Wind power generation is likely to hinder the safe and stable operations of power systems for its irregularity, intermittency, and non-smoothness. Since wind power is continuously connected to power systems, the step length required for predicting wind power is increasingly extended, thereby causing an increasing cumulative error. Correcting the cumulative error to predict wind power in multi-step is an urgent problem that needs to be solved. In this study, a multi-step wind power prediction method was proposed by exploiting improved TCN to correct the cumulative error. First, multi-scale convolution (MSC) and self-attentiveness (SA) were adopted to optimize the problem that a single-scale convolution kernel of TCN is difficult to extract temporal and spatial features at different scales of the input sequence. The MSC-SA-TCN model was built to recognize and extract different features exhibited by the input sequence to improve the accuracy and stability of the single-step prediction of wind power. On that basis, the multi-channel time convolutional network with multiple input and multiple output codec technologies was adopted to build the nonlinear mapping between the output and input of the TCN multi-step prediction. The method improved the problem that a single TCN is difficult to tap the different nonlinear relationships between the multi-step prediction output and the fixed input. The MMED-TCN multi-step wind power prediction model was developed to separate linearity and nonlinearity between input and output to reduce the multi-step prediction error. An experimental comparative analysis was conducted based on the measured data from two wind farms in Shuangzitai, Liaoning, and Keqi, Inner Mongolia. As revealed from the results, the MAE and RMSE of the MMED-TCN-based multi-step prediction model achieved the cumulative mean values of 0.0737 and 0.1018. The MAE and RMSE metrics outperformed those of the VMD-AMS-TCN and MSC-SA-TCN models. It can be seen that the wind power prediction method proposed in this study could improve the feature extraction ability of TCN for input sequences and the ability of mining the mapping relationship between multiple inputs and multiple outputs. The method is superior in terms of the accuracy and stability of wind power prediction.

风力发电的不规则性、间歇性和非平稳性很可能阻碍电力系统的安全稳定运行。由于风电不断接入电力系统，预测风电所需的步长越来越大，从而导致累积误差越来越大。修正累积误差以预测多步风电功率是一个亟待解决的问题。在本研究中，提出了一种利用改进的 TCN 来校正累积误差的多步风功率预测方法。首先，采用多尺度卷积（MSC）和自注意力（SA）来优化TCN的单尺度卷积核难以在输入序列的不同尺度上提取时空特征的问题。建立MSC-SA-TCN模型，识别和提取输入序列表现出的不同特征，提高风电单步预测的准确性和稳定性。在此基础上，采用多输入多输出编解码技术的多通道时间卷积网络，构建了TCN多步预测的输出与输入之间的非线性映射。该方法改善了单个TCN难以挖掘多步预测输出与固定输入之间不同非线性关系的问题。建立MMED-TCN多步风电功率预测模型，分离输入和输出之间的线性和非线性，减少多步预测误差。以辽宁双子台和内蒙古克旗两个风电场的实测数据为基础，进行了试验对比分析。结果表明，基于MMED-TCN的多步预测模型的MAE和RMSE达到了0.0737和0.1018的累积平均值。MAE 和 RMSE 指标优于 VMD-AMS-TCN 和 MSC-SA-TCN 模型的指标。可以看出，本研究提出的风电功率预测方法可以提高TCN对输入序列的特征提取能力以及挖掘多输入多输出映射关系的能力。该方法在风电功率预测的准确性和稳定性方面具有优越性。基于MMED-TCN的多步预测模型的MAE和RMSE达到了0.0737和0.1018的累积平均值。MAE 和 RMSE 指标优于 VMD-AMS-TCN 和 MSC-SA-TCN 模型的指标。可以看出，本研究提出的风电功率预测方法可以提高TCN对输入序列的特征提取能力以及挖掘多输入多输出映射关系的能力。该方法在风电功率预测的准确性和稳定性方面具有优越性。基于MMED-TCN的多步预测模型的MAE和RMSE达到了0.0737和0.1018的累积平均值。MAE 和 RMSE 指标优于 VMD-AMS-TCN 和 MSC-SA-TCN 模型的指标。可以看出，本研究提出的风电功率预测方法可以提高TCN对输入序列的特征提取能力以及挖掘多输入多输出映射关系的能力。该方法在风电功率预测的准确性和稳定性方面具有优越性。可以看出，本研究提出的风电功率预测方法可以提高TCN对输入序列的特征提取能力以及挖掘多输入多输出映射关系的能力。该方法在风电功率预测的准确性和稳定性方面具有优越性。可以看出，本研究提出的风电功率预测方法可以提高TCN对输入序列的特征提取能力以及挖掘多输入多输出映射关系的能力。该方法在风电功率预测的准确性和稳定性方面具有优越性。