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.
This goal is the same in the subway HVAC control systems. Because the HVAC systems contain different types of subsystems, such as gas-side and water-side subsystems, the optimal control problems of HVAC are extremely difficult. One of the difficulties is the lack of an exact model to describe the internal relationships among different components. A dynamic model of an HVAC system for control analysis was presented in @cite_0 . The authors proposed to use Ziegler-Nichols rule to tune the parameters to optimize PID controlle. A metaheuristic simulation–EP (evolutionary programming) coupling approach was developed in @cite_5 , which proposed evolutionary programming to handle the discrete, non-linear and highly constrained optimization problems. Multi agent-based simulation models were studied in @cite_14 to investigate the performance of HVAC system when occupants are participating. In @cite_4 , swarm intelligence was utilized to determine the control policy of each equipment in the HVAC system.
@cite_0 @cite_5 @cite_14 @cite_4
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This paper describes a procedure for deriving a dynamic model of an HVAC system that consists of a zone, heating coil, cooling and dehumidifying coil, humidifier, ductwork, fan, and mixing box. In particular, the interest is centered on control strategies to reduce energy consumption and improving the quality of the indoor environment. Indoor temperature and humidity may be maintained at set point values by an air-handling unit using a PID control action. The PID parameters must be carefully tuned to produce less oscillatory responses. The tuning technique, using the Ziegler-Nichols rule, is investigated from a practical viewpoint. Simulation results showing the open loop and the closed loop responses of indoor temperature and humidity ratio are given. The results show that the system is capable of controlling the disturbance efficiently within a small period of time and with less error. The dynamic model can be especially useful for control strategies that require knowledge of the dynamic characteristics of HVAC systems. Abstract Energy management of heating, ventilating and air-conditioning (HVAC) systems is a primary concern in building projects, since the energy consumption in electricity has the highest percentage in HVAC among all building services installations and electric appliances. Without sacrifice of thermal comfort, to reset the suitable operating parameters, such as the chilled water temperature and supply air temperature, would have energy saving with immediate effect. For the typical commercial building projects, it is not difficult to acquire the reference settings for efficient operation. However, for some special projects, due to the specific design and control of the HVAC system, conventional settings may not be necessarily energy-efficient in daily operation. In this paper, the simulation-optimization approach was proposed for the effective energy management of HVAC system. Due to the complicated interrelationship of the entire HVAC system, which commonly includes the water side and air side systems, it is necessary to suggest optimum settings for different operations in response to the dynamic cooling loads and changing weather conditions throughout a year. A metaheuristic simulation–EP (evolutionary programming) coupling approach was developed using evolutionary programming, which can effectively handle the discrete, non-linear and highly constrained optimization problems, such as those related to HVAC systems. The effectiveness of this simulation–EP coupling suite was demonstrated through the establishment of a monthly optimum reset scheme for both the chilled water and supply air temperatures of the HVAC installations of a local project. This reset scheme would have a saving potential of about 7 as compared to the existing operational settings, without any extra cost. Building information modeling is only beginning to incorporate human factors, although buildings are sites where humans and technologies interact with globally significant consequences. Some buildings fail to perform as their designers intended, in part because users do not or cannot properly operate the building, and some occupants behave differently than designers expect. Innovative buildings, e.g., green buildings, are particularly susceptible to usability problems. This paper presents a framework for prospectively measuring the usability of designs before buildings are constructed, while there is still time to improve the design. The framework, which was implemented as an agent-based computer simulation model, tests how well buildings are likely to perform, given realistic occupants. An illustrative model for lighting design shows that this modeling approach has practical efficacy, demonstrating that, to the extent that users exhibit heterogeneous behaviors and preferences, designs that allow greater local control and ease of operation perform better. Heating, ventilating and air conditioning (HVAC) systems have played an important role in building energy and comfort management. It is designed to provide a relatively constant and comfortable temperature in buildings and provide fresh and filtered air with a comfortable humidity level. In this paper, an optimal control strategy is proposed to control the HVAC system for maintaining building's indoor environment with high energy efficiency. The control strategy utilized swarm intelligence to determine the amount of energy dispatched to each equipment in the HVAC system. In order to study the impact of HVAC system operations in the indoor environment, both the building model and HVAC equipment models are developed. A case study is carried out to simulate the real time control process in a specified building environment.