Based on the distribution of cooling load at a subway station and the peak-valley electricity price in Guangzhou,a chilled water storage system is reserved in the ample space above the station's distribution area....Based on the distribution of cooling load at a subway station and the peak-valley electricity price in Guangzhou,a chilled water storage system is reserved in the ample space above the station's distribution area.This study proposes a design scheme and operational strategy for a chilled water storage system suitable for subway engineering,based on calculating the cooling load and designing a chilled water storage system in a subway station.Additionally,it proposes calculation coefficients of hourly cooling load suitable for subway engineering and convenient for estimation of hourly cooling load.Furthermore,an economic analysis is conducted by combining hourly cooling load with time-of-use electricity prices.This study provides a reference for the design and application of chilled water storage systems in subsequent subway projects.展开更多
The district cooling system (DCS) with ice storage can reduce the peak electricity demand of the business district buildings it serves, improve system efficiency, and lower operational costs. This study utilizes a mon...The district cooling system (DCS) with ice storage can reduce the peak electricity demand of the business district buildings it serves, improve system efficiency, and lower operational costs. This study utilizes a monitoring and control platform for DCS with ice storage to analyze historical parameter values related to system operation and executed operations. We assess the distribution of cooling loads among various devices within the DCS, identify operational characteristics of the system through correlation analysis and principal component analysis (PCA), and subsequently determine key parameters affecting changes in cooling loads. Accurate forecasting of cooling loads is crucial for determining optimal control strategies. The research process can be summarized briefly as follows: data preprocessing, parameter analysis, parameter selection, and validation of load forecasting performance. The study reveals that while individual devices in the system perform well, there is considerable room for improving overall system efficiency. Six principal components have been identified as input parameters for the cold load forecasting model, with each of these components having eigenvalues greater than 1 and contributing to an accumulated variance of 87.26%, and during the dimensionality reduction process, we obtained a confidence ellipse with a 95% confidence interval. Regarding cooling load forecasting, the Relative Absolute Error (RAE) value of the light gradient boosting machine (lightGBM) algorithm is 3.62%, Relative Root Mean Square Error (RRMSE) is 42.75%, and R-squared value (R<sup>2</sup>) is 92.96%, indicating superior forecasting performance compared to other commonly used cooling load forecasting algorithms. This research provides valuable insights and auxiliary guidance for data analysis and optimizing operations in practical engineering applications. .展开更多
基金supported by the Science and Technology Development Project of China Railway Design Corporation(Project No.2024CJ0401).
文摘Based on the distribution of cooling load at a subway station and the peak-valley electricity price in Guangzhou,a chilled water storage system is reserved in the ample space above the station's distribution area.This study proposes a design scheme and operational strategy for a chilled water storage system suitable for subway engineering,based on calculating the cooling load and designing a chilled water storage system in a subway station.Additionally,it proposes calculation coefficients of hourly cooling load suitable for subway engineering and convenient for estimation of hourly cooling load.Furthermore,an economic analysis is conducted by combining hourly cooling load with time-of-use electricity prices.This study provides a reference for the design and application of chilled water storage systems in subsequent subway projects.
文摘The district cooling system (DCS) with ice storage can reduce the peak electricity demand of the business district buildings it serves, improve system efficiency, and lower operational costs. This study utilizes a monitoring and control platform for DCS with ice storage to analyze historical parameter values related to system operation and executed operations. We assess the distribution of cooling loads among various devices within the DCS, identify operational characteristics of the system through correlation analysis and principal component analysis (PCA), and subsequently determine key parameters affecting changes in cooling loads. Accurate forecasting of cooling loads is crucial for determining optimal control strategies. The research process can be summarized briefly as follows: data preprocessing, parameter analysis, parameter selection, and validation of load forecasting performance. The study reveals that while individual devices in the system perform well, there is considerable room for improving overall system efficiency. Six principal components have been identified as input parameters for the cold load forecasting model, with each of these components having eigenvalues greater than 1 and contributing to an accumulated variance of 87.26%, and during the dimensionality reduction process, we obtained a confidence ellipse with a 95% confidence interval. Regarding cooling load forecasting, the Relative Absolute Error (RAE) value of the light gradient boosting machine (lightGBM) algorithm is 3.62%, Relative Root Mean Square Error (RRMSE) is 42.75%, and R-squared value (R<sup>2</sup>) is 92.96%, indicating superior forecasting performance compared to other commonly used cooling load forecasting algorithms. This research provides valuable insights and auxiliary guidance for data analysis and optimizing operations in practical engineering applications. .
