Error correction-based method for ultra-short term prediction of wind speeds of extreme learning machines

Abstract

The invention provides an error correction-based method for ultra-short term prediction of wind speeds of extreme learning machines. The method comprises the following steps of: S1, normalizing wind speed history data to obtain a normalized data set; S2, establishing an extreme learning machine model, and carrying out wind speed prediction by utilizing the extreme learning machine model and the normalized data set so as to obtain a preliminary predicted value set and an error set; S3, judging whether a sequence of the error set is stable or not, if the judging result is positive, inputting theerror set into an auto-regression moving average model to obtain a first error prediction sequence, and if the judging result is negative, inputting the error set into an auto-regression integral moving average model to obtain a second error prediction sequence; and S4, superposing the preliminary predicted value set with the first error prediction sequence or the second error prediction sequenceso as to obtain a final wind speed predicted value set. According to the error correction-based method for ultra-short term prediction of wind speeds of extreme learning machines, wind speeds are predicted through correcting errors, so that the advantage of improving the wind speed prediction precision is provided.

Description

technical field [0001] The invention relates to the field of ultra-short-term wind speed prediction methods, in particular to an ultra-short-term wind speed prediction method for extreme learning machines based on error correction. Background technique [0002] As one of the important clean energy sources, wind power has made great contributions to my country's energy and environmental protection. The ultra-short-term wind speed prediction plays an important role in the grid-connected operation of the power grid. The higher the prediction accuracy, the more accurate the judgment made by the real-time dispatching system, and the more accurate the selection of the standby unit. Most of today's wind speed forecasts are based on historical data, with less attention paid to forecast errors. In order to improve the prediction accuracy of wind speed and bring more accurate data reference for grid-connected wind farms. A wind speed prediction method for error correction is propose...

AI Technical Summary

Problems solved by technology

These methods reduce the impact of large deviations on the final predicted value by apportioning the error risk of individual predictions, but the robustness of the weighting coefficients has always been the bottleneck of the combination method

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