Short term power load forecasting based on the combination of similar days and BiLSTM
Short term power load has the characteristics of nonlinearity,volatility and many influencing factors.Aiming at the lack of forecasting accuracy caused by the above characteristics,a short-term power load forecasting model based on the combination of similar days and bi directional long short memory neural network(BiLSTM)is proposed.First,the dynamic change mechanism of power load is analyzed,and the similar day and gray correlation analysis methods are introduced to build the load and feature fusion data set;Secondly,the nonlinear and highly fluctuating original load data is decomposed into several relatively stable components by using the variational modal decomposition(VMD)method,and the BiLSTM prediction model is built for each component;Finally,the whale optimization algorithm(WOA)is used to optimize the decomposition parameters and similar days of the model to reduce the inherent error of the model.Based on the actual data of a region in New England,the simulation results show that the MAPE,MAE and RMSE of the proposed model are 0.58％,42 and 78 respectively,which are better than the control model and effectively improve the accuracy of load forecasting.
short-term power load forecastingsimilar daydeep learningwhale optimization algorithmvariational modal decomposition
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