Utilizing Physics-Based Input Features within a Machine Learning Model to Predict Wind Speed Forecasting Error [post]

Daniel Vassallo, Raghavendra Krishnamurthy, Harindra J. S. Fernando
2020 unpublished
<p><strong>Abstract.</strong> Machine learning is quickly becoming a commonly used technique for wind speed and power forecasting. Many of these methods utilize exogenous variables as input features, but there remains the question of which atmospheric variables provide the most predictive power, especially in handling non-linearities that lead to forecasting error. This investigation addresses this question via creation of a hybrid model that utilizes an autoregressive
more » ... oregressive integrated moving average (ARIMA) model to make an initial wind speed forecast followed by a random forest model that attempts to predict the ARIMA forecasting error using knowledge of exogenous atmospheric variables. Variables conveying information about atmospheric stability and turbulence as well as inertial forcing are found to be useful in dealing with non-linear error prediction. Wind direction (<i>θ</i>) and temperature (<i>T</i>) are found to be the most beneficial individual input features. Streamwise wind speed (<i>U</i>), time of day (<i>t</i>), turbulence intensity (<i>TI</i>), turbulent heat flux (<i>w</i>'<i>T</i>'), <i>θ</i>, and <i>T</i> are found to be particularly useful when used in conjunction.The prediction accuracy of the ARIMA-RF hybrid is compared to that of the persistence and bias-corrected ARIMA models. The ARIMA-RF model is shown to improve upon these commonly employed modeling methods, reducing hourly forecasting error by approximately 30 % below that of the bias-corrected ARIMA model.</p>
doi:10.5194/wes-2020-61 fatcat:sn3i72wvqnd4fborfvbda3pzcm