Load forecasting plays an important role in ensuring the safe operation of the power grid. Accurate load forecasting is mainly influenced by historical load data, PV, precipitation and other factors. A load forecasting model with double attention LSTM based on feature selection is proposed, which is designed to comprehensively solve the impact of multiple factors on load forecasting. This model uses Recursive Feature Elimination to remove redundant influencing factors and outputs features with high correlation to the real load. Based on the Long-Short Term Memory network, the feature selection and timing dual attention mechanisms are introduced to merge and dynamically explore the connection between load and input features, which then improves the accuracy of load forecasting. The experimental results show that the accuracy of the proposed model for load forecasting is significantly improved compared with the traditional model.
With the improvement of the quality of human life, more and more people pay more attention to dental health. Panoramic CT image is an important method for studying teeth. The existing technology simply classifies or segment the teeth, and cannot accurately segment each tooth independently. The reason is that they did not consider the relationship between tooth type and location. Therefore, this article proposes a method that combines tooth position and tooth type. Mainly by adding the LSTM network to the extracted features to perform feature screening, and then perform classification, detection, and segmentation tasks. Experimental results show that the effective combination of CNN and RNN can accurately detect and segment teeth.
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