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.

The random movement of occupants in a high-speed railway station results in a more complex indoor environment. In this study, the indoor thermal environment and the thermal comfort in summer were investigated via field measurements and questionnaires in the waiting hall of a high-speed railway station. The results showed that there was an uneven horizontal temperature distribution in the area, and over 30% of the passengers were dissatisfied with the air conditioning system. In order to improve the control of the indoor temperature as well as reduce the energy consumption of the air conditioning system, an improved zonal control strategy and AMPC control optimization algorithm based on real-time people are proposed, and different control strategies are modeled and simulated using MATLAB/Simulink. It is concluded that the improved zonal control method proposed in this paper can save 28.04% of the fan energy consumption compared with the traditional control strategy.