Load forecast and operation optimization of residential outdoor air system based on artificial neural network

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.