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Monthly Average Wind Speed Forecasting in Giresun Province with Fuzzy Regression Functions Approach

Year 2022, Volume: 7 Issue: 1, 27 - 32, 31.03.2022
https://doi.org/10.35229/jaes.1022200

Abstract

In recent years, fuzzy inference systems have been used as an effective method for forecasting problems instead of classical time series methods. Fuzzy inference systems are based on fuzzy sets and use membership values as well as the original data. The fuzzy regression functions approach, which is one of the popular fuzzy inference systems, has different importance from many fuzzy inference systems with its features that it does not have a rule base and is easier to apply, unlike many fuzzy inference systems in the literature. In this study, both the monthly average wind speed forecasting of Giresun Province is performed for the first time in the literature and the fuzzy regression functions approach method is used for the first time in the literature for wind speed forecasting. To evaluate the performance of the fuzzy regression functions approach used to forecast monthly average wind speed in Giresun Province, the results obtained from many methods suggested in the literature for forecasting problems are compared. As a result of the evaluations, it is concluded that the forecasts obtained by the fuzzy regression functions approach are superior than some other methods in the literature.

References

  • Akıncı, T. C. (2011). Short term wind speed forecasting with ANN in Batman. Turkey. Elektronika Ir Elektrotechnika, 107, 41-45.
  • Alexiadis, M. C., Dokopoulos, P.S., Sahsamanoglou, H.S., & Manousaridis, I. M. (1998). Short-term forecasting of wind speed and related electrical power. Solar Energy, 63, 61-68.
  • Bezdek, J. C. (1981). Pattern recognition with fuzzy objective function algorithms. Plenum Press, NewYork, USA.
  • Bisht, K., & Kumar, S. (2019). Hesitant fuzzy set based computational method for financial time series forecasting. Granular Computing, 4(4), 655-669.
  • Cadenas, E., & Rivera, W. (2007). Wind speed forecasting in the south coast of Oaxaca, Mexico. Renewable Energy, 32(12), 2116-2128.,
  • Cadenas, E., & Rivera, W. (2010). Wind speed forecasting in three different regions of Mexico, using a hybrid ARIMA–ANN model. Renewable Energy, 35(12), 2732-2738.
  • Cadenas, E., Rivera, W., Campos-Amezcua, R., & Heard, C. (2016). Wind speed prediction using a univariate ARIMA model and a multivariate NARX model. Energies, 9(2), 109.
  • Celikyilmaz, A., Turksen, I. B. (2009). Modeling uncertainty with Fuzzy Logic, Studies in Fuzziness and Soft Computing Springer.
  • Chen, S. M. (1996). Forecasting enrollments based on fuzzy time-series. Fuzzy Sets and Systems, 81, 311-319.
  • Chen, S. M., & Hsu, C. C. (2008). A new approach for handling forecasting problems using high-order fuzzy time series. Intelligent Automation & Soft Computing, 14(1), 29-43.
  • Chen, S. M., & Jian, W. S. (2017). Fuzzy forecasting based on two-factors second-order fuzzy-trend logical relationship groups, similarity measures and PSO techniques. Information Sciences, 391, 65-79.
  • Chen, S. M., Manalu, G. M. T., Pan, J., & Liu, H. (2013). Fuzzy forecasting based on two-factors second-order fuzzy-trend logical relationship groups and particle swarm optimization techniques. IEEE Transactions on Cybernetics, 43(3), 1102-1117.
  • Chen, S. M, & Phuong, B. D. H. (2016). Fuzzy time series forecasting based on optimal partitions of intervals and optimal weighting vectors. Knowledge-Based Systems, 118, 204-216.
  • Chen, S. M, & Wang, N. (2010). Fuzzy forecasting based on fuzzy-trend logical relationship groups. IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), 40(5), 1343-1358.
  • Egrioglu, E., Yolcu, U., & Bas, E. (2019). Intuitionistic high-order fuzzy time series forecasting method based on pi-sigma artificial neural networks trained by artificial bee colony. Granular Computing, 4(4), 639-654.
  • Egrioglu, E., Yolcu, U., Bas, E., & Dalar, A. Z. (2019). Median-Pi artificial neural network for forecasting. Neural Computing and Applications, 31(1), 307-316.
  • Erdem, E., & Shi, J. (2011). ARMA based approaches for forecasting the tuple of wind speed and direction. Applied Energy, 88(4), 1405-1414.
  • Ewing, B. T., Kruse, J. B., Schroeder, J. L., & Smith, D. A. (2007). Time series analysis of wind speed using VAR and the generalized impulse response technique. Journal of Wind Engineering and Industrial Aerodynamics, 95, 209- 219.
  • Fazelpour, F., Tarashkar, N., & Rosen, M. A. (2016). Short-term wind speed forecasting using artificial neural networks for Tehran, Iran. International Journal of Energy and Environmental Engineering, 7(4), 377-390.
  • Guo, Z. H, Wu, J, Lu, H. Y., & Wang, J. Z. (2011). A case study on a hybrid wind speed forecasting method using BP neural network. Knowledge-Based Systems, 24, 1048-1056.
  • Gupta, K. K., & Kumar, S. (2019.) A novel high-order fuzzy time series forecasting method based on probabilistic fuzzy sets. Granular Computing, 4(4), 699-713.
  • Jang, J. S. R. (1993). ANFIS: Adaptive network based fuzzy inference system. IEEE Trans. On system, Man and Cybernetics, 23(3), 665-685.
  • Jaramillo, J., Velasquez, J. D., & Franco, C. J. (2017). Research in financial time series forecasting with SVM: Contributions from literature. IEEE Latin America Transactions, 15(1), 145-153.
  • Jiang, P., Ge, Y., & Wang, C. (2016). Research and application of a hybrid forecasting model based on simulation annealing algorithm: A Case study of wind speed forecasting. Journal of Renewable and Sustainable Energy, 8(1), 015501.
  • Jiang, P., Wang, Y., & Wang, J. (2017). Short-term wind speed forecasting using a hybrid model. Energy, 119, 561-577.
  • Khashei, M., & Bijari, M. (2012). A new class of hybrid models for time series forecasting. Expert Systems with Applications, 39(4), 4344-4357.
  • Mamdani, E. H., & Assilian, S. (1975). An experiment in linguistic synthesis with a fuzzy logic controller. International Journal of Man-Machine Studies, 7(1), 1–13.
  • Pant, M., & Kumar, S. (2021). Particle swarm optimization and intuitionistic fuzzy set-based novel method for fuzzy time series forecasting. Granular Computing, 1-19.
  • Qian, Z., Pe, Y., Zareipour, H., & Chen, N. (2019). A review and discussion of decomposition-based hybrid models for wind energy forecasting applications. Applied Energy, 235, 939-953.
  • Ren, C., An, N., Wan, J., Li, L., Hu, B., & Shang, D. (2014). Optimal parameters selection for BP neural network based on particle swarm optimization: A case study of wind speed forecasting. Knowledge-Based Systems, 56, 226-239.
  • Rezaeianzadeh, M., Tabari, H., Yazdi, A. A., Isik, S., & Kalin, L. (2014). Flood flow forecasting using ANN, ANFIS and regression models. Neural Computing and Applications, 25(1), 25-37.
  • Saberivahidaval, M., & Hajjam, S. (2015). Comparison between performances of different neural networks for wind speed forecasting in Payam Airport, Iran. Environmental Progress and Sustainable Energy, 34(4), 1191- 1196.
  • Selcuk Nogay, H., Akinci, T. C., & Eidukeviciute M. (2012). Application of artificial neural networks for short term wind speed forecasting in Mardin. Turkey. Journal of Energy in Southern Africa, 23(4), 2-7.
  • Sfetsos, A. (2002). A novel approach for the forecasting of mean hourly wind speed time series. Renewable Energy, 27, 163-174.
  • Shi, J., Guo, J. M., & Zheng, S. T. (2012). Evaluation of hybrid forecasting approaches for wind speed and power generation time series. Renewable and Sustainable Energy Reviews, 16, 3471-3480.
  • Takagi, T., & Sugeno, M. (1985). Fuzzy identification of systems and its applications to modeling and control. Man and Cybernetics, 15,116-132.
  • Turksen, I. B. (2008). Fuzzy function with LSE. Applied Soft Computing, 8, 1178-1188.
  • Wang, J., Zhang, W., Li, Y., Wang, J., & Dang, Z. (2014). Forecasting wind speed using empirical mode decomposition and Elman neural network. Applied Soft Computing, 23, 452-459.
  • Yolcu, U., Aladag, C. H., & Egrioglu, E. (2013). A new linear & nonlinear artificial neural network model for time series forecasting. Decision Support System Journals, 54, 1340-1347.
  • Zhang, G. P. (2003). Time series forecasting using a hybrid ARIMA and neural network model. Neurocomputing, 50, 159-175.
  • Zucatelli, PJ, et al. (2019). Short-term wind speed forecasting in Uruguay using computational intelligence. Heliyon, 5(5), e01664.

Bulanık Regresyon Fonksiyonları Yaklaşımı ile Giresun İli Aylık Ortalama Rüzgâr Hızı Tahmini

Year 2022, Volume: 7 Issue: 1, 27 - 32, 31.03.2022
https://doi.org/10.35229/jaes.1022200

Abstract

Son yıllarda öngörü problemleri için klasik zaman serisi yöntemleri yerine bulanık çıkarım sistemleri etkin bir yöntem olarak kullanılmaya başlanmıştır. Bulanık çıkarım sistemleri, bulanık kümelere dayalıdır ve orijinal verilerin yanı sıra üyelik değerlerini de kullanır. Popüler bulanık çıkarım sistemlerinden biri olan bulanık regresyon fonksiyonları yaklaşımı, literatürdeki birçok bulanık çıkarım sisteminden farklı olarak kural tabanına sahip olmaması ve uygulanmasının daha kolay olması özellikleriyle birçok bulanık çıkarım sisteminden farklı bir öneme sahiptir. Bu çalışmada hem literatürde ilk kez Giresun ilinin aylık ortalama rüzgâr hızı tahmini yapılmakta hem de rüzgâr hızı tahmini için literatürde ilk kez bulanık regresyon fonksiyonları yaklaşımı yöntemi kullanılmaktadır. Giresun ili aylık ortalama rüzgar hızını tahmin etmek için kullanılan bulanık regresyon fonksiyonları yaklaşımının performansını değerlendirmek için, öngörü problemleri için literatürde önerilen birçok yöntemden elde edilen sonuçlar karşılaştırılmıştır. Yapılan değerlendirmeler sonucunda, bulanık regresyon fonksiyonları yaklaşımı ile elde edilen tahminlerin literatürdeki diğer birçok yöntemden daha üstün olduğu sonucuna varılmıştır.

References

  • Akıncı, T. C. (2011). Short term wind speed forecasting with ANN in Batman. Turkey. Elektronika Ir Elektrotechnika, 107, 41-45.
  • Alexiadis, M. C., Dokopoulos, P.S., Sahsamanoglou, H.S., & Manousaridis, I. M. (1998). Short-term forecasting of wind speed and related electrical power. Solar Energy, 63, 61-68.
  • Bezdek, J. C. (1981). Pattern recognition with fuzzy objective function algorithms. Plenum Press, NewYork, USA.
  • Bisht, K., & Kumar, S. (2019). Hesitant fuzzy set based computational method for financial time series forecasting. Granular Computing, 4(4), 655-669.
  • Cadenas, E., & Rivera, W. (2007). Wind speed forecasting in the south coast of Oaxaca, Mexico. Renewable Energy, 32(12), 2116-2128.,
  • Cadenas, E., & Rivera, W. (2010). Wind speed forecasting in three different regions of Mexico, using a hybrid ARIMA–ANN model. Renewable Energy, 35(12), 2732-2738.
  • Cadenas, E., Rivera, W., Campos-Amezcua, R., & Heard, C. (2016). Wind speed prediction using a univariate ARIMA model and a multivariate NARX model. Energies, 9(2), 109.
  • Celikyilmaz, A., Turksen, I. B. (2009). Modeling uncertainty with Fuzzy Logic, Studies in Fuzziness and Soft Computing Springer.
  • Chen, S. M. (1996). Forecasting enrollments based on fuzzy time-series. Fuzzy Sets and Systems, 81, 311-319.
  • Chen, S. M., & Hsu, C. C. (2008). A new approach for handling forecasting problems using high-order fuzzy time series. Intelligent Automation & Soft Computing, 14(1), 29-43.
  • Chen, S. M., & Jian, W. S. (2017). Fuzzy forecasting based on two-factors second-order fuzzy-trend logical relationship groups, similarity measures and PSO techniques. Information Sciences, 391, 65-79.
  • Chen, S. M., Manalu, G. M. T., Pan, J., & Liu, H. (2013). Fuzzy forecasting based on two-factors second-order fuzzy-trend logical relationship groups and particle swarm optimization techniques. IEEE Transactions on Cybernetics, 43(3), 1102-1117.
  • Chen, S. M, & Phuong, B. D. H. (2016). Fuzzy time series forecasting based on optimal partitions of intervals and optimal weighting vectors. Knowledge-Based Systems, 118, 204-216.
  • Chen, S. M, & Wang, N. (2010). Fuzzy forecasting based on fuzzy-trend logical relationship groups. IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), 40(5), 1343-1358.
  • Egrioglu, E., Yolcu, U., & Bas, E. (2019). Intuitionistic high-order fuzzy time series forecasting method based on pi-sigma artificial neural networks trained by artificial bee colony. Granular Computing, 4(4), 639-654.
  • Egrioglu, E., Yolcu, U., Bas, E., & Dalar, A. Z. (2019). Median-Pi artificial neural network for forecasting. Neural Computing and Applications, 31(1), 307-316.
  • Erdem, E., & Shi, J. (2011). ARMA based approaches for forecasting the tuple of wind speed and direction. Applied Energy, 88(4), 1405-1414.
  • Ewing, B. T., Kruse, J. B., Schroeder, J. L., & Smith, D. A. (2007). Time series analysis of wind speed using VAR and the generalized impulse response technique. Journal of Wind Engineering and Industrial Aerodynamics, 95, 209- 219.
  • Fazelpour, F., Tarashkar, N., & Rosen, M. A. (2016). Short-term wind speed forecasting using artificial neural networks for Tehran, Iran. International Journal of Energy and Environmental Engineering, 7(4), 377-390.
  • Guo, Z. H, Wu, J, Lu, H. Y., & Wang, J. Z. (2011). A case study on a hybrid wind speed forecasting method using BP neural network. Knowledge-Based Systems, 24, 1048-1056.
  • Gupta, K. K., & Kumar, S. (2019.) A novel high-order fuzzy time series forecasting method based on probabilistic fuzzy sets. Granular Computing, 4(4), 699-713.
  • Jang, J. S. R. (1993). ANFIS: Adaptive network based fuzzy inference system. IEEE Trans. On system, Man and Cybernetics, 23(3), 665-685.
  • Jaramillo, J., Velasquez, J. D., & Franco, C. J. (2017). Research in financial time series forecasting with SVM: Contributions from literature. IEEE Latin America Transactions, 15(1), 145-153.
  • Jiang, P., Ge, Y., & Wang, C. (2016). Research and application of a hybrid forecasting model based on simulation annealing algorithm: A Case study of wind speed forecasting. Journal of Renewable and Sustainable Energy, 8(1), 015501.
  • Jiang, P., Wang, Y., & Wang, J. (2017). Short-term wind speed forecasting using a hybrid model. Energy, 119, 561-577.
  • Khashei, M., & Bijari, M. (2012). A new class of hybrid models for time series forecasting. Expert Systems with Applications, 39(4), 4344-4357.
  • Mamdani, E. H., & Assilian, S. (1975). An experiment in linguistic synthesis with a fuzzy logic controller. International Journal of Man-Machine Studies, 7(1), 1–13.
  • Pant, M., & Kumar, S. (2021). Particle swarm optimization and intuitionistic fuzzy set-based novel method for fuzzy time series forecasting. Granular Computing, 1-19.
  • Qian, Z., Pe, Y., Zareipour, H., & Chen, N. (2019). A review and discussion of decomposition-based hybrid models for wind energy forecasting applications. Applied Energy, 235, 939-953.
  • Ren, C., An, N., Wan, J., Li, L., Hu, B., & Shang, D. (2014). Optimal parameters selection for BP neural network based on particle swarm optimization: A case study of wind speed forecasting. Knowledge-Based Systems, 56, 226-239.
  • Rezaeianzadeh, M., Tabari, H., Yazdi, A. A., Isik, S., & Kalin, L. (2014). Flood flow forecasting using ANN, ANFIS and regression models. Neural Computing and Applications, 25(1), 25-37.
  • Saberivahidaval, M., & Hajjam, S. (2015). Comparison between performances of different neural networks for wind speed forecasting in Payam Airport, Iran. Environmental Progress and Sustainable Energy, 34(4), 1191- 1196.
  • Selcuk Nogay, H., Akinci, T. C., & Eidukeviciute M. (2012). Application of artificial neural networks for short term wind speed forecasting in Mardin. Turkey. Journal of Energy in Southern Africa, 23(4), 2-7.
  • Sfetsos, A. (2002). A novel approach for the forecasting of mean hourly wind speed time series. Renewable Energy, 27, 163-174.
  • Shi, J., Guo, J. M., & Zheng, S. T. (2012). Evaluation of hybrid forecasting approaches for wind speed and power generation time series. Renewable and Sustainable Energy Reviews, 16, 3471-3480.
  • Takagi, T., & Sugeno, M. (1985). Fuzzy identification of systems and its applications to modeling and control. Man and Cybernetics, 15,116-132.
  • Turksen, I. B. (2008). Fuzzy function with LSE. Applied Soft Computing, 8, 1178-1188.
  • Wang, J., Zhang, W., Li, Y., Wang, J., & Dang, Z. (2014). Forecasting wind speed using empirical mode decomposition and Elman neural network. Applied Soft Computing, 23, 452-459.
  • Yolcu, U., Aladag, C. H., & Egrioglu, E. (2013). A new linear & nonlinear artificial neural network model for time series forecasting. Decision Support System Journals, 54, 1340-1347.
  • Zhang, G. P. (2003). Time series forecasting using a hybrid ARIMA and neural network model. Neurocomputing, 50, 159-175.
  • Zucatelli, PJ, et al. (2019). Short-term wind speed forecasting in Uruguay using computational intelligence. Heliyon, 5(5), e01664.
There are 41 citations in total.

Details

Primary Language English
Journal Section Articles
Authors

Abdullah Yıldırım This is me 0000-0002-5489-7330

Eren Baş 0000-0002-0263-8804

Publication Date March 31, 2022
Submission Date November 11, 2021
Acceptance Date January 13, 2022
Published in Issue Year 2022 Volume: 7 Issue: 1

Cite

APA Yıldırım, A., & Baş, E. (2022). Monthly Average Wind Speed Forecasting in Giresun Province with Fuzzy Regression Functions Approach. Journal of Anatolian Environmental and Animal Sciences, 7(1), 27-32. https://doi.org/10.35229/jaes.1022200


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