Short-term building load probability density prediction method

• Main Authors: ZHANG FEIXIANG, WEI JIANGCHUAN, MI YANG, LIN SHUNFU, CHEN TENG, YANG TAO, MA TIANTIAN, SUN GAIPING, LIU RONGHUI, ZHAO ZENGKAI, WANG LEKAI
• Format: Patent
• Language: Chinese; English
• Published: 05.03.2021
• Subjects: CALCULATING; COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS; COMPUTING; COUNTING; DATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FORADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORYOR FORECASTING PURPOSES; PHYSICS; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE,COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTINGPURPOSES, NOT OTHERWISE PROVIDED FOR

The invention relates to a short-term building load probability density prediction method, which comprises the following steps of: 1) screening an original exogenous variable by adopting an orthogonalmaximum correlation coefficient by taking a maximum correlation coefficient and minimum redundancy as principles to obtain a selected exogenous variable; 2) for the selected exogenous variable, introducing a binary risk indication variable to improve the accuracy of peak load prediction; 3) constructing a convolution gating quantile regression model, taking the screened exogenous variables and binary risk indication variables as input together, and taking building load prediction values under different quantiles as output for training; and 4) predicting by adopting the trained constructed convolution gating quantile regression model to obtain predicted values under different quantiles, and obtaining a probability density distribution function from the predicted values under the differentquantiles through a fitted