Zhu Dongge, Zhang Shuang, Ma Rui, Kang Wenni, Sha Jiangbo
Electric Power Research Institute of State Grid Ningxia Electric Power Co., Ltd. Yinchuan, Ningxia, 750002, China.
Sci Rep. 2024 Oct 14;14(1):24016. doi: 10.1038/s41598-024-74444-y.
In order to reduce the interference of abnormal energy big data and improve the accuracy of anomaly cleaning and detection, this paper proposes a method for cleaning abnormal energy big database based on sparse self-coding. Firstly, the abnormal data detection method based on multi-criteria evaluation is used to analyze the spectral feature distribution of abnormal energy big data. By using chaotic time series reconstruction model, robust local weighted regression analysis and sparse self-coding method, the feature decomposition of time series of energy data is realized. Then, according to the periodicity of the original sequence, the original sequence is segmitated adaptively to represent the morphological characteristics of the abnormal energy data sub-sequences driven by dynamic carbon emissions, and the anomaly index of each sub-sequence is obtained by AFCM algorithm. Secondly, an energy anomaly evaluation model based on LOF value is established. Finally, the output decision function of data cleaning is constructed to realize the abnormal energy big data cleaning. The test results show that the error detection rate of this method is 0.24%, the missing detection rate is 0.27%, the cleaning rate can reach 99.49%, and the cleaning time is less than 2s.
为了减少异常能源大数据的干扰，提高异常清洗和检测的准确性，本文提出一种基于稀疏自编码的异常能源大数据清洗方法。首先，采用基于多准则评估的异常数据检测方法，分析异常能源大数据的频谱特征分布。利用混沌时间序列重构模型、稳健局部加权回归分析和稀疏自编码方法，实现能源数据时间序列的特征分解。然后，根据原始序列的周期性，对原始序列进行自适应分割，以表征动态碳排放驱动下异常能源数据子序列的形态特征，并通过AFCM算法得到各子序列的异常指数。其次，建立基于LOF值的能源异常评估模型。最后，构建数据清洗的输出决策函数，实现异常能源大数据的清洗。测试结果表明，该方法的误检率为0.24%，漏检率为0.27%，清洗率可达99.49%，且清洗时间小于2秒。
