Short-term Load Forecasting Based On Wavelet Transform And Particle Swarm Grey Neural Network

Short-term power load forecasting has been closely linked with the realization of the management modernization of power systems.It is an important guarantee for the safe and stable operation of power systems and the improvement of economic efficiency.It has a very important impact on the safe and stable operation of power systems and the development of the national economy.Especially in the context of the rapid development of China’s power industry and the continuous improvement of the smart grid,finding solutions to short-term power load forecasting problems has become an arduous and important task.In this paper,based on wavelet transform and particle swarm grey neural network,a combined optimization model is established,and a new short-term power load forecasting method is proposed.Analysis of the characteristics of power load and the basic principles of prediction can be seen that the study of power load forecasting is a study of uncertain events.The factors affecting short-term power load forecast mainly include time factor,meteorological factor,holiday factor,economic factor and random interference factor.Wait.Through further analysis,the time period of the electrical load is very complicated,and often other small cycles are nested in the large cycle.The characteristics of the load duration time period appear in the frequency domain as the energy is concentrated in a certain frequency range.Therefore,these long and short time periods can be regarded as different frequency components in the frequency domain angle,and the frequency components are superimposed on each other to form a power load sequence.In this paper,the multi-resolution analysis in wavelet transform is used to decompose the electrical load from coarse to fine into components with different frequency characteristics.By using the ADF unit root test method to select the most suitable wavelet decomposition layer,the smoothness of each wavelet component can be guaranteed,and each component will have more obvious regularity and periodicity.The decomposed wavelet components are constructed according to their different characteristics,and their respective gray neural network prediction models are combined.The advantages of grey system and neural network prediction methods are suitable for the research of less data,poor information and uncertainty.The processing ability of strong nonlinear problems,while using the particle swarm optimization algorithm to optimize the weight threshold to solve the problem of local minimization,and finally obtain the expected prediction result through wavelet reconstruction.The model adopts a parallel prediction architecture as a whole,classifying the load on an hourly basis,and separately predicting each time point in the day.The volume of the sample data set of each prediction module is reduced,the model is simpler,and the load values at the same time every day have similarities,which helps to improve the generalization ability and prediction accuracy,and reduce the possibility of over-fitting.Through practical examples,the prediction results produced by other different prediction methods are analyzed and compared,including BP neural network prediction method based on wavelet transform and separate particle swarm grey neural network prediction method.The comparison between RMSE and R2 shows that the short-term electric load forecasting model based on combinatorial optimization has the smallest error and the highest prediction accuracy,which verifies the rationality and feasibility of the predictive model.It has certain guiding significance for the power sector to maintain safe and stable operation of the power grid and realize economic and reasonable power dispatching.