基于人工神经网络的住宅新风系统负荷预测与运行优化
Load forecast and operation optimization of residential outdoor air system based on artificial neural network
摘要：
通过建立人工神经网络模型来预测溶液调湿新风系统的湿负荷及冷负荷,考虑使用热感区域划分来提高预测精度;引入了一种系统控制优化模型,并结合神经网络模型的负荷预测结果对新风系统的运行控制进行了优化,在潜能蓄能和分时电价情况下制定了优化控制策略,提高了系统灵活性。结果表明,分区神经网络模型具有较高的预测精度,湿负荷及冷负荷预测结果对应的均方根误差变异系数分别为8.72%和9.98%,优化结果可使系统在整个空调季的运行能耗和费用分别降低27.2%和29.2%。该结果可为住宅独立新风调湿系统的运行优化提供参考。
Abstract：
In this paper, an artificial neural network model is established to forecast the moisture load and cooling load of the solution humidification outdoor air system. In order to improve the forecast accuracy, the division of thermal area is considered. This paper also presents a system control optimization model, and optimizes the operation of the outdoor air system combined with the load forecast results of the neural network model. Under the scenario of potential energy storage and time-of-use tariff, the optimization control strategy is formulated to improve the flexibility of the system. The results show that the partition neural network model has high forecast accuracy. The root mean square error variation coefficients corresponding to the prediction results of moisture load and cooling load are 8.72% and 9.98% respectively. The optimization results can reduce the operation energy consumption and cost of the system by 27.2% and 29.2% in the whole air conditioning season respectively. The results provide a reference for the operation optimization of independent outdoor air humidification systems in residential buildings.
Keywords:load forecast; outdoor air; partition artificial neural network; solution humidification; heat storage