Wind Speed at Hub Height (Using Dynamic Wind Shear) and Wind Power Prediction

Abstract

AbstractPrediction of hub-height wind speed with the ground-level (10 m) wind speed is difficult as the wind is chaotic. Several forecasters provide wind speed forecasts, but due to variations in hub heights, conversion of a hub-height wind speed is challenging. At present, lots of research is going on to predict the wind speed by using mathematical formulae and statistics, and biologically inspired computing have also been used to predict particular height wind speed. Weather parameter affects the accuracy and increases the error band. To solve this issue, the models have been created based on the Decision Tree Regressor/Keras Neural Network ML technique, which uses the weather parameter and ground-level wind speed to predict the wind shear. These attributes will help in predicting the wind particular hub height and wind speed for at least 1.5–3 h. Besides, there are also two power forecast models (Decision Tree Regressor/Keras Neural Network ML) which take the hub-height wind speed and weather parameters as input and forecast the power generation for the given power plant. It also provides brief information about the power-law method to calculate the wind shear coefficient. This model will help many wind power plants know about the present wind prediction model capabilities; it will also allow us to predict the particular hub-height wind speed and power generation for their specific wind farms.KeywordsDecision regressor tree (DRT)Keras neural networkWind shear coefficientRecursive and non-recursive modelingMAE (mean absolute error)MAPE (mean absolute percentage error)WS (wind shear)