To investigate the dynamic characteristics of the thermal conditions of hot-water district-heating networks, a dynamic modeling method is proposed with consideration of the heat dissipations in pipes and the character...To investigate the dynamic characteristics of the thermal conditions of hot-water district-heating networks, a dynamic modeling method is proposed with consideration of the heat dissipations in pipes and the characteristic line method is adopted to solve it. Besides, the influences of different errors, space steps and initial values on the convergence of the dynamic model results are analyzed for a model network. Finally, a part of a certain city district-heating system is simulated and the results are compared with the actual operation data in half an hour from 6 secondary heat stations. The results indicate that the relative errors for the supply pressure and temperature in 5 stations are all within 2%, except in one station, where the relative error approaches 4%. So the proposed model and algorithm are validated.展开更多
基金supported by the Scientific Development Pro-gram of Shandong Province(Grant No.2012GGB01071)the Doctoral Scientific Research Fund Program of Shandong Jianzhu University (Grant No. XNBS1225)the School Scientific Research Fund Program of Shandong Jianzhu University (Grant No. XN110108)
文摘To investigate the dynamic characteristics of the thermal conditions of hot-water district-heating networks, a dynamic modeling method is proposed with consideration of the heat dissipations in pipes and the characteristic line method is adopted to solve it. Besides, the influences of different errors, space steps and initial values on the convergence of the dynamic model results are analyzed for a model network. Finally, a part of a certain city district-heating system is simulated and the results are compared with the actual operation data in half an hour from 6 secondary heat stations. The results indicate that the relative errors for the supply pressure and temperature in 5 stations are all within 2%, except in one station, where the relative error approaches 4%. So the proposed model and algorithm are validated.
文摘提出一种基于深度置信网络的区域供热逐时负荷预测方法,并以兰州新区某换热站实际运行数据对所提出方法的有效性进行验证。此外,为分析建筑物热惰性对供热逐时负荷预测精确度的影响,分别将预测时刻前1 h,1~2 h,和1~3 h时作为输入参数的时间序列。研究结果表明:当时间序列取为预测时刻前1 h时显示出最佳的预测性能,预测值与实际值的平均绝对误差和平均相对误差分别为277.98 k W和2.28%,且相比采用人工神经网络分别降低约17.56 k W和0.15%。
