There are a large number of unstructured texts without data cleaning in the field of electric power. It is expensive to rely on manual ways to process the large amount of text data. In order to reduce the workload of data cleaning, we propose an intelligent method of automatic revision of power defect logs in this paper by adopting natural language processing technologies. We utilize entity recognition technology to recognize electrical equipment words on the text and utilize word similarity calculation to find out words with similar meaning in the standard vocabulary, which is the main process to revise Abnormal text. With the outstanding performance of entity recognition, the workload of data cleaning is reduced approximately 70% through our proposed method, which greatly improves the efficiency of unstructured data processing.
Aiming at the difficulty of load forecasting due to the current holiday load jump, a holiday load forecasting method with multi-scale feature combination is proposed. Feature extraction and recoding of date information, load information and weather information, and effectively use of historical data information, reconstruct load forecast feature combination. This method is used to reconstruct the electric load data of a certain area in Jiangsu Province. XGBoost and LSTM were used to predict the holiday load in the reconstructed multi-scale feature combination dataset and the traditional feature combination dataset. The experimental results show that in both load forecasting models, this feature combination method can effectively mine the latent relationship contained in historical data, represent more refined prior knowledge, and improve the accuracy of holiday load forecasting.
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