In order to improve the convergence speed, stability and state estimation accuracy of the traditional consensus Kalman filter algorithm, this paper proposes a consensus Kalman filter optimization algorithm based on fractional powers, that is, on the basis of the traditional consensus Kalman filter algorithm, fractional powers are introduced into the local Kalman filter part and the consensus fusion part respectively. The two better fractional power values are selected respectively and added to the traditional consensus Kalman filter algorithm at the same time. Through simulation experiments, it is validated that adjusting the fractional powers can notably expedite the convergence speed. Additionally, introducing fractional powers into the Kalman filtering process can also smooth error curves, enhancing stability and estimation accuracy. In comparison to introducing fractional powers separately in the Kalman filtering part and consensus fusion part, simultaneously introducing appropriate fractional powers in both parts demonstrates superior performance.
Based on the characteristics of power load and considering various meteorological factors, this paper improved BiLSTM model for forecasting. On the improved Bi-LSTM model, the prediction effect of historical power load data at the peak is significantly better than the original model; Then, considering the meteorological factors mined by KNN algorithm, different meteorological factors and historical load data are used as the input end of the prediction model to predict, and the corresponding evaluation criteria are obtained. Simulation results show that the prediction accuracy is improved after considering the influence of multiple meteorological factors. Compared with the previous methods, the proposed method has higher prediction accuracy.
The traditional short-term load forecasting is not accurate in the extraction of meteorological factors, which leads to make prediction accuracy low. To fully explore the influence of meteorological factors on electrical load and effectively utilize the advantages of deep learning technology in nonlinear fitting, a short-term electrical load forecasting method based on two-way long short-term memory network taking meteorological factors into account is proposed in this paper. After the outliers of the original data were removed and standardized, the key factors affecting the power load were fully excavated by using the K-Neighbor-Nearest (KNN) algorithm, and the data sequence was reconstructed. After setting the hyperparameter of the neural network, the Bidirectional long short-term memory (BiLSTM ) network model is built to realize the short-term high-precision prediction of power load. The simulation results show that, compared with BiLSTM and LSTM, the combined method of KNN and BiLSTM mentioned in this paper has higher prediction accuracy.
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