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
针对强迫导向油循环风冷(oir directrd air forced,ODAF)结构变压器负荷能力受温升约束影响的问题,提出了3种负荷类型情况下变压器负荷能力评估方法。首先,考虑风扇与油泵的运行状态以及油粘度变化对热阻的影响等因素,基于热电类比法建...针对强迫导向油循环风冷(oir directrd air forced,ODAF)结构变压器负荷能力受温升约束影响的问题,提出了3种负荷类型情况下变压器负荷能力评估方法。首先,考虑风扇与油泵的运行状态以及油粘度变化对热阻的影响等因素,基于热电类比法建立了变压器热路模型,以计算绕组热点与顶部油温度;其次,采用粒子群优化(particle swarm optimization,PSO)算法拟合热路模型参数,并基于2台不同型号变压器的运行数据,对热路模型的计算精度与拟合参数适用性进行有效性验证;最后,参考GB/T1094.7负载导则给出的温升限值,基于温升特性提出了负荷能力评估模型。分析结果表明,该研究所提热路模型计算热点温度的误差不大于2.35℃,在工程允许范围内;正常周期性负荷下当环境温度低于1℃时,关闭1组子散热器后仍满足温升约束。展开更多
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. .
