind speed
Vector AutoRegressive
pre-processing technique
correlation

Abstract

Today, wind energy sources are increasingly attracting researchers as well as companies that exploit and use wind energy because of great benefits from this energy source, especially for environmental benefits. However, in order to effectively exploit and use wind energy, one of the important areas is forecasting. It is essential to accurately forecast wind speed and power output of wind farms. The information provided by the forecast not only serves the calculating and generating power of the plant itself, but also serves the operator of the grid where the wind power plant is connected. This paper presents a forecasting methodology considering spatial and temporal correlations. The proposed approach is suitable for simultaneously forecasting correlated wind speed of multiple wind power plants.

References

    [1] Wen-Yeau Chang, “A Literature Review of Wind Forecasting Methods”, Journal of Power and Energy Engineering, 2, 161–168, 2014.
    [2] Q. Chen, K. A. Folly, “Wind Power Forecasting”, IFAC, Volume 51, Issue 28, pp. 414-419, 2018.
    [3] David Barbosa de Alencar, Carolina de Mattos Affonso, Roberto Célio Limão de Oliveira, Jorge Laureano Moya Rodríguez, Jandecy Cabral Leite, José Carlos Reston Filho, “Different Models for Forecasting Wind Power Generation: Case Study”, Energies, 10, 2017.
    [4] Vladislavleva, E., Friedrich, T., Neumann, F., Wagner, M., “Predicting the energy output of wind farms based on weather data: Important variables and their correlation”, Renew. Energy, 2013, 50, 236–243.
    [5] Sideratos, G., Hatziargyriou, N.D., “An advanced statistical method for wind power forecasting”, IEEE Trans. Power Syst., 2007, 22, 258–265.
    [6] Brown, B. G., Katz, R. W., and Murphy, A. H., “Time Series Models to Simulate and Forecast Wind Speed and Wind Power”, Journal of Climate and Applied Meteorology, 23, 1184–1195, 1984.
    [7] Huang, Z., and Chalabi, Z. S., “Use of Time-Series Analysis to Model and Forecast Wind Speed”, Journal of Wind Engineering and Industrial Aerodynamics, 56, 311–322, 1995.
    [8] Kamal L. and Jafri Y.Z., “Time Series Models to Simulateand Forecast Hourly Averaged Wind Speed in Quetta, Pakistan”, Solar Energy, 61, 23–32, 1997.
    [9] Katz, R. W., and Skaggs, R. H., “On the Use of Autoregressive-Moving Average Processes to Model Meteorological TimeSeries”, Monthly Weather Review, 109, 479–484, 1981.
    [10] Runkle, D. E., “Vector Autoregressions and Reality”, Journal of Business and Economic Statistics, 5 (4), 437-442, 1987.
    [11] Stock, J.H. and M.W. Watson, “Vector Autoregressions”, Journal of Economic Perspectives, 15, 101–115, 2001.
    [12] Watson, M., Vector Autoregressions and Cointegration,
    [13] in Handbook of Econometrics, Volume IV. R.F. Engle and
    [14] D. McFadde, Elsevier Science Ltd., Amsterdam, 1994.
    [15] De Giorgi, M.G., Ficarella, A., Russo, M.G., “Short-term wind forecasting using artificial neural networks (ANNs)”, WIT Trans. Ecol. Environ., 2009, 121, 197–208.
    [16] Li, G., Shi, J., “On comparing three artificial neural networks for wind speed forecasting”, Appl. Energy, 2010, 87, 2313–2320.
    [17] Hu, J., Wang, J., Zeng, G., “A hybrid forecasting approach applied to wind speed time series”, Renew. Energy, 2013, 60, 185–194.
    [18] Shiyu Liu, Gengfeng Li, Haipeng Xie, Xifan Wang, “Correlation Characteristic Analysis for Wind Speed in Different Geographical Hierarchies”, Energies, 10, 2017.
    [19] M. Kendall and A. Stuart, The Advanced Theory of Statistics. London, U.K.: C. Griffin, 4th edition, 1977.
    [20] H. Cramer, Mathematical Methods of Statistics, Princeton University Press, 1945.
    [21] G. E. P. Box and G. M. Jenkins, Time Series Analysis: Forecasting and Contro, San Francisco, CA: Holden Day, 1976.
    [22] K. Aho, D. Derryberry, T. Peterson, "Model selection for ecologists: the worldviews of AIC and BIC", Ecology, 95: 631–636, 2014.
    [23] Online: https://en.wikipedia.org/wiki/Ordinary_least_squares.
    [24] F. E. Grubbs, "Procedures for Detecting Outlying Observations in Samples", Technometrics, Feb. 1969.
    [25] P. J. Davis, Interpolation and approximation, Dover, New York, 1976.
    [26] D. D. Le, G. Gross, and A. Berizzi, “Probabilistic Modeling of Multisite Wind Farm Production for Scenario-Based Applications”, IEEE Transactions on Sustainable Energy, vol. 6, no. 3, 748–758, 2015.
    [27] Hamid Shaker, Hamidreza Zareipour, and David Wood, “On error measures in wind forecasting evaluations”, 26th IEEE Canadian Conference of Electrical and Computer Engineering (CCECE), 2013.
    [28] A. Papavasiliou, S. S. Oren, “Stochastic modeling of multi-area wind power production”, in Proc. 12th Int. Conf. Probab. Methods Appl. Power Syst., Istanbul, Turkey, Jun. 10–14, 2012, pp. 1–6.
Read more

##plugins.themes.academic_pro.article.sidebar##

Published

Aug 31, 2019
Download
PDF (Tiếng Việt)
How to Cite
Le Dinh Duong. “A Methodology for Forecasting Wind Speed of Wind Power Plants Considering Spatial and Temporal Correlations”. The University of Danang - Journal of Science and Technology, vol. 17, no. 8, Aug. 2019, pp. 6-10, https://jst-ud.vn/jst-ud/article/view/2400.

##plugins.themes.academic_pro.article.details##