Short-to-medium-term electric load forecasting is crucial for grid planning, transformation, and load scheduling for power supply departments. Various complex and ever-changing factors such as weather, seasons, regional economic structures, and enterprise production cycles exert uncontrollable effects on the electric grid load. While the causal convolutional neural network can significantly enhance long-term sequence prediction, it may suffer from problems such as vanishing gradients and overfitting due to extended time series. To address this issue, this paper introduces a new power load data anomaly detection method, which leverages a convolutional neural network (CNN) to extract temporal and spatial information from the load data. The features extracted are then processed using a bidirectional long short-term memory network (BiLSTM) to capture the temporal dependencies in the data more adeptly. An enhanced random forest (RF) classifier is employed for anomaly detection in electric load data. Furthermore, the paper proposes a new model framework for electricity load forecasting that combines a dilated causal convolutional neural network with ensemble learning. This combination addresses issues such as vanishing gradients encountered in causal convolutional neural networks with long time series. Extreme gradient boosting (XGBoost), category boosting (CATBoost), and light gradient boosting machine (LightGBM) models act as the base learners for ensemble modeling to comprehend deep cross-features, and the prediction results generated by ensemble learning serve as a new feature set for secondary ensemble modeling. The dilated convolutional neural network broadens the receptive field of the convolutional kernel. All acquired feature values are concatenated and input into the dilated causal convolutional neural network for training, achieving short-to-medium-term electric load forecasting. Experimental results indicate that compared to existing models, its root mean squared error (RMSE) and mean squared error (MSE) in short-term and mid-term electricity load forecasting are reduced by 4.96% and 12.31%, respectively, underscoring the efficacy of the proposed framework.

Grammar error correction (GEC) is a crucial task in the field of Natural Language Processing (NLP). Its objective is to automatically detect and rectify grammatical mistakes in sentences, which possesses immense application research value. Currently, mainstream grammar-correction methods primarily rely on sequence labeling and text generation, which are two kinds of end-to-end methods. These methods have shown exemplary performance in areas with low error density but often fail to deliver satisfactory results in high-error density situations where multiple errors exist in a single sentence. Consequently, these methods tend to overcorrect correct words, leading to a high rate of false positives. To address this issue, we researched the specific characteristics of the Chinese grammar error correction (CGEC) task in high-error density situations. We proposed a grammar-correction method based on prompt templates. Firstly, we proposed a strategy for constructing prompt templates suitable for CGEC. This strategy transforms the CGEC task into a masked fill-in-the-blank task compatible with the masked language model BERT. Secondly, we proposed a method for dynamically updating templates, which incorporates already corrected errors into the template through dynamic updates to improve the template quality. Moreover, we used the phonetic and graphical resemblance knowledge from the confusion set as guiding information. By combining this with BERT’s prediction results, the model can more accurately select the correct characters, significantly enhancing the accuracy of the model’s prediction correction results. Our methods were validated through experiments on a public grammar-correction dataset. The results indicate that our method achieves higher correction performance and lower false correction rates in high-error density scenarios.

The stability and self-adaption for combination texts must be processed in Web Texts Environment. Therefore a language and technology method for self-adapting environment of web texts is needed. To do this, we have built an adaptive data-stream metho