Research On Power Load Interval Prediction And Probability Density Prediction Based On Optimal Combination Model

Power load forecasting is an important part of the planning and operation of the process grid system.Accurate forecasting which realized by reasonable load forecasting methods will help improve power utilization efficiency and achieve economic and social benefits.However,The power load is non-linear and easily affected by various uncertain factors such as weather and economy.As a result,that adds randomness and instability to the load forecast,making the simple point forecasting method or a single model unable to meet the demand for the stability of forecast accuracy.Therefore,this paper takes interval prediction and probability density prediction as the direction,and describes the range and degree of load change by constructing an optimized combination load forecasting method to improve the model’s comprehensive forecasting ability in terms of accuracy and stability.The empirical research in this paper is based on the actual power load data set and influencing factor data set of a certain city in China in2018.In this paper,the XGBoost algorithm is used to perform feature selection on influencing factors such as temperature,body temperature,seasons,holidays,and historical load to remove redundant features and prepare data for subsequent model verification.After that,the paper constructs the optimal combination of model: Firstly,building the optimized single model,which uses Bayesian optimization algorithm to nonlinear three kinds quantile regression algorithm(Random Forest Quantile Regression,Quantile Regression Gradient Boosting and Support Vector Quantile Regression)to optimize the hyperparameters and obtain the optimized model;Secondly,different combination methods are used to combine the optimized single models,and the corresponding optimized combination model is constructed.For the choice of combination method,this paper considers fixed weight and variable weight combination methods,including Simple Average,MAPE-based weight,Least Square and Constrained Quantile Regression Average combination methods.Both the constructed optimized combination model and the optimized single model can realize the prediction of the conditional quantile of load,and obtain 0.01 to 0.99 quantile information with an interval of 0.01.In addition,based on the realization of point prediction,this paper uses quantile information combined with Bootstrap interval estimation method to achieve interval prediction,and combined with kernel density estimation to achieve probability density prediction,and obtain more comprehensive prediction information.In order to evaluate the performance of the model,this paper compares and analyzes the prediction effects of the single model and the optimized combination model,and uses different indicators to evaluate the performance of the model,such as MAPE,MSE,CPIA,and APL.The results show that the Bayesian optimization algorithm effectively improves the prediction accuracy of the single model.Compared with the optimized single model,the Constrained Quantile Regression Average optimized combination model shows better performance in point prediction,interval prediction,and probability density prediction stability and accuracy.