Forecasting of Short-Term and Long-Term Wind Speed of Ras Gharib Using Time Series Analysis

Osama Ahmed Elkashaty, Ahmed Ali Daoud, Elsaid Elsayed Elaraby

Abstract


The integration of renewable energy sources (RES) into the power grid has significantly grown in response to the rise in energy demand. Since wind energy tends to be intermittent and is continuously incorporated into the grid, it is crucial to properly forecast wind speed to maintain the reliability of the power system and the balance between supply and demand. In this study, using R-Studio software, daily and monthly data of wind speed of Ras Gharib-Egypt were modeled using statistical models (time series forecasting methods). The best parameters related to each method that result in the best model are identified through a careful examination of various values for each parameter. Then, using various error metrics calculated for each model, a process of model assessment is used to determine the best models among the investigated forecasting techniques. The precise models selected are extremely important because they can be employed to address both short-term operating issues and long-term planning concerns in the power system to get more trustworthy results over a variety of timescales.

Keywords


Forecasting, Statistical models, Time-Series analysis, Daily data, Monthly data, Wind speed.

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References


IEA (2022), Renewables 2022, IEA, Paris https://www.iea.org/reports/renewables-2022, License: CC BY 4.0.

International Trade Administration U.S. Department of Commerce, Country Commercial Guides, Egypt -Electricity and Renewable Energy (2022).

Graabak, Ingeborg, and Magnus Korpås. 2016. "Variability Characteristics of European Wind and Solar Power Resources—A Review" Energies 9, no. 6: 449. DOI.:10.3390/en9060449.

G. Sideratos and N. D. Hatziargyriou, "An Advanced Statistical Method for Wind Power Forecasting," in IEEE Transactions on Power Systems, vol. 22, no. 1, pp. 258-265, Feb. 2007, DOI: 10.1109/TPWRS.2006.889078 .

Lerner, Jeff & Grundmeyer, Michael & Garvert, Matt. (2009). The importance of wind forecasting. Renewable Energy Focus. 10. 64-66. DOI: 10.1016/S1755-0084(09)70092-4.

Ma Lei, Luan Shiyan, Jiang Chuanwen, Liu Hongling, Zhang Yan, A review on the forecasting of wind speed and generated power, Renewable and Sustainable Energy Reviews,Volume13,Issue4,2009,Pages915-920,ISSN1364-0321,DOI:10.1016/j.rser.2008.02.002 .

Xin Zhao, Shuangxin Wang, Tao Li,Review of Evaluation Criteria and Main Methods of Wind Power Forecasting, Energy Procedia, Volume12,2011,Pages761-769,ISSN 1876-6102, DOI:10.1016/j.egypro.2011.10.102.

Negnevitsky M, Potter CW. Innovative short-term wind generation pre-diction techniques. In: Proceedings of the Power Systems Conference and Exposition; 2006. p. 60–5.

Chang, W.-Y. (2014) A Literature Review of Wind Forecasting Methods. Journal of Power and Energy Engineering, 2, 161-168, DOI.:10.4236/jpee.2014.24023.

M.C Alexiadis, P.S Dokopoulos, H.S Sahsamanoglou, I.M Manousaridis,Short-term forecasting of wind speed and related electrical power,Solar Energy,Volume 63, Issue 1,1998, Pages 61-68,ISSN 0038-092X, DOI:10.1016/S0038-092X(98)00032-2.

Sfetsos A. A novel approach for the forecasting of mean hourly wind speed time series. Renewable Energy 2002;27(2):163–74.

Shi, J., Guo, J.M. and Zheng, S.T. (2012) Evaluation of Hybrid Forecasting Approaches for Wind Speed and Power Generation Time Series. Renewable and SustainableEnergyReviews,16,3471-3480, DOI:10.1016/j.rser.2012.02.044.

S. Shukla, R. Ramaprasad, S. Pasari and S. Sheoran, "Statistical Analysis and Forecasting of Wind Speed," 2022 4th International Conference on Energy, Power and Environment (ICEPE), 2022, pp. 1-6, DOI:10.1109/ICEPE55035.2022.9798358 .

Dhaheri, Khawla & Woon, Wei & Aung, Zeyar. (2017). Wind Speed Forecasting Using Statistical and Machine Learning Methods: A Case Study in the UAE. 107-120, DOI: 10.1007/978-3-319-71643-5_10 .

Cadenas, Erasmo & Rivera, Wilfrido. (2007). Rivera, W.: Wind speed forecasting in the South Coast of Oaxaca, México. Renew. Energy 32(12), 2116-2128. Renewable Energy. 32. 2116-2128, DOI: 10.1016/j.renene.2006.10.005 .

Grigonyt?, Ernesta & Butkevi?i?t?, Egl?. (2016). Short-term wind speed forecasting using ARIMA model. Energetika. 62, DOI: 10.6001/energetika. v62i1-2.3313.

Tyass, Ilham & Abdelouahad, Bellat & Raihani, Abdelhadi & Mansouri, Khalifa & Khalili, Tajeddine. (2022). Wind Speed Prediction Based on Seasonal ARIMA model. E3S Web of Conferences. 336. 00034. DOI: 10.1051/e3sconf/202233600034.

Cadenas, Erasmo, Wilfrido Rivera, Rafael Campos-Amezcua, and Christopher Heard. 2016. "Wind Speed Prediction Using a Univariate ARIMA Model and a Multivariate NARX Model" Energies 9, no. 2: 109, DOI.:10.3390/en9020109.

Wind Speed Forecasting using Time Series Analysis Methods. Çukurova University Journal of the Faculty of Engineering and Architecture, 32(2), pp. 161-172, June 2017.

M. Abotaleb, “Wind speed in England using BATS, TBATS, Holt’s Linear and ARIMA model”, MAUSAM, vol. 73, no. 1, pp. 129–138, Mar. 2022.

Yousuf, Muhammad Uzair & Al-Bahadly, Ibrahim & Avci, Ebubekir. (2021). Short-term wind speed forecasting based on hybrid MODWT-ARIMA-Markov model. IEEE Access. PP. 1-1. DOI: 10.1109/ACCESS.2021.3084536.

Alencar, David & Affonso, Carolina & Oliveira, Roberto & Reston Filho, José Carlos. (2018). Hybrid Approach Combining SARIMA and Neural Networks for Multi-Step Ahead Wind Speed Forecasting in Brazil. IEEE Access. PP. 1-1.DOI: 10.1109/ACCESS.2018. 2872720.

J. W. Taylor, P. E. McSharry and R. Buizza, "Wind Power Density Forecasting Using Ensemble Predictions and Time Series Models," in IEEE Transactions on Energy Conversion, vol. 24, no. 3, pp. 775-782, Sept. 2009, DOI: 10.1109/TEC.2009.2025431.

Jain, Garima. (2018). Time-Series analysis for wind speed forecasting. Malaya Journal of Matematik. S. 55-61.DOI: 10.26637/MJM0S01/11.

Arzu, Arieni & Kutty, Saiyad & Ahmed, M. Rafiuddin & Khan, M.G.M. (2018). Wind Speed Forecasting using Regression, Time Series and Neural Network Models: a Case Study of Kiribati. DOI: 10.14264/ccee311.

Chambers, John. (2007). Software for Data Analysis: Programming with R. DOI: 10.1007/978-0-387-75936-4.

Iwok, I.A., Okpe, A.S.: ‘A comparative study between univariate and multivariate linear stationary time series models’, American Journal of Mathematics and Statistics, 2016, 6, (5), pp. 203–212.

Hyndman, Rob J; Athanasopoulos, George. "8.9 Seasonal ARIMA models". Forecasting: principles and practice. oTexts. Retrieved 19 May 2015.

Young, P. C., Pedregal, D. J., & Tych, W. (1999). Dynamic harmonic regression. Journal of Forecasting, 18, 369–394.

Hyndman, R.J., & Athanasopoulos, G. (2018) Forecasting: principles and practice, 2nd edition, OTexts: Melbourne, Australia. OTexts.com/fpp2.

Gardner, E. S. (1985). Exponential smoothing: The state of the art. Journal of Forecasting, 4(1), 1–28, DOI:10.1002/for.3980040103.

Hyndman, Koehler, et al. (2002, “A State Space Framework for Automatic Forecasting Using Exponential Smoothing Methods,” International Journal of Forecasting, 18, 439–454.)

De Livera, A.M., Hyndman, R.J., & Snyder, R. D. (2011), Forecasting time series with complex seasonal patterns using exponential smoothing, Journal of the American Statistical Association, 106(496), 1513–1527.

NASA POWER | Data Access Viewer. NASA POWER | Data Access Viewer. Retrieved December 10, 2022, DOI: power.larc.nasa.gov/data-access-viewer/.

Lendave, V.What are autocorrelation and partial autocorrelation in time series data? Analytics India Magazine.

D. A. Dickey and W. A. Fuller, "Distribution of the estimators for autoregressive time series with a unit root," Journal of the American Statistical Association, vol. 74, no. 366a, pp. 427-431, 1979.

Dongin Lee, Peter Schmidt, On the power of the KPSS test of stationarity against fractionally-integrated alternatives, Journal of Econometrics, Volume 73, Issue 1, 1996, Pages 285-302, ISSN 0304-4076.

Bevans, R. (2022, July 9). The p-value explained. Scribbr. http://www.scribbr.com/statistics/p-value.




DOI (PDF): https://doi.org/10.20508/ijrer.v13i1.13785.g8680

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