Power Text Data Preprocessing of Power Grid Infrastructure Project based on Skip-gram Model

The power grid infrastructure project is a large-scale and long-period instance which often involves various subjects. It would produce a large amount of data, serving as an important original data source of the operating maintenance and asset management systems in power supply enterprises. However, the manpower analysis fails to deal with unstructured natural text language data as well as nonstandard semi-structured tabular data. To address this issue, the deep analysis on the data with different forms is first conducted based on the power grid infrastructure project. Then, a data cleaning technique is used to eliminate the noise in the original low-quality data. Finally, a skip-gram model is built to convert the text data into a word embedding vector form. The well-preprocessed data contains contextual semantic information which is more suitable for data mining. Extensive simulation experiments clearly demonstrate the effectiveness of the proposed method.