A New Neural Networks Approach Used to Improve Wind Speed Time Series Forecasting
Generally, wind turbines convert the energy of wind into electricity. In this order, it is essential to predict accurately this source’s availability and intensity at the same location and height where wind electric generators will be installed, and therefore obtain reliable time-series data. The problem of meteorological time series prediction can be formulated as a system identification problem. To improve the prediction of these meteorological time series, we describe then use an application of a new neural networks approach in this paper. This novel, robust, and reliable forecasting method is based on the application of a new learning algorithm that allows a renewal of learning data, with time. For our algorithm a neural network is developed to estimate just one value y (t+1), then it is taken up with a new learning set enriched by data freshly measured. The obtained results showed a good agreement between measured and predicted series, and the mean relative error over the whole data set, which are not exceeding 5 %.
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