数据缺失在电力负荷数据采集过程中经常发生,对提高算法的预测精确度带来了不利影响。现有的缺失数据补全算法只适用于缺失数据量较少的情况,而对于缺失数据较多的情况表现不佳。面对严重数据缺失的挑战,文中提出了一种基于稀疏表示的电力负荷缺失数据补全方法。首先以数据随机缺失为前提,将训练数据中假定缺失后的数据与完整的训练数据上下拼接构成训练矩阵;其次,利用离散余弦变换(Discrete Cosine Transform,DCT)生成一个过完备字典,并根据训练矩阵对其进行学习,旨在通过调优得到一个合适的字典,能对训练矩阵中的样本进行最好的稀疏表示。最后,在测试阶段,先利用学习后字典的上半部分获得测试集缺失数据的稀疏表示,然后利用稀疏表示和学习后字典的下半部分重构出无缺失的完整数据。实验结果表明,使用该方法对电力负荷数据缺失值进行补全,可以获得比传统插值方法、基于相关性的KNN算法、时空压缩感知估计算法以及时序压缩感知预测算法更高的精度。即使数据缺失率高达95%,该方法依然可以有效地补全缺失数据。

Data loss often occurs in the process of power load data collection,which adversely affects the accuracy of algorithm prediction.The existing missing data completion algorithm is only suitable for the case with less missing data,but performs poorly for the case with more missing data.Faced with the challenge of severe data loss,a method for power load missing data completion based on sparse representation is proposed.First of all,we assume that the data is randomly missing,and stitch the assumed missing data in the training data and the complete training data to form a training matrix.Secondly,an over-complete dictionary is generated by discrete cosine transform (DCT),and is learned according to the training matrix,aims to obtain a suitable dictionary for the best sparse representations of the samples in the training matrix.Finally,in the test phase,the upper part of the learned dictionary is used to obtain sparse representations of the missing data in the test set,and then the sparse representations and the lower part of the learned dictionary are used to reconstruct the complete data without missing.Experimental results show that using this method to complete missing values of power load data can achieve higher accuracy than traditional interpolation me-thods,correlation-based KNN algorithm,spatiotemporal compressed sensing estimation algorithm and time-series compressed sen-sing prediction algorithm.Even if the data miss rate is as high as 95%,this method can still effectively complete the missing data.

精确的短期负荷预测能通过预知所需用电负荷来指导电网的调度运行。但电力负荷不仅与用户用电习惯相关,还容易受到温度、湿度等气象因素的影响。因此,文章在现有的使用负荷历史数据基础上,增加了影响区域型电力负荷的气象数据,并考虑高维气象参量数据对预测算法的过拟合问题,提出了基于稀疏核主成分分析(sparse kernel principal component analysis, SKPCA)的高维气象数据降维方法。进而以历史负荷功率和SKPCA重构后主成分为输入,构建了基于卷积神经网络(convolutional neural network, CNN)和长短时记忆(long-short-term memory, LSTM)神经网络的混合深度学习预测模型。CNN-LSTM模型可以同时提取负荷功率及气象数据的空间和时间相关特征,从而全面利用数据的时-空相关性特征,提升负荷功率的短期预测精度。相比于常见的数据降维和负荷预测方法,文章所提方法的数据维度降低了71.43%,预测精度达到了98.92%。结果表明,所提算法融合通过SKPCA降维后的气象数据能够显著提升区域型电力短期负荷预测准确度。

Accurate short-term load forecasting can provide guidance for the dispatching operation of power grids by predicting the required power loads. However, the power load is not only related to the user’s electricity consumption habits but is also easily affected by meteorological factors such as temperature and humidity, Therefore, based on existing historical load data, this paper incorporates meteorological data that affect regional power loads, considers the overfitting problem of high-dimensional meteorological parameter data to the prediction algorithm, and proposes a dimensionality reduction method for high-dimensional meteorological data based on sparse kernel principal component analysis (SKPCA). Subsequently, taking the historical load power and principal components reconstructed by SKPCA as the input, we construct a hybrid deep learning prediction model based on a convolutional neural network (CNN) and a long short-term memory (LSTM) neural network. The CNN-LSTM model can extract the spatial and temporal correlation characteristics of the load power and meteorological data simultaneously to fully utilize the temporal-spatial correlation characteristics of the data and improve the short-term prediction accuracy of the load power. Compared with common methods of data dimension reduction and load forecasting, the data dimensions of this method decrease by 71.43%, and the prediction accuracy reaches 98.92%. The results show that the proposed algorithm can significantly improve the accuracy of regional power short-term load forecasting by fusing meteorological data after dimensionality reduction using SKPCA.

电力系统数据采集、测量、传输和存储等过程均可能出现数据缺失问题,威胁电网安全。针对传统电力系统缺失数据重建方法仅考虑数据分布规律,忽略了数据时序与空间特性的问题,提出一种考虑时空特性的电力系统缺失数据重建模型(spatial-temporal seq2seq imputation model, ST-SSIM)。ST-SSIM具备编码-解码结构,编码器由基于图卷积层与长短时记忆单元构造的时空信息提取单元组成,用于提取数据高维时空特征,解码器由长短时记忆单元与全连接层组成,用于解码高维特征,生成电力系统数据。所提模型的输入包括电力系统数据时间序列与电网拓扑邻接矩阵,因此ST-SSIM可实现对电力系统数据复杂时空关系的自动学习。算例中,将所提方法与现有方法在不同规模电网下比较,ST-SSIM具有最高的重建精度,证明了ST-SSIM能有效地学习到电力系统数据的时空特性。通过讨论重建误差与数据缺失节点数以及缺失时间跨度的关系,验证了所提模型重建效果较稳定。

Power system data may miss during acquisition, measurement, transmission and storage, which threatens the security of power grid. Since traditional missing data reconstruction methods only consider the data distribution and ignore the spatio-temporal characteristics, a power system missing data reconstruction model called ST-SSIM (spatio-temporal seq2seq imputation model) is proposed in this paper. ST-SSIM has an encoder-decoder structure. The encoder is composed of a spatio-temporal information extraction unit which is constructed by graph convolution layer and long short-term memory cell. The decoder is composed of long short-term memory cell and full connection layer. The input of the proposed model includes power system timeseries and adjacency matrix, so ST-SSIM can realize the automatic learning of complex time-space relationship of data. In experiment, compared the proposed method with the existing methods in power grids of different scales, ST-SSIM has the highest reconstruction accuracy, which proves that ST-SSIM can effectively learn the spatio-temporal characteristics of power system data. By discussing the relationship between reconstruction error and the number of missing nodes and time span, it is verified that the reconstruction effect of the proposed model is stable.

提出一种基于非线性收敛因子的改进鲸鱼优化算法（简记为IWOA）用于求解大规模复杂优化问题.为算法全局搜索奠定基础，在搜索空间中利用对立学习策略进行初始化鲸鱼个体位置；设计一种随进化迭代次数非线性变化的收敛因子更新公式以协调WOA算法的探索和开发能力；对当前最优鲸鱼个体执行多样性变异操作以减少算法陷入局部最优的概率.选取15个大规模（200维、500维和1000维）标准测试函数进行数值实验，结果表明，IWOA在求解精度和收敛速度方面明显优于其他对比算法.