Wind speed analysis using Markov chain

Authors

DOI:

https://doi.org/10.33448/rsd-v10i9.17435

Keywords:

Petrolina; Wind energy; Modeling; Entropy.

Abstract

The search for renewable energy has increased in recent decades, mainly due to environmental concerns as well as the growing energy demand. Wind energy is one of the sources of renewable energy widely adopted in the world, with the northeast region of Brazil highlighted by its large production capacity. The implementation of wind farms lacks preliminary studies on statistical modeling of wind speed and, among the proposed methods, the Markov Chain is known to be efficient. This work presents a statistical analysis of the wind speed in the city of Petrolina - PE, with the objective to apply a first-order Markov chain, a probabilistic method, in the hourly wind speed modeling. Data were obtained from the website of the National Institute of Meteorology (INMET), corresponding to the period 2010 to 2020. First-order Markov chain with two states was used to find  transition probabilities, and anual variation of these probabilities and  normalized Entropy was analyzed. The results showed that the proposed model adjusts well to wind speed variations in specific months of the year and that models with more states can be proposed to reduce the loss of information calculated by the entropy value.

References

ABEEólica. (2018). Annual wind energy report. Recuperado em 12 de abril de 2021, de http://abeeolica.org.br/wp-content/uploads/2019/06/Boletim-Anual_2018_Inglês.pdf

Bagal, H. A., Soltanabad, Y. N., Dadjuo, M., Wakil, K., & Ghadimi, N. (2018). Risk-assessment of photovoltaic-wind-battery-grid based large industrial consumer using information gap decision theory. Solar Energy, 169, 343-352.doi.org/10.1016/j.solener.2018.05.003

Balzter, H. (2000). Markov chain models for vegetation dynamics. Ecological modelling, 126(2-3), 139-154. doi.org/10.1016/S0304-3800(00)00262-3

Carta, J. A., Ramirez, P., & Velazquez, S. (2009). A review of wind speed probability distributions used in wind energy analysis: Case studies in the Canary Islands. Renewable and sustainable energy reviews, 13(5), 933-955. doi.org/10.1016/j.rser.2008.05.005

Chitsaz, H., Amjady, N., & Zareipour, H. (2015). Wind power forecast using wavelet neural network trained by improved Clonal selection algorithm. Energy conversion and Management, 89, 588-598.doi.org/10.1016/j.enconman.2014.10.001

de Figueirêdo, B. C. L., Moreira, G. R., Stosic, B., & Stosic, T. (2014). Multifractal analysis of hourly wind speed records in Petrolina, Northeast Brazil. Revista Brasileira de Biometria, 32(4), 599-608. Recuperado em 8 de abril de 2021, de http://jaguar.fcav.unesp.br/RME/fasciculos/v32/v32_n4/A9_Barbara.pdf

Ettoumi, F. Y., Sauvageot, H., & Adane, A. E. H. (2003). Statistical bivariate modelling of wind using first-order Markov chain and Weibull distribution. Renewable energy, 28(11), 1787-1802.doi.org/10.1016/S0960-1481(03)00019-3

Global Wind Energy Council. (2019). Global wind report: Annual market update. Recuperado em 3 de junho de 2020, de https://gwec.net/global-wind-report-2019

Global Wind Energy Council. (2021). Global Wind Report, 2021. Recuperado em 8 de abril de 2021, de https://gwec.net/global-wind-report-2021/

Wickham, H. ggplot2: Elegant Graphics for Data Analysis. Springer-Verlag New York, 2016.

INMET - Instituto Nacional de Meteorologia, https://portal.inmet.gov.br/dadoshistoricos (Último acesso em 29 de abril de 2021)

Jiao, R., Huang, X., Ma, X., Han, L., & Tian, W. (2018). A model combining stacked auto encoder and back propagation algorithm for short-term wind power forecasting. Ieee Access, 6, 17851-17858. doi: 10.1109/ACCESS.2018.2818108.

Jung, C., & Schindler, D. (2019). Wind speed distribution selection–A review of recent development and progress. Renewable and Sustainable Energy Reviews, 114, 109290.doi.org/10.1016/j.rser.2019.109290

Kaygusuz, K. (2012). Energy for sustainable development: A case of developing countries. Renewable and Sustainable Energy Reviews, 16(2), 1116-1126.doi.org/10.1016/j.rser.2011.11.013

Masseran, N. (2015). Markov chain model for the stochastic behaviors of wind-direction data. Energy conversion and management, 92, 266-274.doi.org/10.1016/j.enconman.2014.12.045

Poggi, P., Muselli, M., Notton, G., Cristofari, C., & Louche, A. (2003). Forecasting and simulating wind speed in Corsica by using an autoregressive model. Energy conversion and management, 44(20), 3177-3196.doi.org/10.1016/S0196-8904(03)00108-0

Ruffato-Ferreira, V., Barreto, R. C., Júnior, A. O., Silva, W. L., Viana, D. B., Nascimento, J. A. S., & Freitas, M. A. V. (2017). A foundation for the strategic long-term planning of the renewable energy sector in Brazil: Hydroelectricity and wind energy in the face of climate change scenarios. Renewable and Sustainable Energy Reviews, 72, 1124-1137.doi.org/10.1016/j.rser.2016.10.020

Sadorsky, P. (2021). Wind energy for sustainable development: Driving factors and future outlook. Journal of Cleaner Production, 289, 125779.doi.org/10.1016/j.jclepro.2020.125779

Sarmiento, C., Valencia, C., & Akhavan-Tabatabaei, R. (2018). Copula autoregressive methodology for the simulation of wind speed and direction time series. Journal of Wind Engineering and Industrial Aerodynamics, 174, 188-199. doi.org/10.1016/j.jweia.2018.01.009

Shamshad, A., Bawadi, M. A., Hussin, W. W., Majid, T. A., & Sanusi, S. A. M. (2005). First and second order Markov chain models for synthetic generation of wind speed time series. Energy, 30(5), 693-708. doi.org/10.1016/j.energy.2004.05.026

Simas, M., & Pacca, S. (2013). Energia eólica, geração de empregos e desenvolvimento sustentável. Estudos avançados, 27(77), 99-116. doi.org/10.1590/S0103-40142013000100008

Spedicato, G. A. Discrete Time Markov Chains with R. The R Journal, 2017.

Van Kooten, G. C., Duan, J., & Lynch, R. (2016). Is there a future for nuclear power? Wind and emission reduction targets in fossil-fuel Alberta. PloS one, 11(11), e0165822. https://doi.org/10.1371/journal.pone.0165822

Xie, K., Liao, Q., Tai, H. M., & Hu, B. (2017). Non-homogeneous Markov wind speed time series model considering daily and seasonal variation characteristics. IEEE Transactions on Sustainable Energy, 8(3), 1281-1290. doi: 10.1109/TSTE.2017.2675445

Published

20/07/2021

How to Cite

NUNES, J. E. de O.; SOUZA NETO, J. V. de .; SILVA, M. M. de L. .; CORDEIRO, N. M. .; PESSOA, R. . V. S. .; BARRETO, I. D. de C.; BEJAN, L. B. .; STOSIC, T. Wind speed analysis using Markov chain. Research, Society and Development, [S. l.], v. 10, n. 9, p. e3610917435, 2021. DOI: 10.33448/rsd-v10i9.17435. Disponível em: https://rsdjournal.org/index.php/rsd/article/view/17435. Acesso em: 23 sep. 2021.

Issue

Section

Exact and Earth Sciences