New Energy Power Prediction Optimization Based on Improved TF-IDF Single Machine Information Feature Extraction

In order to lean management of new energy consumption, based on the characteristics of wide range and complex structure of unstructured data including dispatch and maintenance logs in the power grid system, based on the idea of improving TF-IDF, a DF-IDF data reflecting time slots is proposed Feature extraction algorithm, taking wind power in a power grid in the northern region as an example, extracts keyword and part-of-speech features from unstructured power grid dispatch logs and maintenance data to achieve single-machine state data screening and eliminate irrelevant state data. With the help of the effectiveness of the stand-alone information feature extraction algorithm, the management methods of new energy power prediction optimization are then studied. The results show that in the case of highly complex data, the average accuracy of the DF-IDF-processed feature extraction method is 8% and 7% higher than the traditional TF-IDF method and the One-Hot method. At the same time, it also provides pre-processing methods for stand-alone power prediction calculation, accuracy analysis, and summary station power prediction optimization in new energy power prediction and optimization, improving the rationality, accuracy, and effectiveness of data collaborative processing, and laying new energy lean. Contain the foundation of management level.