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An Improvement in Performance and Computational Cost of ANN Based Wind Speed Prediction System

机译:基于ANN的风速预测系统性能和计算成本的提高

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Wind speed prediction is very important as it influences the wind energy and hence wind farm applications. An accurate wind speed prediction will be helpful in scheduling and management of the wind farms. In this work, empirical mode distribution (EMD) and ensemble empirical mode distribution (EEMD) have been used for decomposition of wind speed data into IMFs. An ANN has been proposed to predict the wind speed with IMFS given as the inputs. The accuracy of prediction by the ANN is further increased by using the actual wind speed at the previous instant along with the IMFs as the input to the ANN. This increase in accuracy in prediction was also accompanied by a reduction in the total computational cost.
机译:风速预测非常重要,因为它影响了风能,因此风电场应用。精确的风速预测将有助于风电场的调度和管理。在这项工作中,经验模式分布(EMD)和集合经验模式分布(EEMD)已被用于将风速数据分解为IMF。已经提出了一个人以预测作为输入给出的IMF的风速。通过使用前一瞬间的实际风速以及IMF作为输入的输入,通过使用实际风速进一步提高了ANN预测的准确性。这种预测精度的增加也伴随着总计算成本的降低。

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