Data Reconstruction of Wireless Sensor Network and Zonal Demand Control in a Large-Scale Indoor Space Considering Thermal Coupling

Abstract

An indoor high and open space is characterized by high mobility of people and uneven temperature distribution, so the conventional design and operation of air conditioning systems makes it difficult to regulate the air conditioning system precisely and efficiently. Thus, a Wireless Sensor Network was constructed in an indoor space located in Hong Kong to monitor the indoor environmental parameters of the space and improve the temperature control effectively. To ensure the continuity of the measurement data, three algorithms for reconstructing temperature, relative humidity and carbon dioxide data were implemented and compared. The results demonstrate the accuracy of support vector regression model and multiple linear regression model is higher than Back Propagation neural network model for reconstructing temperature data. Multiple linear regression is the most convenient from the perspective of program complexity, computing speed and difficulty in obtaining input conditions. Based on the data we collected, the traditional single-input-single-output control, zonal temperature control and the proposed zonal demand control methods were modeled on a Transient System Simulation Program (TRNSYS) control platform, the thermal coupling between the subzones without physical partition was taken into account, and the mass transfer between the virtual boundaries was calculated by an external CONTAM program. The simulation results showed the proposed zonal demand control can alleviate the over-cooling or over-heating phenomenon in conventional temperature control, thermal comfort and energy reduction is enhanced as well.