Research On Short-term Power Load Forecasting Method Based On Hybrid Optimization Algorithm

Nowadays,the social and economic level is constantly improving and the people’s living conditions are improving.As an important energy source,electric energy plays an irreplaceable role in all aspects of social production activities.Power system dispatch planning is indispensable in every link from power generation to power consumption.With more and more forms of social power demand,the integration of large-scale renewable energy and the wide application of electric vehicles have led to the emergence of more different types of load i nfluencing factors.Its unstable distribution characteristics and strong randomness make short-term load forecasting more difficult.In the economic dispatch of the power grid and the maintenance of the normal operation of the power system,it is necessary to accurately predict the short-term load of the power system.This is not only an important condition for ensuring the normal progress of social production activities,but also the key to saving power resources.Therefore,the power field has always attached great importance to how to predict the short-term load of the power system in a complex environment.The main contents of this article are as follows:The basic theory of short-term load forecasting is introduced,including the characteristics of short-term load forecasting,the structure of power load,and the influence of factors such as weather,time and electricity price on power load.The input variables of the forecast model and the evaluation index of the forecast results are selected,and the main steps of short-term load forecasting are summarized systematically,which pave the way for the future forecasting work.Starting from the basic principles of artificial neural networks,various types of neural networks are compared,their pros and cons and their scope of application are discussed,so as to determine the JANET network as the core prediction model of this article.At the same time,in response to the problem of difficult to determine the parameters in the JANET neural network,the particle swarm algorithm with non-linear changing inertia weights is used to optimize the learning rate,the number of iterations,and the number of hidden layer neurons in the model,and a prediction model based on IPSO-JANET is constructed.From the perspective of data decomposition and feature information selection,the load data analysis method based on empirical mode decomposition(EMD)and mutual information theory(MI)is introduced.Combining the shortcomings of these two methods,it is determined to choose its optimization algorithm variation.Modal decomposition(VMD)and minimum redundancy and maximum correlation criterion(mRMR)are used as the method of analyzing load data in this paper,and a load data analysis model based on VMD-mRMR is constructed.Finally,a combination model of short-term power load forecasting based on VMD-mRMR-IPSO-JANET was constructed.and real power grid data in a certain area of Zhejiang Province was selected for example simulation.Through experiments.it is found that the method used in this paper can make more accurate predictions of short-term load under complex conditions.