Wind Power Prediction Based on Wind Velocity Variable and Polynomial Regression Method.

Abstract

Predicting the output energy obtained from wind velocity is required to investigate the wind power characteristics at potential locations and accurate evaluation of the power fluxes generating into the wind farms. This study provides a proposed approach to predict and assess wind power, using wind turbine parameters as a learning factor and daily wind velocity dataset of 8 cities in Iraq, namely Duhok, Mosul, Kirkuk, Baghdad, Najaf, Wasit, Qadisiyyah, and Basra. The nonlinear predictive models are established via the polynomial regression technique (POR) as a less complex machine learning method to extract potential wind energy from the wind turbine model (GhrepowerFD21-50_61.2kW_21.5). According to the outcomes, the scatter points of the wind power prediction values at all locations can follow the power curve output of the suggested turbine model with high accuracy and minimal error. Also, the 3 cities of Wasit, Qadisiyyah, and Basra have best total annual wind power quantities, with more than 2000 Kilowatt (kW) for each turbine unit, outperforming Duhok and Mosul that were less than 1000 kW. In addition, the POR technique was useful in extracting annual wind energy and estimation procedures for the regression method.

Keywords

• Artificial intelligence
• machine learning
• wind power

Polinom Regresyon Yöntemine Dayalı Rüzgar Gücü Tahmini.

Abstract

Rüzgar hızından elde edilen çıkış enerjisini tahmin etmek, potansiyel lokasyonlardaki rüzgar gücü karakteristiklerini araştırmak ve rüzgar çiftliklerine üretilen güç akılarının doğru bir şekilde değerlendirilmesi için gereklidir. Bu çalışma, öğrenme faktörü olarak rüzgar türbini parametrelerini ve Irak'taki 8 şehrin (Duhok, Musul, Kerkük, Bağdat, Necef, Vasit, Kadisiye ve Basra) günlük rüzgar hızı veri setini kullanarak rüzgar gücünü tahmin etmek ve değerlendirmek için önerilen bir yaklaşım sunmaktadır. Doğrusal olmayan tahmini modeller, rüzgar türbini modelinden (GhrepowerFD21-50_61.2kW_21.5) potansiyel rüzgar enerjisini çıkarmak için daha az karmaşık bir makine öğrenme yöntemi olarak polinom regresyon tekniği (POR) aracılığıyla kurulmuştur. Sonuçlara göre, tüm lokasyonlardaki rüzgar gücü tahmin değerlerinin dağılım noktaları, önerilen türbin modelinin güç eğrisi çıktısını yüksek doğruluk ve minimum hata ile takip edebilir. Ayrıca, Wasit, Qadisiyyah ve Basra'nın 3 şehri, her türbin ünitesi için 2000 Kilowatt'tan (kW) fazla olan en iyi toplam yıllık rüzgar gücü miktarlarına sahiptir ve 1000 kW'tan az olan Duhok ve Musul'u geride bırakmıştır. Ek olarak, POR tekniği, yıllık rüzgar enerjisini çıkarmada ve regresyon yöntemi için tahmin prosedürlerinde yararlıydı.

Keywords

• Yapay zeka
• makine öğrenmesi
• rüzgar enerjisi
• rüzgar hızı.

References

1. [1] Daily Loads. Retrieved March 10, 2023, from Ministry of Electricity: http://www.moelc.gov.iq
2. [2] Altai, Hisham Dawood Salman, Faisal Theyab Abed, Mohammed H. Lazim, and Haider TH Salim ALRikabi, “Analysis of the problems of electricity in Iraq and recommendations of methods of overcoming them, ” Periodicals of Engineering and Natural Sciences (PEN) 10, no. 1, 607-614, 2022.
3. [3] Bacci, A. (2017). Iraq Petroleum 2018—Natural Gas Must Be an Asset for Iraq.
4. [4] Mohammadi, Kasra, Omid Alavi, and Jon G. McGowan, “Use of Birnbaum-Saunders distribution for estimating wind speed and wind power probability distributions: A review,” Energy Conversion and Management, 143, 109-122, 2017.
5. [5] Song, Dongran, Yinggang Yang, Songyue Zheng, Xiaofei Deng, Jian Yang, Mei Su, Weiyi Tang, Xuebing Yang, Lingxiang Huang, and Young Hoon Joo. “New perspectives on maximum wind energy extraction of variable-speed wind turbines using previewed wind speeds,” Energy conversion and management, 206, 112496, 2020
6. [6] Wang, Jianzhou, Yuansheng Qian, Linyue Zhang, Kang Wang, and Haipeng Zhang, “A novel wind power forecasting system integrating time series refining, nonlinear multi-objective optimized deep learning and linear error correction,” Energy Conversion and Management, 299, 117818, 2024. https://doi.org/10.1016/j.enconman.2023.117818
7. [7] Alkesaiberi, Abdulelah, Fouzi Harrou, and Ying Sun, “Efficient wind power prediction using machine learning methods, A comparative study,” Energies 15, no. 7, 2327, 2022. https://doi.org/10.3390/en15072327
8. [8] Mi, Xiwei, Hui Liu, and Yanfei Li, “Wind speed prediction model using singular spectrum analysis, empirical mode decomposition and convolutional support vector machine,” Energy conversion and management 180, 196-205, 2019.

9. [9] Aldossary, Yasmeen, Nabil Hewahi, and Abdulla Alasaadi, “Wind Speed Forecasting Based on Data Decomposition and Deep Learning Models, A Case Study of a Wind Farm in Saudi Arabia,” arXiv preprint arXiv,2412.13356, 2024.
10. [10] Meng, Anbo, Shun Chen, Zuhong Ou, Weifeng Ding, Huaming Zhou, Jingmin Fan, and Hao Yin, “A hybrid deep learning architecture for wind power prediction based on bi-attention mechanism and crisscross optimization,” Energy 238, 121795, 2022. https://doi.org/10.1016/j.energy.2021.121795
11. [11] Wang, Jianing, Hongqiu Zhu, Yingjie Zhang, Fei Cheng, and Can Zhou, “A novel prediction model for wind power based on improved long short-term memory neural network,” Energy 265, 126283, 2023. https://doi.org/10.1016/j.energy.2022.126283
12. [12] Tarek, Zahraa, Mahmoud Y. Shams, Ahmed M. Elshewey, El-Sayed M. El-kenawy, Abdelhameed Ibrahim, Abdelaziz A. Abdelhamid, and Mohamed A. El-dosuky, “Wind Power Prediction Based on Machine Learning and Deep Learning Models,” Computers, Materials and Continua, 75, no. 1, 2023.
13. [13] Voyant, Cyril, Gilles Notton, Soteris Kalogirou, Marie-Laure Nivet, Christophe Paoli, Fabrice Motte, and Alexis Fouilloy, “Machine learning methods for solar radiation forecasting, A review,” Renewable energy 105, 569-582, 2017. https://doi.org/10.1016/j.renene.2016.12.095
14. [14] Al-Ansari, Nadhir, “Topography and climate of Iraq,” Journal of Earth Sciences and Geotechnical Engineering 11, no. 2, 1-13, 2021.
15. [15] Adeeb, H. Q., and Y. K. Al-Timimi, “GIS techniques for mapping of wind speed over Iraq,” Iraqi Journal of Agricultural Sciences 50, no. 6, 2019. https://doi.org/10.36103/ijas.v50i6.852
16. [16] Iraqi Meteorological Organization and Seismology (IMOS). http://www.meteoseism.gov.iq
17. [17] SWCC Small Wind Certification Council, “ICC-SWCC Summary Report SWCC-15-01-P,” Brea, CA,Small Wind Certification Council, 2014. http://smallwindcertification.org/wpcontent/uploads/2019/09/Summary-Report-SWCC-15- 01-2019-P.pdf.
18. [18] Ostertagova, Eva, “Modelling using polynomial regression,” Procedia engineering, 48, 500-506, 2012. https://doi.org/10.1016/j.proeng.2012.09.545
19. [19] Shi, Maolin, Weifei Hu, Muxi Li, Jian Zhang, Xueguan Song, and Wei Sun, “Ensemble regression based on polynomial regression-based decision tree and its application in the in-situ data of tunnel boring machine,” Mechanical Systems and Signal Processing, 188, 110022, 2023. https://doi.org/10.1016/j.ymssp.2022.110022
20. [20] Imran, Hamza, Nadia Moneem Al-Abdaly, Mohammed Hammodi Shamsa, Amjed Shatnawi, Majed Ibrahim, and Krzysztof Adam Ostrowski, “Development of prediction model to predict the compressive strength of eco-friendly concrete using multivariate polynomial regression combined with stepwise method,”Materials 15, no. 1, 317, 2022. https://doi.org/10.3390/ma15010317
21. [21] Alahmer, Ali, Hussein Alahmer, Ahmed Handam, and Hegazy Rezk, “Environmental assessment of a diesel engine fueled with various biodiesel blends, Polynomial regression and grey wolf optimization,” Sustainability, 14, no. 3, 1367, 2022. https://doi.org/10.3390/su14031367
22. [22] Saleh, Hussein H, “Estimating the amounts of the solar energy, A multiple linear regression approach,” In AIP Conference Proceedings, vol. 2591, no. 1. AIP Publishing, 2023. https://doi.org/10.1063/5.0125118