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

机译:基于人工神经网络的风速预测系统性能和计算成本的改进

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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,因为它会影响风能,进而影响风电场的应用。准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,经验模态分布(EMD)和整体经验模态分布(EEMD)已用于将风速数据分解为IMF。已经提出了一种人工神经网络,以IMFS作为输入来预测风速。通过使用前一时刻的实际风速以及IMF作为ANN的输入,可以进一步提高ANN的预测精度。预测准确性的提高还伴随着总计算成本的降低。

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