浅埋地下工程间歇空调最优启停时间的神经网络控制
ANN control on optimum on/off time of intermittent air conditioning systems in shallow underground projects
摘要：
将影响最优启停时间的四个主要因素作为控制输入，最优启停时间作为系统输出，建立了最优启停时间的神经网络预测模型。分析了考虑岩土蓄热作用下室内热环境的变化，应用CFD模拟软件建立了采用间歇空调时室内空气与周围岩土的耦合传热模型，将模拟所得数据用来训练神经网络模型，得出的最优模型可用于预测各种复杂的非线性条件下的最优启停时间。通过一小型系统模型验证了该神经网络控制的有效性。
Abstract：
Taking four main affecting factors as input and optimum on/off time as output, develops an artificial neutral network (ANN) prediction model. Analyses the variation of indoor thermal environment under the influence of soil heat storage. Based on a CFD simulation, builds up a coupling heat transfer model of indoor air and envelopes when using intermittent air conditioning. The outcomes of simulation are taken as inputs to train the neutral network model. The optimized neutral network model can be used to predict the optimum on/off time under various complicated non-linear conditions. Verifies the ANN control by a small system model.
Keywords:shallowundergroundproject,intermittentairconditioning,optimumon/offtime,couplingheattransfer,artificialneutralnetwork