Ultra-short-term Power Load Forecasting Based on Two-layer Optimization VMD-LSTM
Stable power supply is a guarantee for rural development and construction,and the level of power load is an important measure of the construction effect.Therefore,establishing a precise load prediction model can more accurately and intuitively show the power load,and provide a strong support for the formulation of decision-making for power supply companies.Since the LSTM load forecasting model has problems such as poor convergence and low forecasting accuracy in data forecasting,in order to improve the forecasting accuracy of the model,an ultra-short-term power load forecasting method based on two-layer optimization VMD-LSTM is proposed.First,the sparrow algorithm is proposed to optimize the variational mode decomposition(sparrow variational mode decomposition,SVMD),and the original data is converted into modal components(intrinsic mode functions,IMF)through SVMD;secondly,the improved salp swarm algorithm(association salp swarm algorithm,ASSSA)to optimize the LSTM model.The optimization ability of the standard salp algorithm is enhanced by introducing four strategies;finally,each modal component is substituted into the new model and superimposed prediction is performed.The real historical load data of 10 kV transformer in a certain city and village in Liaoning Province is chosen,the root mean square error(RMSE),average absolute error(MAE),average absolute percentage error(MAPE),and fitting degree(R²)are taken as evaluation indicators,and compared with other basic prediction models.The results show that the improved algorithm is superior to other basic prediction models in terms of calculation accuracy and stability.
万方数据
维普
