Abstract
Data describing various natural and industrial phenomena can be modeled by directional statistical distributions. In the field of energy, wind direction and wind speed are the most important variables for wind energy generation, integration, and management. This work proposes and evaluates a new method for accurate estimation of wind direction distribution parameters utilizing the well-known Particle Swarm Optimization algorithm. It is used to optimize the parameters of a site-specific wind direction distribution model realized as a finite mixture of circular normal von Mises statistical distributions. The evaluation of the proposed algorithm is carried out using a data set describing annual wind direction on two distinct locations. Experimental results show that the proposed method is able to find good model parameters corresponding to input data.
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References
Mooney, J.A., Helms, P.J., Jolliffe, I.T.: Fitting mixtures of von Mises distributions: a case study involving sudden infant death syndrome. Computational Statistics & Data Analysis 41(3-4), 505–513 (2003); Recent Developments in Mixture Model
Calderara, S., Cucchiara, R., Prati, A.: Detection of Abnormal Behaviors Using a Mixture of Von Mises Distributions. In: Proceedings of the 2007 IEEE Conference on Advanced Video and Signal Based Surveillance, AVSS 2007, pp. 141–146. IEEE Computer Society, Washington, DC (2007)
Vejmelka, M., Musilek, P., Paluš, M., Pelikán, E.: K-means clustering for problems with periodic attributes. International Journal of Pattern Recognition and Artificial Intelligence 23(4), 721–743 (2009)
Allen, C.T., Young, G.S., Haupt, S.E.: Improving pollutant source characterization by better estimating wind direction with a genetic algorithm. Atmospheric Environment 41(11), 2283–2289 (2007)
Carta, J.A., Bueno, C., Ramírez, P.: Statistical modelling of directional wind speeds using mixtures of von mises distributions: Case study. Energy Conversion and Management 49(5), 897–907 (2008)
Carta, J.A., Ramírez, P., Bueno, C.: A joint probability density function of wind speed and direction for wind energy analysis. Energy Conversion and Management 49(6), 1309–1320 (2008)
Heckenbergerova, J., Musílek, P., Mejznar, J., Vancura, M.: Estimation of wind direction distribution with genetic algorithms. In: CCECE, pp. 1–4. IEEE (2013)
Masseran, N., Razali, A., Ibrahim, K., Latif, M.: Fitting a mixture of von Mises distributions in order to model data on wind direction in Peninsular Malaysia. Energy Conversion and Management 72, 94–102 (2013); The III. International Conference on Nuclear and Renewable Energy Resources {NURER2012}.
Jung, S., Kwon, S.D.: Weighted error functions in artificial neural networks for improved wind energy potential estimation. Applied Energy 111, 778–790 (2013)
Bazilian, M., Nussbaumer, P., Rogner, H.H., Brew-Hammond, A., Foster, V., Kammen, D.M., Pachauri, S., Williams, E., Howells, M., Niyongabo, P., Lawrence, M.: O Gallachoir, B., Radka, M.: Energy Access Scenarios to 2030 for the Power Sector in Sub-Saharan Africa. Utilities Policy 20, 1–16 (2012)
Pereira, M.G., Camacho, C.F., Freitas, M.A.V., da Silva, N.F.: The renewable energy market in Brazil: Current status and potential. Renewable and Sustainable Energy Reviews 16(6), 3786–3802 (2012)
Bekele, G., Tadesse, G.: Feasibility study of small Hydro/PV/Wind hybrid system for off-grid rural electrification in Ethiopia. Applied Energy 97, 5–15 (2012); Energy Solutions for a Sustainable World - Proceedings of the Third International Conference on Applied Energy, Perugia, Italy, May 16-18 (2011)
Davis, M.W.: A new thermal rating approach: The real time thermal rating system for strategic overhead conductor transmission lines – Part I: General description and justification of the real time thermal rating system. IEEE Transactions on Power Apparatus and Systems 96(3), 803–809 (1977)
Douglass, D.: Weather-dependent versus static thermal line ratings [power overhead lines]. IEEE Transactions on Power Delivery 3(2), 742–753 (1988)
Heckenbergerová, J., Musilek, P., Filimonenkov, K.: Quantification of gains and risks of static thermal rating based on typical meteorological year. International Journal of Electrical Power & Energy Systems 44(1), 227–235 (2013)
Ettoumi, F., Sauvageot, H., Adane, A.E.H.: Statistical bivariate modelling of wind using first-order Markov chain and Weibull distribution. Renewable Energy 28(11), 1787–1802 (2003)
García-Rojo, R.: Algorithm for the Estimation of the Long-term Wind Climate at a Meteorological Mast Using a Joint Probabilistic Approach. Wind Engineering 28(2), 213–223 (2004)
Erdem, E., Shi, J.: {ARMA} based approaches for forecasting the tuple of wind speed and direction. Applied Energy 88(4), 1405–1414 (2011)
de la Rosa, J.J.G., Pérez, A.A., Salas, J.C.P., Leo, J.G.R., Muñoz, A.M.: A novel inference method for local wind conditions using genetic fuzzy systems. Renewable Energy 36(6), 1747–1753 (2011)
Shamshirband, S., Iqbal, J., Petković, D., Mirhashemi, M.A.: Survey of four models of probability density functions of wind speed and directions by adaptive neuro-fuzzy methodology. Advances in Engineering Software 76, 148–153 (2014)
Tascikaraoglu, A., Uzunoglu, M.: A review of combined approaches for prediction of short-term wind speed and power. Renewable and Sustainable Energy Reviews 34, 243–254 (2014)
Koch, W.: Directional analysis of SAR images aiming at wind direction. IEEE Transactions on Geoscience and Remote Sensing 42(4), 702–710 (2004)
AlRashidi, M.R., El-Hawary, M.: A Survey of Particle Swarm Optimization Applications in Electric Power Systems. IEEE Transactions on Evolutionary Computation 13(4), 913–918 (2009)
Onwunalu, J., Durlofsky, L.: Application of a particle swarm optimization algorithm for determining optimum well location and type. Computational Geosciences 14(1), 183–198 (2010)
Mardia, K., Jupp, P.: Directional Statistics. John Wiley & Sons (2000)
Fisher, N.I.: Statistical analysis of circular data. Cambridge University Press (1995)
Banerjee, A., Dhillon, I.S., Ghosh, J., Sra, S.: Clustering on the unit hypersphere using von Mises-Fisher distributions. Journal of Machine Learning Research 6 (2005)
Pearson, K.: On the criterion that a given system of deviations from the probable in the case of a correlated system of variables is such that it can be reasonably supposed to have arisen from random sampling. Philosophical Magazine Series 5 50, 157–175 (1900)
Anderson, T.W., Darling, D.A.: A Test of Goodness of Fit. Journal of the American Statistical Association (49), 765–769 (1954)
Clerc, M.: Particle Swarm Optimization. ISTE. Wiley (2010)
Engelbrecht, A.: Computational Intelligence: An Introduction, 2nd edn. Wiley, New York (2007)
Kennedy, J., Eberhart, R.: Particle Swarm Optimization. In: Proceedings of the IEEE International Conf. on Neural Networks, vol. 4, pp. 1942–1948 (1995)
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Heckenbergerova, J., Musilek, P., Krömer, P. (2015). Optimization of Wind Direction Distribution Parameters Using Particle Swarm Optimization. In: Abraham, A., Krömer, P., Snasel, V. (eds) Afro-European Conference for Industrial Advancement. Advances in Intelligent Systems and Computing, vol 334. Springer, Cham. https://doi.org/10.1007/978-3-319-13572-4_2
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DOI: https://doi.org/10.1007/978-3-319-13572-4_2
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-13571-7
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