Very fast training neural-computation techniques for real measure-correlate-predict wind operations in wind farms

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The real operation of a wind farm implies the solution of many different problems related to wind speed at a wind farm location site. Wind speed prediction and wind series reconstruction are the two examples of important problems tackled in wind farm management and prospection. Usually, wind speed prediction and reconstruction of wind series are carried out in wind farms using data from in situ measuring towers, usually named as Measure-Correlate-Predict methods (MCP). MCP processes consist, therefore, in the wind speed prediction or reconstruction from neighbor stations, using different methods. In this paper, we tackle the special case of real MCP operations in wind farms, in which the algorithms to reconstruct or predict the wind series must be extremely fast in order to be useful. We present the application of two state-of-the-art neural networks which have shown a very fast training time, with an excellent performance in terms of accuracy. Specifically, we show the application of Group Method of Data Handling and Extreme Learning Machines in the MCP reconstruction and prediction of wind speed series, in a real wind farm in Spain. A comparison in terms of computation time and accuracy with alternative algorithms in the literature is also carried out. Finally, we show a real implementation of the Group Method of Data Handling (GMDH) and Extreme Learning Machine (ELM) in a software in use for real MCP operations in wind farms.