Predictability of Wind Speed with Heights Using Recurrent Neural Networks

Accurate wind speed prediction is important for wind energy integration into the power grid. While most wind turbines have hub heights of about 80 - 140m, wind speeds are usually measured up to 40mm and in exceptional cases up to 100m. This paper analyzes the predictability of wind speed with heights. To achieve this, a Laser Illuminated Detection and Ranging (LiDAR) system, ZephIR 300, was acquired and installed at the beach of King Fahd University of Petroleum Minerals. The ZephIR 300 device is widely accepted for wind resource assessment and its wind speed measurements have been validated and found to be accurate for heights from 10 to 300m. Wind speed data was collected at 20, 40, 50, 60, 80, 100, 120, 140, 160, and 180m heights for three months. The collected data was used for training and testing the performance of the RNN model for predicting the wind speed 12 hours ahead of time using 48 previous hourly values. Careful analyses of short-term wind speed prediction at different heights and future hours showed that wind speed is predicted more accurately at higher heights. For example, the mean absolute percent error decreased from 0.15 to 0.11 corresponding to heights 20 and 180m; respectively.