To forecast the short-term power load accurately and reliably, a short-term power load model based on gate recurrent unit was proposed. In order to improve the accuracy of the model, the Adam algorithm is used to calculate the GRU hyperparameter Optimization, so as to construct an Adam-GRU short-term power load forecasting model. The performance of the forecasting model was evaluated by the Root Mean Square Error and running time, then the forecast results were compared with LSTM method. The experimental results show that Adam-GRU has a higher accuracy than the LSTM model, an increase of 15.54%, and a time complexity reduction of 15.75%. Compared with the RNN model, the accuracy has improved 10.84%. The results show that the short-term power load forecasting model and parameter optimization method based on Adam-GRU can effectively forecast the power load data.

The accuracy of facial expression feature extraction directly influences the recognition rate of facial expression. In order to extract facial expression feature effectively, this paper puts forward a new way of facial expression feature extraction ensemble learning algorithm based on ensemble thinking. The superposition method of heteromorphy ensemble learning is used to construct an ensemble learning model, two-d gabor wavelet, block local binary patterns and two-directional two-dimensional principal component analysis as the single learning device. Firstly, two-d gabor wavelet was used to get image texture information at each level. Then the image was divided into some blocks to acquire the eigenvectors of block local binary patterns, and reduced its dimensionality. Next, the dimensionality was further reduced by two-directional two-dimensional principal component analysis, extracted the effective characteristic features at the same time. Finally, it is classified by nearest neighbor classifier on the extracted feature library. Experimental results on JAFFE expression database show that this ensemble learning model gets higher recognition rate and better generalization ability than single learning device.

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