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.
The autonomous, optimal control for HVAC systems has attracted great research attentions in the studies of smart and sustainable buildings @cite_12 , which is to determine the optimal solutions (operation mode and setpoints) that minimize overall energy consumption or operating cost while still maintaining the satisfied indoor thermal comfort and healthy environment @cite_6 .
@cite_6 @cite_12
2104957212 2594724848 2018614621 2119681812
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. The goal of maintaining users’ thermal comfort conditions in indoor environments may require complex regulation procedures and a proper energy management. This problem is being widely analyzed, since it has a direct effect on users’ productivity. This paper presents an economic model-based predictive control (MPC) whose main strength is the use of the day-ahead price (DAP) in order to predict the energy consumption associated with the heating, ventilation and air conditioning (HVAC). In this way, the control system is able to maintain a high thermal comfort level by optimizing the use of the HVAC system and to reduce, at the same time, the energy consumption associated with it, as much as possible. Later, the performance of the proposed control system is tested through simulations with a non-linear model of a bioclimatic building room. Several simulation scenarios are considered as a test-bed. From the obtained results, it is possible to conclude that the control system has a good behavior in several situations, i.e., it can reach the users’ thermal comfort for the analyzed situations, whereas the HVAC use is adjusted through the DAP; therefore, the energy savings associated with the HVAC is increased. HVAC systems are the major energy consumers in buildings. Operation and control of HVAC systems have significant impacts on the energy or cost efficiency of buildings besides their designs. Buildings nowadays are mostly equipped with comprehensive building automation systems (BASs) and building energy management control systems (EMCSs) that allow the possibility of enhancing and optimizing the operation and control of HVAC systems. Supervisory and optimal control, which addresses the energy or cost-efficient control of HVAC systems while providing the desired indoor comfort and healthy environment under the dynamic working conditions, is attracting more attention of the building professionals and the society and provides incentives to make more efforts in developing more extensive and robust control methods for HVAC systems. This paper provides a framework for categorizing the main supervisory and optimal control methods and optimization techniques developed and or utilized in the HVAC field. The applicat... We study the problem of heating, ventilation, and air conditioning (HVAC) control in a typical commercial building. We propose a model predictive control (MPC) approach which minimizes energy use while satisfying occupant comfort and actuator constraints by using predictive knowledge of weather and occupancy.