Machine Learning and Statistical Techniques for Daily Wind Energy Prediction

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

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