Improving the quality of building spaces that are planned mainly on loads rather than residents: Human comfort and energy savings for warehouses

This research proposes a study for a warehouse building to mitigate both thermal dissatisfaction and energy use through the network based real-time analysis. In order to optimize heating and cooling supply, an algorithm for simultaneous control of the amount of air and its temperature is designed, and a neural network model that learns the algorithm is generated. Also, an inner algorithm for thermal comfort analyses real-time temperature levels and rectifies the model's control signals to mitigate thermal dissatisfaction. By comparing results, this research concludes advantages of a neural network model with estimating thermal comfort. The model reduces thermal dissatisfaction by 21.2% and saves energy use by 6.4% in comparison with the conventional thermostat on/off controller equipped in most buildings. Without compromising thermal comfort for workers, the proposed model that consists of two independent structures for optimizing supply air and estimating thermal comfort can contribute to the improvement of thermal performance for warehouses.