Establishing accurate short-term forecasting models for electrical load is crucial for the stable operation and intelligent advancement of power systems.Traditional methods have not adequately addressed the issues of data volatility and model uncertainty.In this paper,a multi-dimensional adaptive short-term forecasting method for electrical load based on Bayesian Autoformer network is proposed.Specifically,an adaptive feature selection method is designed to capture multi-dimensional features.By capturing multi-scale features and time-frequency localized information,the model is enhanced to handle high volatility and nonlinear features in load data.Subsequently,an adaptive probabilistic forecasting model based on Bayesian Autoformer network is proposed.It captures relationships of significant subsequence features and associated uncertainties in load time series data,and dynamically updates the probability prediction model and parameter distributions through Bayesian optimization.The proposed model is subjected to a series of experimental analyses(comparative analysis,adaptive analysis,robustness analysis)on real load datasets of three different magnitudes(GW,MW,and KW).The model exhibits superior performance in adaptability and accuracy,with average improvements in Root Mean Square Error(RMSE),Pinball Loss,and Continuous Ranked Probability Score(CRPS)of 1.9％,24.2％,and 4.5％,respectively.
万方数据
维普
