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为了解决风电功率预测误差对电力系统调度运行影响的问题，提出一种基于自适应移动平滑（adaptive movement smoothing，AMS）和时间卷积网络（temporal convolutional network，TCN）误差修正的风电功率预测方法。该方法首先利用变分模态分解和TCN提取风电功率的时空特性，得到初步预测结果；然后利用AMS模型对预测误差序列进行自适应平滑处理，降低误差的波动性；最后利用TCN模型提取预测误差的时间特性，对初步预测结果进行修正，提高预测的精度和稳定性。基于辽宁双子台和内蒙古克什克腾旗两个风电场的实测数据进行了实验对比分析，相较于其他方法，采用所提风电功率预测方法在15 min、30 min和1 h时间尺度下得到的预测结果，平均绝对误差降低50.0%以上，平均相对误差降低10.0%以上，验证了所提方法的有效性。

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