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ENTROPY APPROACH FOR VOLATILITY OF WIND ENERGY

ABSTRACT
In this study, we give the practice of entropy in wind energy. Firstly, we fit marginal distributions to each of the variables and later demonstrate the notion of entropy to perform a comparison the wind energy data of the stations (Bursa, Elazığ, İstanbul, Muğla, Rize, Tokat, Van and Zonguldak) that have been examined in a period 2015-2018. The results of probability distribution fitting to these wind energy variables show that the wind energy time series of Bursa, Elazığ, İstanbul, Muğla, Rize, Tokat, Van and Zonguldak are best resubmitted by Gamma Burr and Lognormal distributions. Later, we calculate Shannon entropy for several various values, Tsallis entropy, Rényi entropy and the approximate entropy. We form calculation outcomes with these entropies for daily data.
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PAPER SUBMITTED: 2019-01-01
PAPER REVISED: 2019-06-25
PAPER ACCEPTED: 2019-07-27
PUBLISHED ONLINE: 2019-09-15
DOI REFERENCE: https://doi.org/10.2298/TSCI190101346C
CITATION EXPORT: view in browser or download as text file
THERMAL SCIENCE YEAR 2019, VOLUME 23, ISSUE Supplement 6, PAGES [S1863 - S1874]
REFERENCES
  1. Laidler, K.J., Thermodynamics, In The World of Physical Chemistry, Oxford University Press, New York, NY, USA, 1995, pp. 156-240
  2. Tsallis, C., Possible generalization of Boltzmann-Gibbs statistics, Journal of Statistical Physics, 52 (1988), 479-487.
  3. Rao, M.,Chen, Y.,Vemuri, B.C., Wang, F., Cumulative residual entropy: a new measure of information , IEEE transactions on Information Theory, 50 (2004), 6, 1220-1228.
  4. Shafee, F., Lambert function and a new non-extensive form of entropy, IMA journal of applied mathematics, 72 (2007), 6, 785-800.
  5. Akpinar, S., Akpinar, E. K., Wind energy analysis based on maximum entropy principle (MEP)-type distribution function. Energy Conversion and Management, 48(4), (2007), 1140-1149.
  6. Pincus, S., Approximate entropy as an irregularity measure for financial data, Econometric Reviews, 27 (2008), 4-6, 329-362.
  7. Ubriaco, M. R., Entropies based on fractional calculus, Physics Letters A, 373 (2009), 30, 2516-2519.
  8. Akpinar, S., Akpinar, E. K., Estimation of wind energy potential using finite mixture distribution models. Energy Conversion and Management, 50 (2009), 4, 877-884.
  9. Rompolis, L. S., Retrieving risk neutral densities from European option prices based on the principle of maximum entropy, Journal of Empirical Finance, 17 (2010), 5, 918-937.
  10. Moreno, B., García-Álvarez, M. T., Analyzing the effect of Renewable Energy Sources on Electricity Prices in Spain. A Maximum Entropy Econometric Approach, 2011.
  11. Hodge, B. M., Orwig, K., Milligan, M., Examining Information Entropy Approaches as Wind Power Forecasting Performance Metrics (No. NREL/CP-5500-53515). National Renewable Energy Lab.(NREL), Golden, CO (United States), 2012
  12. Wang, G. J., Xie, C., Han, F., Multi-scale approximate entropy analysis of foreign exchange markets efficiency, Systems Engineering Procedia, 3 (2012), 201-208.
  13. Moreno, B., Garcia-Alvarez, M. T., The role of renewable energy sources on electricity prices in Spain. A maximum entropy econometric model. Strojarstvo: časopis za teoriju i praksu u strojarstvu, 55 (2013), 2, 149-159.
  14. Lucia, U., Entropy and exergy in irreversible renewable energy systems. Renewable and Sustainable Energy Reviews, 20 (2013), 559-564.
  15. Ormos, M., Zibriczky, D., Entropy-based financial asset pricing, PloS one, 9 (2014), 12, e115742
  16. Van Erven, T., Harremos, P., Rényi divergence and Kullback-Leibler divergence, IEEE Transactions on Information Theory, 60 (2014), 7, 3797-3820.
  17. Azad, A. K., Rasul, M. G., Alam, M. M., Uddin, S. A., Mondal, S. K., Analysis of wind energy conversion system using Weibull distribution. Procedia Engineering, 90 (2014), 725-732.
  18. Niu, H., Wang, J., Quantifying complexity of financial short-term time series by composite multiscale entropy measure. Communications in Nonlinear Science and Numerical Simulation, 22 (2015), 1-3, 375-382.
  19. Dedu, S., Toma, A., An Integrated Risk Measure and Information Theory Approach for Modeling Financial Data and Solving Decision Making Problems, Procedia Economics and Finance, 22 (2015), 531-537.
  20. Sati, M. M., Gupta, N., "Some characterization results on dynamic cumulative residual Tsallis entropy", Journal of Probability and Statistics, 2015.
  21. Sheraz, M., Dedu, S., Preda, V., Entropy measures for assessing volatile markets. Procedia Economics and Finance, 22 (2015), 655-662.
  22. Stosic, D., Stosic, D., Ludermir, T., de Oliveira, W., Stosic, T., Foreign exchange rate entropy evolution during financial crises. Physica A: Statistical Mechanics and its Applications, 449 (2016), 233-239
  23. Ram, S. K., Kulia, G., Molinas, M., On wind Turbine failure detection from measurements of phase currents: a permutation entropy approach. arXiv preprint arXiv:1601.05387, 2016
  24. Shoaib, M., Siddiqui, I., Rehman, S., Rehman, S. U., Khan, S., & Lashin, A. Comparison of wind energy generation using the maximum entropy principle and the Weibull distribution function. Energies, 9 (2016), 10, 842
  25. Ponta, L., Carbone, A., Information measure for financial time series: quantifying short-term market heterogeneity, Physica A: Statistical Mechanics and its Applications, 2018.
  26. Khammar, A. H., Jahanshahi, S. M. A., On weighted cumulative residual Tsallis entropy and its dynamic version, Physica A: Statistical Mechanics and its Applications, 491 (2018), 678-692.

© 2020 Society of Thermal Engineers of Serbia. Published by the Vinča Institute of Nuclear Sciences, Belgrade, Serbia. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International licence