Machine Learning Implementation on Wind Speed Prediction
Abstract views: 85
/ 
PDF downloads: 47
Keywords:
Machine Learning, Long-Short Term Memory, Adaptive Neuro-Fuzzy Inference System, Wind Speed, Time-SeriesAbstract
In this study, the measured hub-height wind speed of a wind turbine is forecasted using machine learning. Accordingly, the wind speed data has been obtained from an installed wind turbine of a wind power plant that is located in the Republic of Kosovo. A cumulative of 2,000 wind speed data has been used in historical time-series predictions performed by long-short term memory, adaptive neuro-fuzzy inference system with fuzzy c-means (FCM), subtractive clustering (SC), and grid partitioning (GP). The results of 102 computed models have indicated that the best wind speed predictions have been obtained during the utilization of the ANFIS-SC algorithm. The accuracy of the predictions has been evaluated considering the mean absolute error (MAE), root mean square error (RMSE), as well as the correlation coefficient (R). In this context, it was determined that the SC tool of the ANFIS resulted the wind speed predictions with the superior statistical error outcomes corresponding to 0.2562 MAE, 0.3047 RMSE, and 0.9990 R.
References
A. Ilhan, “Forecasting of river water flow rate with machine learning,” Neural. Comput. Appl., vol. 34(22), pp. 20341–20363, Nov. 2022.
(2020) Global Wind Energy Council website. [Online]. Available: https://gwec.net/
S. Hochreiter and J. Schmidhuber, “Long short-term memory,” Neural Comput., vol. 9(8), pp. 1735-1780, Nov. 1997.
S. Zahroh, Y. Hidayat, R. S. Pontoh, A. Santoso, and A. T. Sukono Bon, “Modeling and forecasting daily temperature in Bandung,” in Proc. First GCC International Conference on Industrial Engineering and Operations Management, 2019, pp. 406–412.
A. G. Salman, Y. Heryadi, E. Abdurahman, and W. Suparta, “Single layer & multi-layer long short-term memory (LSTM) model with intermediate variables for weather forecasting,” Procedia Comput. Sci., vol. 135, pp. 89-98, 2018.
H. Z. Abyaneh, A. M. Nia, M. B. Varkeshi, S. Marofi, and O. Kisi, “Performance evaluation of ANN and ANFIS models for estimating garlic crop evapotranspiration,” J. Irrig. Drain. Eng., vol. 137, pp. 280-286, May 2011.
J. R. Jang, “ANFIS: adaptive-network-based fuzzy inference system,” IEEE Trans. Syst. Man. Cybern., vol. 23(3), pp. 665–685, 1993.
O. Karakus¸ E. E. Kuruoglu, and M. A. Altinkaya, “One-day ahead wind speed/power prediction based on polynomial autoregressive model,” IET Renew. Power Gener., vol. 11(11), pp. 1430–1439, Sep. 2017.
H. Tabari, O. Kisi, A. Ezani, and P. H. Talaee, “SVM, ANFIS, regression and climate based models for reference evapotranspiration modeling using limited climatic data in a semi-arid highland environment,” J. Hydrol., vol. 444-445, pp. 78-89, Jun. 2012.
(2020) Mathworks Long Short-term Memory Networks website. [Online]. Available: https://www.mathworks.com
K. Benmouiza and A. Cheknane, “Clustered ANFIS network using fuzzy c-means, subtractive clustering, and grid partitioning for hourly solar radiation forecasting,” Theor. Appl. Climatol., vol. 137, pp. 31-43, 2019.
