Research On Short-term Load Forecasting Of Power System Based On Random Forest

The short-term load forecast of the power system provides an important basis for the safe and stable operation of the power grid and the efficient and timely dispatch of electrical energy.Due to the differences in the forms of power consumption by users,a variety of power load modes have been caused.Whether or not accurate load prediction can directly affect the optimal control of power quality and the economic benefits of the power grid.With the development of information technology,the research on power load forecasting technology is further deepened,and the research on short-term load forecasting methods is also increasing.Because there are many kinds of algorithms,choosing the appropriate algorithm and prediction model to obtain the best prediction accuracy is the goal that power load forecasting is constantly pursuing.The random forest algorithm has many advantages such as high prediction accuracy,strong generalization ability,and fast convergence speed.It can more effectively avoid the phenomenon of "over-fitting".In this paper,an improved random forest algorithm and a combined algorithm are used to establish a preload forecasting model,and the historical data of a regional power company is used for verification analysis.The random forest algorithm is predicted from the parameters of the algorithm,the data set,and the analysis of climate characteristics And compared with the prediction error of traditional SVM and other algorithms,and found that the improved and combined random forest prediction models have higher accuracy and can achieve the expected results.This article first introduces the research background and significance of short-term load forecasting,elaborates the current research status at home and abroad,and compares and analyzes the advantages and disadvantages of common methods for short-term load forecasting.Secondly,the basic principle,characteristic classification and main influencing factors of power load forecasting are introduced,and the basic theory of random forest anddata mining is explained in detail,which lays a theoretical foundation for the improvement analysis below.Then it introduces the improved random forest prediction model of particle swarm optimization.The unique advantages of using particle swarm algorithm to solve the optimization problem are parameter optimization and feature evaluation of random forest decision tree k and split feature m.Combined with the advantages of random forest calculation,the training prediction model is optimized.Case analysis is given by using real load data.The superiority of the algorithm.Then it introduces the combined prediction algorithm based on cluster analysis and random forest.In order to deal with the poor performance of some training samples,the improved particle swarm optimization algorithm uses the advantages of data mining of FCM cluster analysis algorithm to cluster and analyze the load data on the basis of the improved random number forest algorithm for power load prediction.A case analysis was performed based on the original data of a certain area,and the average absolute percentage error was calculated and compared with the traditional RF and SVM prediction methods.Based on the cluster analysis and the random forest combination prediction method,the prediction error was small,the prediction accuracy was high,and the verification The effectiveness and practicability of the combined method are illustrated.It provides a strong reference for power system dispatching and grid planning.Finally,the methods of the paper are summarized and compared,and the research content of this paper and the method and significance of short-term load forecasting of future power systems are prospected.