Wind Energy Potential Assessment Based on Wind Direction Modelling and Machine Learning

Pavel Krömer, Stanislav Mišák, Jindřich Stuchlý, Jan Platos

Abstract


Precise wind energy potential assessment is vital for wind energy generation and planning and development of new wind power plants. This work proposes and evaluates a novel two-stage method for location-specific wind energy potential assessment. It combines accurate statistical modelling of annual wind direction distribution in a given location with supervised machine learning of efficient estimators that can approximate energy efficiency coefficients from the parameters of optimized statistical wind direction models. The statistical models are optimized using differential evolution and energy efficiency is approximated by evolutionary fuzzy rules.

Keywords


differential evolution;wind direction modelling;evolutionary fuzzy rules;wind energy potential assessment;estimation;optimization

References


Abohela, I., Hamza, N., and Dudek, S. Effect of roof shape, wind direction, building

height and urban configuration on the energy yield and positioning of roof mounted wind

turbines. Renewable Energy 50, 0 (2013), 1106 - 1118.

Affenzeller, M., Winkler, S., Wagner, S., and Beham, A. Genetic Algorithms and Ge-

netic Programming: Modern Concepts and Practical Applications. Chapman & Hall/CRC,

Afzal, W., and Torkar, R. On the application of genetic programming for software

engineering predictive modeling: A systematic review. Expert Systems with Applications 38,

(2011), 11984 - 11997.

Arslan, T., Bulut, Y. M., and Yavuz, A. A. Comparative study of numerical methods

for determining weibull parameters for wind energy potential. Renewable and Sustainable

Energy Reviews 40, 0 (2014), 820 - 825.

Bacardit, J., and Llora, X. Large-scale data mining using genetics-based machine learning.

Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery 3, 1 (2013),

-61.

Banerjee, A., Dhillon, I. S., Ghosh, J., and Sra, S. Clustering on the unit hypersphere

using von mises-fisher distributions. J. Mach. Learn. Res. 6 (Dec. 2005), 1345-1382.

Bezdek, J. C., Keller, J., Krisnapuram, R., and Pal, N. R. Fuzzy Models and Algorithms

for Pattern Recognition and Image Processing (The Handbooks of Fuzzy Sets). Springer-

Verlag New York, Inc., Secaucus, NJ, USA, 2005.

Bordogna, G., and Pasi, G. Modeling vagueness in information retrieval. In Lectures on

information retrieval. Springer-Verlag New York, Inc., New York, NY, USA, 2001, pp. 207-

Carta, J. A., Bueno, C., and Ramirez, P. Statistical modelling of directional wind speeds

using mixtures of von mises distributions: Case study. Energy Conversion and Management

, 5 (2008), 897 - 907.

Carta, J. A., Ramirez, P., and Bueno, C. A joint probability density function of wind

speed and direction for wind energy analysis. Energy Conversion and Management 49, 6

(2008), 1309 - 1320.

Chen, N., Li, Y., and Xiang, H. A new simulation algorithm of multivariate short-term

stochastic wind velocity field based on inverse fast fourier transform. Engineering Structures

, 0 (2014), 251 - 259.

Cordon, O. A historical review of evolutionary learning methods for mamdani-type fuzzy

rule-based systems: Designing interpretable genetic fuzzy systems. International Journal of

Approximate Reasoning 52, 6 (2011), 894 - 913.

Cordon, O., Gomide, F., Herrera, F., Hoffmann, F., and Magdalena, L. Ten years of

genetic fuzzy systems: current framework and new trends. Fuzzy Sets and Systems 141, 1

(2004), 5 - 31. Genetic Fuzzy Systems: New Developments.

Croonenbroeck, C., and Dahl, C. M. Accurate medium-term wind power forecasting in

a censored classification framework. Energy 73, 0 (2014), 221 - 232.

de Andrade, C. F., ao Maia Neto, H. F., Rocha, P. A. C., and da Silva, M. E. V.

An efficiency comparison of numerical methods for determining weibull parameters for wind

energy applications: A new approach applied to the northeast region of brazil. Energy

Conversion and Management 86, 0 (2014), 801 - 808.

de la Rosa, J. J. G., Perez, A. A., Salas, J. C. P., Leo, J. G. R., and noz, A. M. M.

A novel inference method for local wind conditions using genetic fuzzy systems. Renewable

Energy 36, 6 (2011), 1747 - 1753.

Devroye, L. Non-Uniform Random Variate Generation. Springer-Verlag, New York, NY,

Engelbrecht, A. Computational Intelligence: An Introduction, 2nd Edition. Wiley, New

York, NY, USA, 2007.

Feng, J., and Shen, W. Z. Modelling wind for wind farm layout optimization using joint

distribution of wind speed and wind direction. Energies 8, 4 (2015), 3075-3092.

Fisher, N. I. Statistical analysis of circular data. Cambridge University Press, 1995.

Gaumond, M., Rethore, P.-E., Ott, S., Pe~na, A., Bechmann, A., and Hansen, K. S.

Evaluation of the wind direction uncertainty and its impact on wake modeling at the horns

rev offshore wind farm. Wind Energy 17, 8 (2014), 1169-1178.

Hastie, T., Tibshirani, R., and Friedman, J. The Elements of Statistical Learning.

Springer Series in Statistics. Springer New York Inc., New York, NY, USA, 2001.

Heckenbergerova, J., Musilek, P., and Kromer, P. Optimization of wind direction

distribution parameters using particle swarm optimization. In Afro-European Conference for

Industrial Advancement, A. Abraham, P. Kromer, and V. Snasel, Eds., vol. 334 of Advances

in Intelligent Systems and Computing. Springer International Publishing, 2015, pp. 15-26.

Heckenbergerova, J., Musilek, P., Mejznar, J., and Vancura, M. Estimation of wind

direction distribution with genetic algorithms. In CCECE (2013), IEEE, pp. 1-4.

Hirata, Y., Mandic, D. P., Suzuki, H., and Aihara, K. Wind direction modelling using

multiple observation points. Philosophical Transactions of the Royal Society of London A:

Mathematical, Physical and Engineering Sciences 366, 1865 (2008), 591-607.

Hirata, Y., Suzuki, H., and Aihara, K. Wind modelling and its possible application

to control of wind farms. In Signal Processing Techniques for Knowledge Extraction and

Information Fusion, D. Mandic, M. Golz, A. Kuh, D. Obradovic, and T. Tanaka, Eds.

Springer US, 2008, pp. 23-36.

Jung, J., and Broadwater, R. P. Current status and future advances for wind speed and

power forecasting. Renewable and Sustainable Energy Reviews 31, 0 (2014), 762 - 777.

Jung, S., and Kwon, S.-D. Weighted error functions in artificial neural networks for improved

wind energy potential estimation. Applied Energy 111, 0 (2013), 778 - 790.

Klir, G. J., and Yuan, B. Fuzzy Sets and Fuzzy Logic; Theory and Applications. Prentice

Hall, Upper Saddle River, N. Y., 1995.

Kromer, P., Owais, S. S. J., Platos, J., and Snasel, V. Towards new directions of data

mining by evolutionary fuzzy rules and symbolic regression. Computers & Mathematics with

Applications 66, 2 (2013), 190-200.

Kromer, P., Platos, J., Snasel, V., and Abraham, A. Fuzzy classification by evolutionary

algorithms. In Systems, Man, and Cybernetics (SMC), 2011 IEEE International Conference

on (Oct 2011), pp. 313-318.

Lahouar, A., and Ben Hadj Slama, J. Wind speed and direction prediction for wind

farms using support vector regression. In Renewable Energy Congress (IREC), 2014 5th

International (March 2014), pp. 1-6.

Masseran, N. Markov chain model for the stochastic behaviors of wind-direction data.

Energy Conversion and Management 92, 0 (2015), 266 - 274.

Medimorec, D., and Zeljko Tomsic. Portfolio theory application in wind potential assessment.

Renewable Energy 76, 0 (2015), 494 - 502.

Mentis, D., Hermann, S., Howells, M., Welsch, M., and Siyal, S. H. Assessing the

technical wind energy potential in africa a gis-based approach. Renewable Energy 83, 0

(2015), 110 - 125.

Misak, S., Stuchly, J., Platos, J., and Kromer, P. A heuristic approach to active

demand side management in off-grid systems operated in a smart-grid environment. Energy

and Buildings 96, 0 (2015), 272 - 284.

Musilek, P., Guanlao, R., and Barreiro, G. Genetic programming of fuzzy aggregation

operations. Journal of Intelligent and Fuzzy Systems 16, 2 (2005), 107-118.

Olaofe, Z. O. A 5-day wind speed & power forecasts using a layer recurrent neural network

(lrnn). Sustainable Energy Technologies and Assessments 6, 0 (2014), 1 - 24.

Pasi, G. Fuzzy sets in information retrieval: State of the art and research trends. In

Fuzzy Sets and Their Extensions: Representation, Aggregation and Models, H. Bustince,

F. Herrera, and J. Montero, Eds., vol. 220 of Studies in Fuzziness and Soft Computing.

Springer Berlin / Heidelberg, 2008, pp. 517-535.

Price, K. V., Storn, R. M., and Lampinen, J. A. Differential Evolution A Practical Ap-

proach to Global Optimization. Natural Computing Series. Springer-Verlag, Berlin, Germany,

Prokop, L., Misak, S., Novosad, T., Kromer, P., Platos, J., and Snasel, V. Artificially

evolved soft computing models for photovoltaic power plant output estimation. In Proceed-

ings of the IEEE International Conference on Systems, Man, and Cybernetics, SMC 2012,

Seoul, Korea (South), October 14-17, 2012 (2012), pp. 1011-1016.

Ritter, M., Shen, Z., Cabrera, B. L., Odening, M., and Deckert, L. Designing an

index for assessing wind energy potential. Renewable Energy 83, 0 (2015), 416 - 424.

Rocha, P. A. C., de Sousa, R. C., de Andrade, C. F., and da Silva, M. E. V. Comparison

of seven numerical methods for determining weibull parameters for wind energy generation

in the northeast region of brazil. Applied Energy 89, 1 (2012), 395 - 400. Special issue on

Thermal Energy Management in the Process Industries.

Schallenberg-Rodriguez, J., and del Pino, J. N. Evaluation of on-shore wind technoeconomical

potential in regions and islands. Applied Energy 124, 0 (2014), 117 - 129.

Tagliaferri, F., Viola, I., and Flay, R. Wind direction forecasting with artificial neural

networks and support vector machines. Ocean Engineering 97, 0 (2015), 65 - 73.

Verikas, A., Guzaitis, J., Gelzinis, A., and Bacauskiene, M. A general framework for

designing a fuzzy rule-based classifier. Knowledge and Information Systems (2010), 1-19.

Wang, L.-X., and Mendel, J. Generating fuzzy rules by learning from examples. Systems,

Man and Cybernetics, IEEE Transactions on 22, 6 (1992), 1414-1427.

Weekes, S., Tomlin, A., Vosper, S., Skea, A., Gallani, M., and Standen, J. Long-term

wind resource assessment for small and medium-scale turbines using operational forecast data

and measure-correlate-predict. Renewable Energy 81, 0 (2015), 760 - 769.

Widen, J., Carpman, N., Castellucci, V., Lingfors, D., Olauson, J., Remouit, F.,

Bergkvist, M., Grabbe, M., and Waters, R. Variability assessment and forecasting of

renewables: A review for solar, wind, wave and tidal resources. Renewable and Sustainable

Energy Reviews 44, 0 (2015), 356 - 375.

Witten, I., Frank, E., and Hall, M. Data Mining: Practical Machine Learning Tools

and Techniques: Practical Machine Learning Tools and Techniques. The Morgan Kaufmann

Series in Data Management Systems. Elsevier Science, 2011.

Yamaguchi, A., and Ishihara, T. Assessment of offshore wind energy potential using

mesoscale model and geographic information system. Renewable Energy 69, 0 (2014), 506 -

Zadeh, L. A. Fuzzy sets. Information and Control 8 (1965), pp. 338-353.




DOI: http://dx.doi.org/10.14311/NNW.1901.%25x

Refbacks

  • There are currently no refbacks.


Should you encounter an error (non-functional link, missing or misleading information, application crash), please let us know at nnw.ojs@fd.cvut.cz.
Please, do not use the above address for non-OJS-related queries (manuscript status, etc.).
For your convenience we maintain a list of frequently asked questions here. General queries to items not covered by this FAQ shall be directed to the journal editoral office at nnw@fd.cvut.cz.