短期预测在智能电网建设中扮演着重要角色，深刻影响电网发输变配用各个环节的智能化改造。短期预测一般基于系统实测数据，而传感器故障，数据传输错误等原因会导致数据质量下降，严重影响短期预测的精确性。为建立数据质量受损情况下的精确短期预测模型，提出了结合数据预处理和双向长短期记忆（bi-directional long short-term memory, Bi-LSTM）的短期预测框架Bi-LSTM-DP(bi-directional long short-term memory data preprocessing)。在Bi-LSTM-DP中，采集的数据首先通过均值填补缺失值，进而基于Savitzky-Golay滤波器对数据降噪，最后采用Bi-LSTM提取时间序列的信息，实现短期预测。为了评估所提方法的性能，文中使用实测的公开数据集分别预测风电发电量和负荷需求，与其他参考方法对比表明了所述方法的有效性和鲁棒性。
英文摘要:
Short-term prediction plays an important role in the construction of smart grid, and profoundly affects the intelligent transformation of all aspects of power grid generation, transmission and distribution. Short-term predictions are generally based on system measured data, while sensor failures, data transmission errors and other reasons will lead to a decline in data quality, which will seriously affect the accuracy of short-term predictions. In order to establish an accurate short-term prediction model under the condition of data quality impairment, this paper proposes a short-term prediction framework bi-directional long short-term memory data preprocessing (Bi-LSTM-DP) that combines data preprocessing and bi-directional long short-term memory (Bi-LSTM). In Bi-LSTM-DP, the acquired data first fills in the missing values by mean, then denoises the data based on the Savitzky-Golay filter, and finally uses Bi-LSTM to extract time series information to achieve short-term predictions. In order to evaluate the performance of the proposed method, the measured public data sets are used to predict the wind power generation capacity and load demand respectively, and the comparison with other reference methods shows the effectiveness and robustness of the proposed method.