بهبود کیفیت مدل­‌سازی آماری نوسانات سرعت باد با استفاده از مدل­‌های گارچ و گارچ نامتقارن (ایستگاه هواشناسی اردبیل)

نوع مقاله : مقاله پژوهشی

نویسندگان

1 عضو هیات علمی گروه ریاضی، دانشکده فنی، واحدتهران جنوب دانشگاه ازاد اسلامی

2 کارشناس ارشد آمار، گروه ریاضی و آمار واحد تهران مرکزی، دانشگاه آزاد اسلامی، تهران، ایران.

3 استادیار، گروه ریاضی و آمار واحد تهران مرکزی، دانشگاه آزاد اسلامی، تهران، ایران.

چکیده

ایران به دلیل مجاورت با دریا و دارا بودن فلات‌های گوناگون، کشوری بادخیز است. آمارهای جهانی 30 سال گذشته حاکی از افزایش نیازهای انرژی جهان است و استفاده بهینه از منابع انرژی‌های تجدیدپذیر، از جمله انرژی باد برای تولید برق ارزان به ویژه براساس نگرش‌های زیست محیطی در بسیاری از کشورهای جهان رو به افزایش است. به دلیل ناپایداری انرژی باد استفاده از آن با چالش مواجه است که با مدل­سازی نوسانات سرعت باد، می‌توان این مشکل را کاهش داد. شهر اردبیل بادخیز و تحت تاثیر دو نوع باد محلی و جبهه‌ای است. در این مقاله داده‌های ثبت شده میانگین هفتگی سرعت باد در ایستگاه هواشناسی شهر اردبیل طی سال‌های 1395-1380 با استفاده از الگوهای سری زمانی، مدل­های گارچ (شامل مدل گارچ و مدل­های گارچ نامتقارن) مدل­سازی می‌شوند. بر اساس معیارهای اطلاع BIC, AIC, HQ ، بهترین مدل برای نوسانات سرعت باد در این ایستگاه طبق نتایج، مدل گارچ تعیین گردید. برای تجزیه و تحلیل داده­ها با روش مدل­سازی باکس-جنکینز، از دو نرم‌افزار Eviews  وR  استفاده می‌شود.

کلیدواژه‌ها


عنوان مقاله [English]

Improving the quality of statistical modeling of wind speed volatilities using GARCH and asymmetric GARCH models (Ardebil Meteorological Station)

نویسندگان [English]

  • Nasrin Akhoundi 1
  • bita molaabasi 2
  • Leila Golshani 3
1 a
2 1. Master of Statistics, Mathematics and Statistics Department, Central Tehran Branch, Islamic Azad University, Tehran, Iran.
3 Assistant Professor, Mathematics and Statistics Department, Central Tehran Branch, Islamic Azad University, Tehran, Iran.
چکیده [English]

Iran is a windy country due to its proximity to the sea and having various plateaus. The global statistics of the last 30 years indicate an increase in the world's energy needs. Therefore, the optimal use of renewable energy sources, including wind energy for electricity generation, especially based on environmental attitudes, is increasing in many countries of the world. But due to the instability of wind energy, its use faces a challenge, which can be effectively reduced by modeling wind speed volatilities. In this article, the weekly mean recorded data of wind speed in Ardabil meteorological station during 1380-1395 are modeled using time series GARCH models (including GARCH model and asymmetric GARCH models). Based on the Bayesian information criterion, the best model for wind speed volatilities in Ardabil meteorological station is the GARCH model. In this article, Box-Jenkins modeling method with Eviews and R software is used for data analysis.

کلیدواژه‌ها [English]

  • GARCH
  • Asymmetric GARCH
  • Autoregressive Moving Average
  • Wind Speed
[1] Rezaei, B., Javan, M, and Zainali, K. (2013). Investigating the trend of wind speed changes in northwest Iran, Journal of Natural Geography, (13), 27-36. [In Persian]
[2] Ebrahimi, S. and Ghafarzadeh, N. (2016). Short-term forecasting of wind speed using three types of combinations of neural networks based on division and combination, Journal of Renewable and New Energies, (1), 44-51. [In Persian]
[3] Mujiri, A. and Momeni, R. (2018). Modeling and forecasting wind speed in Zabul using statistical data (from 2008 to 2017), Dust conference in Southwest Asia. [In Persian]
[4] Malaabasi, B. and Golshani, L. (2019). Evaluation of Time Series Patterns for Wind Speed Volatilities in Anzali Meteorological Station, Iranian Journal of Official Statistics Studies, Vol. 31 (2), 461-476. [In Persian]
[5] Mohammadpour, M., Biorani, H. and Arabi Balaghi, R. (2021). Choosing the right statistical model for the wind speed of Tabriz and Urmia stations, Journal of Statistical Sciences, Vol. 15 (1), 219-232. [In Persian]
[6] Jan Nisari, F. and Islamian, S. (2022). Variable forecasting of wind speed in Zayandeh Rood basin using time series, New Research in Sustainable Water Engineering, Vol. 1 (1), 27-43. [In Persian]
[7] Tahamipour, M., Abedi, S., Karimi Baba Ahmadi, R. and Ebrahimizadeh, M. (2015). Investigating the effect of renewable energies on real economic growth per capita in Iran, Iranian Energy Economy Research Journal, 5th year, (91), 53-77. [In Persian]
[8] Ghafarpour, R. and Jam, A. R. (2015). The use of renewable energy sources in order to provide safe energy in sensitive centers, Iranian Energy Journal, Vol. 19 (3), 167-180. [In Persian]
[9] Akhbari, R. and Amadeh, H. (2017). Modeling and forecasting the state of air pollutants in the city of Tehran, using the self-regression model with long-term memory feature, Journal of Environmental Sciences and Technology, Vol. 20 (1), 41-57. [In Persian]
[10] Zakari, Z., Shakri, A. and Mohammadi, T. (2019). Choosing the right model in investigating the contagion of turbulence between financial markets among selected Islamic oil exporting countries, Quarterly Journal of Applied Economic Theories, 7th year, (3), 1-24. [In Persian]
[11] Kiqbadi, A. R. and Ahmadi, M. (2015). Comparing the effectiveness of GARCH and ARCH methods in predicting the value at risk for choosing the optimal portfolio, Journal of Financial Accounting and Auditing Research, 8th year, (63), 63-85. [In Persian]
[12] Ewing, B. T., Kruse, J. B, and Schroeder, J. L. (2006). Time series analysis of wind speed with time varying turbulence. Environ metrics, Vol. 17 (2), 119-127.
[13] Ewing, B. T, Kruse, J. B, and Thompson, M. A. (2008). Analysis of time-varying turbulence in geographically-dispersed wind energy markets. Energy Sources, Part B, Vol. 3 (4), 340-347.
[14] Payne, J. E. and Carroll, B. (2009). Modeling wind speed and time-varying turbulence in geographically dispersed wind energy markets in China. Energy Sources. Part A, Vol. 31 (19), 1759-1769.
[15] Li, H., Li, R. and Zhao, (2011). Wind speed forecasting based on autoregressive moving average- exponential generalized autoregressive conditional heteroscedasticity-generalized error distribution (ARMA-EGARCH-GED) model. International Journal of the Physical Sciences Vol. 6 (30), 6867 – 6871.
[16] Chen, H., Zhang, J., Tao, Y. and Tan, F. (2019). Asymmetric GARCH type models for asymmetric volatility characteristics analysis and wind power forecasting. Protection and Control of Modern Power Systems, 4-29
[17] Gupta, A., Kumar, A. and Boopathi, K. (2021). Day-ahead and intra-day wind power forecasting based on feedback error correction, IET Renewable Power Generation, Vol. 15 (1), 2840-2848.
[18] Engle, R. F. (1987). Estimating time varying risk premia in the term structure: The ARCH-M model. Econometrica, Vol. 55 (2), 391-407.
[19] Bollerslev, T. (1986). Generalized autoregressive conditional heteroscedasticity. Journal of econometrics, Vol. 31 (3), 307-327.
[20] Glosten, L. R, Jagannathan, R., and Runkle, D.E. (1993). On the relation between the expected value and the volatility of the nominal excess return on stocks. The journal of finance, Vol. 48 (5), 1779-1801.
[21] Souri, A. (2016). Econometrics with Stata & Eviews Application. Tehran: Culture Press. [in Persian]
[22] Nelson, D. B. (1991). Conditional heteroskedasticity in asset returns: A new approach Econometric. Journal of the Econometric Society, 347-370.
[23] Engle, R. F. and Ng, V. K. (1993). Measuring and testing the impact of news on volatility. The journal of finance, Vol. 48 (5), 1749-1778.
[24] Chakrabarti, A. and Ghosh, J.K. (2011). AIC, BIC and Recent Advances in Model Selection. In Handbook of the Philosophy of Science, Vol. 7, 583-605.