Development of an optimal mechanical ventilation system control strategy based on weather forecasting data for outdoor air cooling in livestock housing

Mechanical ventilation systems are commonly utilized in livestock housing to counter indoor pollution and control the indoor thermal environment. During their operation, the systems must be appropriately controlled according to the changes in the indoor/outdoor thermal environments (conventionally handled by the facility manager based on their experience). In this study, a 24 h optimal ventilation system control strategy was developed using weather forecasting data to control the temperature in livestock housing. A building energy system model for livestock housing was developed based on measurement data. The optimal control strategy was derived by conducting simulation analysis using the model and optimizing four control variables of the venti-lation system. The developed optimal ventilation system control strategy showed better indoor air temperature maintenance performance (energy-saving performance (ESP) of 20.50%) compared to the conventional control method. Furthermore, the ESP achievement increased as the prediction performance of the weather forecasting data increased. The optimal ventilation fan control strategy developed in this study, an intelligent control method based on an energy model and weather forecasting data, is expected to be introduced as an advanced technology to the field of livestock housing wherein research on the development of such strategies in the operation stage is still insufficient.

Automated summary

In this study, a 24-hour optimal ventilation system control strategy was developed using weather forecasting data to control the temperature in livestock housing. The strategy showed better indoor air temperature maintenance performance compared to the conventional control method, with an energy-saving performance of 20.50%. The developed strategy is expected to be introduced as an advanced technology to the field of livestock housing.