The next-generation heating systems,crucial for rational heat distribution and refined management,rely heavily on accurate zone-specific heat load predictions.This paper introduces a method for rapid zone-specific hea...The next-generation heating systems,crucial for rational heat distribution and refined management,rely heavily on accurate zone-specific heat load predictions.This paper introduces a method for rapid zone-specific heat load prediction based on heat consumption allocation and data-driven techniques.The approach involves predicting the overall heat load of the building and then redistributing the total heat according to a heat consumption matrix.This eliminates the need for real-time data collection from each room,resulting in cost savings on hardware and improved computational efficiency.The overall building heat load data is obtained through a data-driven algorithm,while the heat consumption matrix is constructed through energy software simulation analysis.Using Building 2 in the Baotou Industrial Park,China,as a case study,the paper analyzes the differences between actual measurements and room estimates.Experimental results indicate an average error of 7.02%for the proposed estimation method.Although not achieving high precision(>95%)in heat load prediction,this level of accuracy is deemed sufficient to meet the requirements of feedforward control.展开更多
基金supported by the National Natural Science Foundation of China,61765012Natural Science Foundation of Inner Mongolia Autonomous Region,2023MS05047Basic research funds for universities directly under the Inner Mongolia Autonomous Region(2023RCTD011,2023YXXS012)。
文摘The next-generation heating systems,crucial for rational heat distribution and refined management,rely heavily on accurate zone-specific heat load predictions.This paper introduces a method for rapid zone-specific heat load prediction based on heat consumption allocation and data-driven techniques.The approach involves predicting the overall heat load of the building and then redistributing the total heat according to a heat consumption matrix.This eliminates the need for real-time data collection from each room,resulting in cost savings on hardware and improved computational efficiency.The overall building heat load data is obtained through a data-driven algorithm,while the heat consumption matrix is constructed through energy software simulation analysis.Using Building 2 in the Baotou Industrial Park,China,as a case study,the paper analyzes the differences between actual measurements and room estimates.Experimental results indicate an average error of 7.02%for the proposed estimation method.Although not achieving high precision(>95%)in heat load prediction,this level of accuracy is deemed sufficient to meet the requirements of feedforward control.
