Machine Learning and Statistical Techniques for Daily Wind Energy Prediction

Year 2022, , 1359 - 1370, 01.12.2022

Lasini Wickramasinghe Piyal Ekanayake Jeevani Jayasinghe

https://doi.org/10.35378/gujs.961338

Cited By: 4

Abstract

This paper presents the development of wind energy prediction models for the Nala Danavi wind farm in Sri Lanka by using machine learning and statistical techniques. Wind speed and ambient temperature were used as the input variables in modeling while the daily wind energy production was the output variable. Correlation between the wind energy and each weather index was investigated using the Pearson’s and Spearman’s correlation coefficients and it was found that daily wind energy output is positively correlated with both daily averaged input variables. Statistical prediction models of Multiple Linear Regression (MLR) and Power Regression (PR) and the machine learning techniques of Support Vector Regression (SVR), Gaussian Process Regression (GPR), Feed Forward Backpropagation Neural Network (FFBPNN), Cascade-Forward Backpropagation Neural Network (CFBPNN) and Recurrent Neural Network (RNN) were developed. The accuracy of the prediction models was measured in terms of the coefficient of determination, Bias, Percent Root mean square error (RMSE)Bias, and Nash-Sutcliffe Efficiency (NSE). Results of the performance evaluation indicated that all the models are highly accurate while the FFBPNN-based model demonstrates outstanding performance with very low error. Such prediction models are highly important for a country like Sri Lanka whose power generation mainly depends on imported coal followed by hydropower and expanding the on-shore and off-shore wind farms gradually in many potential locations scattered over the country.

Keywords

Machine learning, Neural networks, Regression, Wind energy, Prediction models

References

• [1] Tian, Z., “A state-of-the-art review on wind power deterministic prediction”, Wind Engineering, 1-19, (2020).
• [2] Wang, Y., Zou, R., Liu, F., Zhang, L., Liu, Q., “A review of wind speed and wind power forecasting with deep neural networks”, Applied Energy, 304, 117766, (2021).
• [3] Chen, Q., Folly, K. A., “Wind power forecasting”, IFAC-PapersOnLine, 51(28): 414-419, (2018).
• [4] Dupré, A., Drobinski, P., Alonzo, B., Badosa, J., Briard, C., Plougonven, R., “Sub-hourly forecasting of wind speed and wind energy”, Renewable Energy, 145: 2373-2379, (2020).
• [5] Tena García, J. L., Cadenas Calderón, E., González Ávalos, G., Rangel Heras, E., Mbikayi Tshikala, A., “Forecast of daily output energy of wind turbine using sARIMA and nonlinear autoregressive models”, Advances in Mechanical Engineering, 11(2): 1-15, (2019).
• [6] Biswas, A. K., Ahmed, S. I., Bankefa, T., Ranganathan, P., Salehfar, H., “Performance Analysis of Short and Mid-Term Wind Power Prediction using ARIMA and Hybrid Models”, 2021 IEEE Power and Energy Conference at Illinois (PECI), Urbana, 1-7, (2021).
• [7] Ekanayake, P., Peiris, A. T., Jayasinghe, J. M., Rathnayake, U., “Development of wind power prediction models for Pawan Danavi wind farm in Sri Lanka”, Mathematical Problems in Engineering, 2021: (2021).
• [8] Naik, J., Dash, P. K., Dhar, S., “A multi-objective wind speed and wind power prediction interval forecasting using variational modes decomposition based Multi-kernel robust ridge regression”, Renewable Energy, 136: 701-731, (2019).
• [9] Liu, R., Peng, M., Xiao, X., “Ultra-short-term wind power prediction based on multivariate phase space reconstruction and multivariate linear regression”, Energies, 11(10): 2763, (2018).
• [10] Zafirakis, D., Tzanes, G., Kaldellis, J. K., “Forecasting of wind power generation with the use of artificial neural networks and support vector regression models”, Energy Procedia, 159: 509-514, (2019).
• [11] Treiber, N. A., Kramer, O., “Evolutionary feature weighting for wind power prediction with nearest neighbor regression”, 2015 IEEE Congress on Evolutionary Computation (CEC), Sendai, 332-337, (2015).
• [12] Jin, H., Shi, L., Chen, X., Qian, B., Yang, B., Jin, H., “Probabilistic wind power forecasting using selective ensemble of finite mixture Gaussian process regression models”, Renewable Energy, 174: 1-18, (2021).
• [13] Liu, T., Wei, H., Zhang, K., “Wind power prediction with missing data using Gaussian process regression and multiple imputation”, Applied Soft Computing, 71: 905-916, (2018).
• [14] Manero, J., Béjar, J., Cortés, U., “Wind energy forecasting with neural networks: A literature review”, Computación y Sistemas, 22(4): 1085-1098, (2018).
• [15] Narayana, M., Witharana, S., “Adaptive prediction of power fluctuations from a wind turbine at Kalpitiya area in Sri Lanka”, In 2012 IEEE 6th International Conference on Information and Automation for Sustainability Beijing, China, 262-265, (2012).
• [16] Olive, D.J., “Multiple linear regression”, In Linear regression, 17-83, Springer, Cham, (2017).
• [17] Welc, J., Esquerdo, P. J. R., “Regression Analysis of Discrete Dependent Variables”, In Applied Regression Analysis for Business, 213-227, (2018).
• [18] Awad, M., Khanna, R., Support vector regression, Apress, Berkeley, CA, 67-80, (2015).
• [19] Herfurth, H., “Gaussian Process Regression in Computational Finance”, Uppsala University, Sweden, (2020).
• [20] Gupta, N., “Artificial neural network”, Network and Complex Systems, 3(1): 24-28, (2013).
• [21] Rajput, N., Verma, S. K., “Back propagation feed forward neural network approach for speech recognition”, In Proceedings of 3rd International Conference on Reliability, Infocom Technologies and Optimization, Noida, India, 1-6, (2014).
• [22] Warsito, B., Santoso, R., Yasin, H., “Cascade forward neural network for time series prediction”, In Journal of Physics: Conference Series, 1025: 012097, (2018).
• [23] Apaydin, H., Feizi, H., Sattari, M. T., Colak, M. S., Shamshirband, S., Chau, K. W., “Comparative analysis of recurrent neural network architectures for reservoir inflow forecasting”, Water, 12(5): 1500, (2020).
• [24] Zhang, D., “A coefficient of determination for generalized linear models”, The American Statistician, 71(4): 310-316, (2017).
• [25] Zeybek, M., “Nash-sutcliffe efficiency approach for quality improvement”, Journal of Applied Mathematics and Computing (JAMC), 2(11): 496-503, (2018).