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.
In recent years, weather forecast plays an extremely important role in new energy power generation as well as prediction and early warning of major meteorological disasters. To improve the accuracy and stability of forecast, it gradually changes from a single deterministic forecast to a multi-mode integrated forecast, that is, the combination of several independent forecast results. CART algorithm divides the global data set into multiple data sets that are easy to model and establishes a local regression model on each local data set, which is especially suitable for modeling complex data with multiple characteristic variables. Therefore, this paper mainly studies the multi-mode integrated method based on CART and its application in the forecast of wind speed. The wind speed sampling data of the European Center for Medium-Range Weather Forecasts and National Centers for Environmental Prediction are integrated by using the integrated method based on CART and compared with single-mode forecast and BREM. The results of the simulation show that the CART has a significant improvement in the accuracy of forecast compared with BREM and the traditional single-mode forecast.
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