Research On Short-term Load Forecasting Strategy Of Microgrid Based On Machine Learning

Microgrid can integrate a variety of distributed energy sources and is a good energy solution.In order to ensure the safe and stable operation of the microgrid system,it is necessary to accurately predict the power load of microgrid.However,the accuracy of load forecasting is affected by the sample data itself,forecasting methods and other factors.In view of this,this paper studies the related issues in short-term load forecasting based on a regional microgrid.The main research contents of this article are as follows:(1)Taking the original load data of an area as the research object,this paper analyzes the internal characteristics of load data change,and summarizes the short-term load influencing factors.For the problem of noise pollution in the collected original load data,variational mode decomposition(VMD)is used to denoise the load data.On the premise of avoiding modal overlap,the components in the sequence that interfere with the prediction results are filtered out.(2)Pearson correlation coefficient,Spearman correlation coefficient and Kendall correlation coefficient in statistics are used to analyze the correlation between the influencing factor data and load data,and the factors that really exert actual influences are screened out.The influencing factors with high correlation value are selected,and the correlation degree between the influencing factors is calculated again to judge whether the influencing factor data has information sharing phenomenon.In order to solve the problem of nonlinear coupling between the data of various influencing factors,kernel principal component analysis(KPCA)is used to extract features,which converts the original vectors containing some relevant information into less uncorrelated vectors and reduces the dimension of model input.(3)For it is difficult to determine the model parameters of long short term memory(LSTM)neural network,an adaptive Cauchy mutation particle swarm optimization(ACMPSO)algorithm is proposed to optimize the key parameters of LSTM model,and three test functions are used to test the optimization performance of ACMPSO algorithm.(4)The simulation experiment on Python shows that the LSTM prediction accuracy of the single model is the highest,and the model fitting ability is the best.The optimized model based on ACMPSO-LSTM has higher prediction accuracy and stability,which has high application value for improving the accuracy of short-term load forecasting of microgrid.