The HVAC systems in subway stations are energy consuming giants, each of which may consume over 10, 000 Kilowatts per day for cooling and ventilation. To save energy for the HVAC systems, it is critically important to firstly know the "load signatures" of the HVAC system, i.e., the quantity of heat imported from the outdoor environments and by the passengers respectively in different periods of a day, which will significantly benefit the design of control policies. In this paper, we present a novel sensing and learning approach to identify the load signature of the HVAC system in the subway stations. In particular, sensors and smart meters were deployed to monitor the indoor, outdoor temperatures, and the energy consumptions of the HVAC system in real-time. The number of passengers was counted by the ticket checking system. At the same time, the cooling supply provided by the HVAC system was inferred via the energy consumption logs of the HVAC system. Since the indoor temperature variations are driven by the difference of the loads and the cooling supply, linear regression model was proposed for the load signature, whose coefficients are derived via a proposed algorithm . We collected real sensing data and energy log data from HaiDianHuangZhuang Subway station, which is in line 4 of Beijing from the duration of July 2012 to Sept. 2012. The data was used to evaluate the coefficients of the regression model. The experiment results show typical variation signatures of the loads from the passengers and from the outdoor environments respectively, which provide important contexts for smart control policies.
In order to examine the factors which affect the range of heat transfer in earth surrounding subways, FLAC3D was adopted in this study to analyze these factors, under different conditions, in a systematic manner. When we compare these numerical tests, the results show that the main factors, affecting the heat transfer range are the thermal properties of the surrounding earth, the initial ground temperature and the temperature in the tunnel. The heat transfer coefficient between air and linings has little effect on the temperature distribution around the tunnel. The current results can provide a reference for improving the thermal environment in subways and optimizing the design of subway ventilation and air conditioning. Because of large numbers of passengers,Beijing subway has a great demand for huge power consumption.It is necessary to search possibilities of reducing the power consumption of Beijing Subway.This paper analyzed the actual power consumption of each subway line,the electric power used for powering the trains and all secondary consumption of electric power for operating the train stations of Beijing subway in detail.Additionally,the water consumption of each subway line of Beijing subway was also analyzed.The results provide a scientific basis for the design and operation of energy-saving subways. Airborne viable and non-viable measurements were carried out in two different metro stations, one located in a tunnel and the other on the surface. The concentrations of airborne total viable bacteria (incubated at 37°C and 22°C), staphylococci, suspended dust and oxidants (ozone) were higher in the air of the tunnel station than those recorded at the surface station. In contrast, spore forming bacteria, Candida spp, fungi and actinomycetes were found at slightly higher levels in the surface station than in the tunnel station. A statistically significant difference (p<0.01) was found between the levels of suspended dust at both stations. Cladosporium, Penicillium and Aspergillus species were the dominant fungi isolates. Fusarium, Aspergillus and Penicillium are the most common fungi that produce toxins. Under certain circumstances (host susceptibility, infective dose and aerodynamic diameter) some of the airborne microorganisms e.g. actinomycetes and Aspergillus species and staphylococci may cause health problems in exposed persons based on toxic or allergic reactions.