
针对配电网故障预测精度低导致的配网运行不稳定问题,提出一种基于STL-LSTM的配电网故障预测模型。首先对配电网故障数据集进行数据分析,使用STL(seasonal and trend decomposition using loess
STL)进行时间序列分解,然后引入长短时记忆网络(long-short memory network
LSTM),对数据进行特征提取,对处理后的数据进行预测。选择平均绝对百分比误差(MAPE)与决定系数(R
2
)作为性能评价指标。实验结果表明,该模型的MAPE和R
2
分别达到0.06和0.78,相较于其他模型具有更优秀的性能,为配电网故障数据预测提供了一种精度可靠的的预测方法。
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