Artificial Intelligence-Based Power Load Situational Awareness Key Technology Research And Application

As a major basic industry in the energy field,the power industry is related to the development of the national economy and the lifeline of the national economy,and is an indispensable and important part of people’s lives.State Grid’s accurate prediction of changes in power load can better balance supply and demand.The traditional single load forecasting model has been difficult to meet the requirements of current load variability.This paper uses situational awareness technology to solve the problem of power load forecasting.First,the concept of situational awareness is mapped to the power load technology,and the concept of power load situational awareness is proposed;Secondly,on the basis of the traditional single load forecasting model,the signal decomposition technology and artificial intelligence algorithm are integrated,and the combination of power load situational awareness is rebuilt Prediction model;Finally,the actual load data is taken as the experimental object and input into the combined prediction model for simulation analysis.The main contents of the text include:(1)This paper expounds the necessity and research status of the research on the power load situational awareness technology,and elaborates the characteristics of the power load,the structure of the situational awareness model and the factors that affect the power load forecasting accuracy.(2)This paper introduces the structure of the power load situational awareness model from three levels: situational awareness,situational understanding and situational prediction.Situation awareness technology is mainly responsible for load data preprocessing,attribute reduction and feature value extraction;situation understanding technology mainly relies on ensemble empirical modal decomposition method(EEMD)and variational modal decomposition method(VMD)to decompose historical load sequences;situation prediction technology the back propagation(BP)neural network and the least squares support vector machine(LSSVM)prediction model are introduced.(3)Aiming at the nonlinear stationary problem of power load data,this paper introduces empirical mode decomposition method(EEMD)and variational mode decomposition method(VMD)to combine with a single load forecasting model,and proposes to use genetic algorithm(GA)and grasshopper algorithm(GOA)to optimize parameters.Build the EEMD-GA-BP power load combination forecast model and the VMD-GOA-LSSVM power load combination forecast model.(4)In order to verify the superiority of the prediction model described in this paper,MATLAB software is used for simulation analysis combined with actual data.Before the simulation experiment,the key parameters of EEMD and VMD were selected through a large number of experiments,and the population and iteration times of the genetic algorithm and the grasshopper algorithm were determined.Experiments show that the addition of signal decomposition technology improves the accuracy of the load forecasting model,and the addition of artificial intelligence optimization algorithms improves the convergence speed of a single forecasting model.(5)Finally,a power load situational awareness system is built to realize the comprehensive management of power load data and effective real-time prediction of power load change trends,and achieve good results in practical applications.