Dört Farklı Metasezgisel Algoritma Kullanılarak Rüzgâr Hızı Olasılık Dağılımı Parametrelerinin Tahmini

Yıl 2022, , 1342 - 1362, 31.12.2022

Okan Oral , Murat İnce , Batin Latif Aylak , Mehmet Hakan Özdemir

https://doi.org/10.31202/ecjse.1135209

Öz

Güneş ve rüzgâr enerjisi gibi yenilenebilir enerji kaynaklarından (YEK) üretilen enerjinin mevcut enerji sistemlerine dahil edilmesi, karbon salınımlarını, hava kirliliğini ve iklim değişikliğini azaltmak ve sürdürülebilir bir kalkınmayı sağlamak için önemlidir. Ancak YEK’lerin enerji sistemine entegrasyonu, oldukça belirsiz ve kesintili yapıları nedeniyle hayli zordur. Bu çalışmada, İstanbul’da bulunan kentsel istasyonlardan elde edilen rüzgâr hızı verileri için, toplamda üç farklı olasılık yoğunluk fonksiyonu dikkate alınarak, Weibull olasılık yoğunluk fonksiyonunun ölçek ve şekil parametreleri, Rayleigh olasılık yoğunluk fonksiyonunun ölçek parametresi ve Gamma olasılık yoğunluk fonksiyonunun ölçek ve şekil parametreleri, Genetik Algoritma (GA), Diferansiyel Evrim (DE), Parçacık Sürü Optimizasyonu (PSO) ve Gri Kurt Optimizasyonu (GWO) algoritmaları olmak üzere dört farklı metasezgisel algoritma kullanılarak tahmin edilmiştir. Her istasyonda her bir olasılık yoğunluk fonksiyonu için ortalama mutlak hata (MAE), kök ortalama kare hata (RMSE) ve R2 değerleri hesaplanarak, rüzgâr hızı olasılık dağılımını en iyi karakterize eden olasılık yoğunluk fonksiyonu belirlenmiştir.

Anahtar Kelimeler

Rüzgâr hızı, yenilenebilir enerji, olasılık yoğunluk fonksiyonu, parametre tahmini, metasezgisel algoritma

Estimation of Wind Speed Probability Distribution Parameters by Using Four Different Metaheuristic Algorithms

Öz

The inclusion of energy produced from renewable energy sources (RES) such as solar and wind energy into existing energy systems is important to reduce carbon emissions, air pollution and climate change, and to ensure sustainable development. However, the integration of RES into the energy system is quite difficult due to their highly uncertain and intermittent nature. In this study, considering three different probability density functions (PDFs) in total, the scale and shape parameters of the Weibull PDF, the scale parameter of the Rayleigh PDF, and the scale and shape parameters of the Gamma PDF were estimated for the wind speed data obtained from urban stations located in Istanbul by using the four different metaheuristic algorithms, namely Genetic Algorithm (GA), Differential Evolution (DE), Particle Swarm Optimization (PSO) and Grey Wolf Optimization (GWO) algorithms. Calculating the mean absolute error (MAE), root mean squared error (RMSE), and R2 values for each PDF at each station, the PDF that characterizes the wind speed probability distribution the best was identified.

Anahtar Kelimeler

Wind speed, renewable energy, probability density function, parameter estimation, metaheuristic algorithm

Kaynakça

• Fyrippis, I., Axaopoulos, P. J., Panayiotou, G., “Wind energy potential assesment in Naxos Island, Greece. Applied Energy, 2010, 87(2), 577-586.
• Leung, D. Y., Yang, Y.,“Wind energy development and its environmental impact: A review”, Renewable and Sustainable Energy Reviews, 2012, 16(1): 1031-1039.
• Salameh, Z., Nandu, C. V., „Overview of building integrated wind energy conversion systems”, In IEEE PES General Meeting, 2010, 1-6, IEEE.
• Li, M., Li, X., “Investigation of wind characteristics and assessment of wind energy potential for Waterloo region, Canada”, Energy Conversion and Management, 2005, 46(18-19):3014-3033.
• Amaya-Martínez, P. A., Saavedra-Montes, A. J., Arango-Zuluaga, E. I., “A statistical analysis of wind speed distribution models in the Aburrá Valley, Colombia”, CT&F-Ciencia, Tecnología y Futuro, 2014, 5(5): 121-136.
• Calif, R., “PDF models and synthetic model for the wind speed fluctuations based on the resolution of Langevin equation”, Applied energy, 2012, 99: 173-182.
• Jiang, H., Wang, J., Wu, J., Geng, W., “Comparison of numerical methods and metaheuristic optimization algorithms for estimating parameters for wind energy potential assessment in low wind regions”, Renewable and Sustainable Energy Reviews, 2017, 69: 1199-1217.
• Alrashidi, M., Rahman, S., Pipattanasomporn, M., “Metaheuristic optimization algorithms to estimate statistical distribution parameters for characterizing wind speeds”, Renewable Energy, 2020, 149: 664-681.
• Akdağ, S. A., Dinler, A., “A new method to estimate Weibull parameters for wind energy applications”, Energy conversion and management, 2009, 50(7): 1761-1766.
• Pobočíková, I., Sedliačková, Z. Michalková, M., “Application of four probability distributions for wind speed modeling”, Procedia Engineering, 2017, 192: 713-718.
• Koca, M. B., Kilic, M. B., Şahin, Y., “Using genetic algorithms for estimating Weibull parameters with application to wind speed”, An International Journal of Optimization and Control: Theories & Applications (IJOCTA). 2020, 137-146.
• Wadi, M., Elmasry, W., “Statistical analysis of wind energy potential using different estimation methods for Weibull parameters: a case study”, Electrical Engineering, 2021, 103: 2573-2594.
• Kollu, R., Rayapudi, S. R., Narasimham, S. V. L., Pakkurthi, K. M., “Mixture probability distribution functions to model wind speed distributions”, International Journal of Energy and Environmental Engineering, 2012, 3(1): 1-10.
• Mazzeo, D., Oliveti, G. Labonia, E., “Estimation of wind speed probability density function using a mixture of two truncated normal distributions”, Renewable Energy, 2018, 115: 1260-1280
• Qin, Z., Li, W., Xiong, X., “Estimating wind speed probability distribution using kernel density method”, Electric Power Systems Research, 2011, 81(2): 2139-2146.
• Xu, X., Yan, Z., & Xu, S., “Estimating wind speed probability distribution by diffusion-based kernel density method”, Electric Power Systems Research, 2015, 121: 28-37,
• Miao, S., Xie, K., Yang, H., Karki, R., Tai, H. M., Chen, T., “A mixture kernel density model for wind speed probability distribution estimation”, Energy Conversion and Management 2016, 126: 1066-1083.
• Petković, D., Shamshirband, S., Anuar, N. B., Saboohi, H., Wahab, A. W. A, Protić, M., Zalnezhad, E., Mirhashemi, S. M. A., “An appraisal of wind speed distribution prediction by soft computing methodologies: A comparative study”, Energy Conversion and Management, 2014, 84: 133-139.
• Rocha, P. A. C., de Sousa, R. C., de Andrade, C. F., da Silva, M. E. V., “Comparison of seven numerical methods for determining Weibull parameters for wind energy generation in the northeast region of Brazil”, Applied Energy, 2012, 89(1): 395-400.
• Asghar, A. B., Liu, X. “Estimation of wind speed probability distribution and wind energy potential using adaptive neuro-fuzzy methodology”, Neurocomputing, 2018, 287: 58-67.
• Saleh, H., Aly, A. A. E. A., Abdel-Hady, S., “Assessment of different methods used to estimate Weibull distribution parameters for wind speed in Zafarana wind farm, Suez Gulf, Egypt, The International Conference on Applied Mechanics and Mechanical Engineering, 44(1): 710-719.
• Shu, Z. R., Jesson, M. “Estimation of Weibull parameters for wind energy analysis across the UK “, Journal of Renewable and Sustainable Energy 2021, 13, 023303(2021): 1-18.
• Gungor, A. Gokcek, M., Uçar, H., Arabaci, E. Akyüz, A., “Analysis of wind energy potential and Weibull parameter estimation methods: a case study from Turkey”, International Journal of Environmental Science and Technology, 2019, 17(2): 1011-1020.
• Akyuz, H. E., Gamgam, H., “Statistical analysis of wind speed data with Weibull, lognormal and gamma distributions”, Cumhuriyet Science Journal, 2017, 38(4): 68-76.
• Michalewicz, Z., “Genetic algorithms + data structures = evolution programs”, Springer-Verlag, New York, 1992.
• Holland, J. H., “Adaptation in natural and artificial systems”, University of Michigan Press, Ann Arbor, 1975.
• Kumar, M., Husain, M., Upreti, N., Gupta, D., “Genetic algorithm: review and application”, Available at SSRN, 2010.
• Katoch, S., Chauhan, S. S., Kumar, V., “A review on genetic algorithm: past, present, and future”, Multimedia Tools and Applications, 2021, 80(5): 8091-8126.
• Davis, L., “Handbook of genetic algorithms”, 1991.
• Srinivas, M., Patnaik, L. M., “Genetic algorithms: A survey”, Computer, 1994, 27(6): 17-26.
• Kennedy, J., Eberhart, R., “Particle swarm optimization”, In Proceedings of ICNN'95-international conference on neural networks, 1995, 4: 942-1948, IEEE.
• Shi, Y., Eberhart, R., “A modified particle swarm optimizer”, In 1998 IEEE international conference on evolutionary computation proceedings, IEEE world congress on computational intelligence, 98TH8360, 69-73, IEEE, 1998.
• Hassan, R., Cohanim, B., de Weck, O., Venter, G., “A comparison of particle swarm optimization and the genetic algorithm”, American Institute of Aeronautics and Astronautics, 2005, 1-13.
• Shi, Y., Particle swarm optimization. IEEE connections, 2004, 2(1): 8-13.
• Xie, Y., Gajewski, D., “3D CRS Attribute Search Using Particle Swarm Optimization”, Annual WIT report, 2016, 127-135.
• Storn, R., “Differrential evolution-a simple and efficient adaptive scheme for global optimization over continuous spaces”, Technical report, International Computer Science Institute, 1995, 11.
• Mallipeddi, R., Suganthan, P. N., Pan, Q. K., Tasgetiren, M. F., “Differential evolution algorithm with ensemble of parameters and mutation strategies”, Applied soft computing, 2011, 11(2): 1679-1696.
• Qin, A. K., Huang, V. L., Suganthan, P. N., “Differential evolution algorithm with strategy adaptation for global numerical optimization”. IEEE transactions on Evolutionary Computation, 2008, 13(2): 398-417.
• Pan, Q. K., Suganthan, P. N., Wang, L., Gao, L., Mallipeddi, R., “A differential evolution algorithm with self-adapting strategy and control parameters”, Computers & Operations Research, 2011, 38(1), 394-408.
• Mirjalili, S., Mirjalili, S. M., Lewis, A., “Grey wolf optimizer”, Advances in engineering software, 2014, 69: 46-61.
• Hu, P., Pan, J. S., Chu, S. C., “Improved binary grey wolf optimizer and its application for feature selection”, Knowledge-Based Systems, 2020, 195: 105746.
• İBB, Istanbul Metropolitan Municipality Open Data Portal Meteorology Observation Station Data Set, https://data.ibb.gov.tr/en/dataset/meteorology-observation-station-data-set, 2021.