Abstract
Accurate prediction of wind speed in future time domain is critical for wind power integration into the grid. Wind speed is usually measured at lower heights while the hub heights of modern wind turbines are much higher in the range of 80-120m. This study attempts to better understand the predictability of wind speed with height. To achieve this, wind data was collected using Laser Illuminated Detection and Ranging (LiDAR) system at 20m, 40m, 50m, 60m, 80m, 100m, 120m, 140m, 160m, and 180m heights. This hourly averaged data is used for training and testing a Recurrent Neural Network (RNN) for the prediction of wind speed for each of the future 12 hours, using 48 previous values. Detailed analyses of short-term wind speed prediction at different heights and future hours show that wind speed is predicted more accurately at higher heights.For example, the mean absolute percent error decreases from 0.19 to 0.16as the height increase from 20m to 180m, respectively for the 12 th future hour prediction. The performance of the proposed method is compared with Multilayer Perceptron (MLP) method. Results show that RNN performed better than MLP for most of the cases presented here at the future 6th hour.
Highlights
Growing population and at the same or even at higher pace increasing power demands are the concerns of people from all walks of life
The results showed that the proposed hybrid model outperformed some of the existing methods such as Back Propagation Neural Networks (BP), Auto-Regressive Integrated Mo– ving Average (ARIMA), and combination of Empirical Mode Decomposition (EMD)
This paper utilizes hourly averaged WS data measured by Laser Illuminated Detection and Ranging (LiDAR) system for 90 days between April 2nd and 30th June 2017 where the data was measured at ten different heights namely 20, 40, 50, 60, 80, 100, 120, 140, 160, and 180 m above ground level (AGL)
Summary
Growing population and at the same or even at higher pace increasing power demands are the concerns of people from all walks of life. There are more than 90 countries cont–ributing towards wind power capacity build up inc–luding 9 countries with more than 10 GW and 29 more than 1 GW of installed capacities globally. Among all the meteorological para–me– ters, is highly fluctuating both in temporal and spatial domains. It changes with time of the day, month of the year, and day of the year. This fluctuating nature of wind speed creates an uncertainty in the availability of continuous power and more importantly the stability of the grid. Understanding the variability of the Artificial intelligence techniques such as Artificial Neural Networks (ANN)[9], Convolution LSTM Net– works [16], neuro-fuzzy systems [17], support vector machines [18], long-short term memory networks [19], Particle Swarm Optimization (PSO) [20],modes decom– position based low rank multi-kernel ridge regre–ssion [21], Gaussian process regression combined with nume– rical weather predictions [22], Singular Spectrum Analysis and Adaptive Neuro Fuzzy Inference System [23], optimal feature selection and a modified bat algorithm with the cognition strategy [24], and spatial model[25] have been applied to capture the nonlinear
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