A COMPARATIVE ASSESSMENT OF MACHINE LEARNING MODELS FOR PREDICTING WIND SPEED
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Volume 5 (1), June 2022, Pages 57-71
Navid Atashfaraz1, Faezeh Gholamrezaie2, Arash Hosseini3 and Nigar Ismayilova4
Renewable energy is one of the most critical issues of continuously increasing electricity consumption which is becoming a desirable alternative to traditional methods of electricity generation such as coal or fossil fuels. This study aimed to develop, evaluate, and compare the performance of Linear multiple regression (MLR), support vector regression (SVR), Bagging and random forest (R.F.), and decision tree (CART) models in predicting wind speed in Southeastern Iran. The data used in this research is related to the statistics of 10 minutes of wind speed in 10-meter, 30-meter, and 40-meter wind turbines, the standard deviation of wind speed, air temperature, humidity, and amount of the Sun's radiation. The bagging and random forest model with an RMSE error of 0.0086 perform better than others in this dataset, while the MLR model with an RMSE error of 0.0407 has the worst.
Machine Learning, MLR, SVR, R.F., CART, Wind Speed Forecasting.
Chen, K., & Yu, J. (2014). Short-term wind speed prediction using an unscented Kalman filter based state-space support vector regression approach. Applied Energy, 113, 690-705.
Chen, K. Y., & Wang, C. H. (2007). Support vector regression with genetic algorithms in forecasting tourism demand. Tourism management, 28(1), 215-226.
Dong, L., Wang, L., et al. (2016). Wind power day-ahead prediction with cluster analysis of NWP. Renewable and Sustainable Energy Reviews, 60, 1206-1212.
Freedman, D. A. (2009). Statistical models: theory and practice. cambridge university press.
Genuer, R. (2010). Forêts aléatoires: aspects théoriques, sélection de variables et applications (Doctoral dissertation, Université Paris Sud-Paris XI).
Ghofrani, M., Mulcare, J., et al. (2017, February). A modified game theoretic self-organizing map for wind speed forecasting. In 2017 IEEE Power and Energy Conference at Illinois (PECI) (pp. 1-5). IEEE.
Gupta, D., Natarajan, N., & Berlin, M. (2022). Short-term wind speed prediction using hybrid machine learning techniques. Environmental Science and Pollution Research, 29(34), 50909-50927.
He, Q., Wang, J., & Lu, H. (2018). A hybrid system for short-term wind speed forecasting. Applied energy, 226, 756-771.
Heinermann, J., & Kramer, O. (2014, September). Precise wind power prediction with SVM ensemble regression. In International conference on artificial neural networks (pp. 797-804). Springer, Cham.
Heinermann, J., & Kramer, O. (2016). Machine learning ensembles for wind power prediction. Renewable Energy, 89, 671-679.
Lahouar, A., & Slama, J. B. H. (2017). Hour-ahead wind power forecast based on random forests. Renewable energy, 109, 529-541.
Liu, D., Wang, J., & Wang, H. (2015). Short-term wind speed forecasting based on spectral clustering and optimised echo state networks. Renewable Energy, 78, 599-608.
Liu, H., Tian, H. Q., Pan, D. F., & Li, Y. F. (2013). Forecasting models for wind speed using wavelet, wavelet packet, time series and Artificial Neural Networks. Applied Energy, 107, 191-208.
Lorenc, A. C. (1986). Analysis methods for numerical weather prediction. Quarterly Journal of the Royal Meteorological Society, 112(474), 1177-1194.
Lydia, M., Kumar, S. S., et al. (2016). Linear and non-linear autoregressive models for short-term wind speed forecasting. Energy conversion and management, 112, 115-124.
Ma, X., Jin, Y., & Dong, Q. (2017). A generalized dynamic fuzzy neural network based on singular spectrum analysis optimized by brain storm optimization for short-term wind speed forecasting. Applied Soft Computing, 54, 296-312.
Santamaría-Bonfil, G., Reyes-Ballesteros, A., & Gershenson, C. J. R. E. (2016). Wind speed forecasting for wind farms: A method based on support vector regression. Renewable Energy, 85, 790-809.
Schyska, B. U., Couto, A., et al. (2017). Weather dependent estimation of continent-wide wind power generation based on spatio-temporal clustering. Advances in science and research, 14, 131-138.
Sun, G., Jiang, C., et al. (2018). Short-term wind power forecasts by a synthetical similar time series data mining method. Renewable energy, 115, 575-584.
Wu, W., & Peng, M. (2017). A data mining approach combining $ K $-Means clustering with bagging neural network for short-term wind power forecasting. IEEE Internet of Things Journal, 4(4), 979-986.
Wu, X., & Kumar, V. (Eds.). (2009). The top ten algorithms in data mining. CRC press.
Xu, Q., He, D., et al. (2015). A short-term wind power forecasting approach with adjustment of numerical weather prediction input by data mining. IEEE Transactions on sustainable energy, 6(4), 1283-1291.
Yu, C., Li, Y., Xiang, H., & Zhang, M. (2018). Data mining-assisted short-term wind speed forecasting by wavelet packet decomposition and Elman neural network. Journal of Wind Engineering and Industrial Aerodynamics, 175, 136-143.
Zhao, X., Wang, S., & Li, T. (2011). Review of evaluation criteria and main methods of wind power forecasting. Energy Procedia, 12, 761-769.