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Monthly electricity consumption of cooling system – TRNSYS vs. Hardware-in-the-loop, Improved Case The results from both test beds show energy saving potential of various control strategies. On TRNSYS test bed, the annual electricity consumption of Improved Case is 43.1% less than that of Baseline Case; and energy consumed by boilers is 36.5% less. On hardware-in-the-loop test bed, the annual electricity consumption of Imp roved Case is 42.3% less than that of Baseline Case and energy consumed by boilers is 35.0% less. 

Monthly electricity consumption of cooling system – TRNSYS vs. Hardware-in-the-loop, Improved Case The results from both test beds show energy saving potential of various control strategies. On TRNSYS test bed, the annual electricity consumption of Improved Case is 43.1% less than that of Baseline Case; and energy consumed by boilers is 36.5% less. On hardware-in-the-loop test bed, the annual electricity consumption of Imp roved Case is 42.3% less than that of Baseline Case and energy consumed by boilers is 35.0% less. 

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Conference Paper
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Advanced control systems are widely applied in modern buildings so as to meet the requirements of energy conservation and higher level of indoor environmental quality. The building control strategies need to be assessed and verified before applying to buildings, by either experiments or simu lation. Verificat ion by simulat ion can saves time and l...

Citations

... Recognizing this problem with the scalability of the hardware interface, some researchers (Pan et al. 2011, Pang et al. 2012, and Pang et al. 2016) proposed a CIL architecture using a software interface ( Figure 2). The software interface is middleware having the following functionalities: 1) communicate with both the control hardware and the simulation for the desired inputs and outputs and 2) synchronize the sampling time step used by the controller with the computing time step used by the simulation. ...
... Controller-in-the-loop simulation using a software interface In the work byPan et al. (2011), an Open Platform Communications (OPC)-based middleware was developed to couple TRNSYS simulation software (SEL 2017) with the Siemens APOGEE building automation system(Siemens 2017). Specifically, a customized TRNSYS Type was developed to call OPC clients that can read/write BAS point values from/to the OPC server installed on a BAS workstation. ...
... The OPC server writes and reads data in controllers connected to the BAS workstation. Using the resulting CIL simulation test bed,Pan et al. (2011) evaluated the impact of different control strategies on HVAC energy consumption for a 3-story office building.Pang et al. (2012) proposed a real-time simulation framework using the Building Controls Virtual Test Bed (BCVTB)(Wetter 2011) as the middleware. The BCVTB has BACnet modules that allow direct reading from and writing to BACnetcompatible controllers. ...
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In general, a hardware-in-the-loop (HIL) building simulation has lower cost and fewer practical limitations (e.g., scheduling issues) than field tests in occupied buildings, while also overcoming limitations of simulations alone by capturing the full behavior of some physical systems, equipment, and components. However, the implementation of an HIL can be difficult due to the scarcity of appropriate tools. This paper presents an agent-based framework for HIL simulation. It can be used for investigation of controller performance via controller-in-the-loop simulations and also HIL for system synthesis. In the latter case, both controllers and major equipment participate in tests to ensure that dynamics of equipment operation are correctly captured in addition to controller performance. The HIL simulation framework presented allows such actual physical parts to be included in the framework while representing others for which behaviors are better known and modeled in simulation models. The mechanism implemented in the framework to synchronize simulations in software with real-time operation of physical equipment is described. As an example, use of the HIL simulation framework is illustrated through a brief study of speed control of the supply fan in the air handling unit of a variable-air-volume building heating, ventilating and air-conditioning system.
... The performance of the proposed event-driven optimization method is evaluated by comparing with the conventional time-driven optimization method using computer simulation since it is cheaper and faster compared with experiments [15]. The virtual HVAC system representing the real building HVAC system (modeled in TRNSYS [16]) is used to produce the online operation data (i.e., status data), while the control settings are computed in a separate MATLAB platform. ...
Conference Paper
Full-text available
In large and complex HVAC systems, control optimization is always adopted to improve the operational efficiency since a small increase in the operational efficiency may lead to substantial energy savings. As the HVAC system becomes more and more complex, the real-time optimization of the system operation becomes a challenge due to the computational complexity. Almost all of the developed optimization methods are time-driven, in which the optimization is driven by “time” with a fixed optimization frequency. It is well-known that optimization should be done when the operational condition experiences a significant change, which may cause the current settings not optimal. Hence, “time” may not be the real optimization driver and the fixed optimization frequency may lead to unnecessary or delayed actions. To overcome these limitations, this paper proposes an event-driven optimization method, which originates from the event-driven control in control engineering. The key idea of the event-driven method is to use “event”, rather than “time”, as the optimization trigger. The “event” should be a well-defined condition, reflecting the system state or the state change. Optimizations will be conducted only when predefined events happened. The computation load and the energy saving of the proposed method are compared with that of a time-driven method by simulation. The results show that the computation loads of the proposed method are greatly reduced (up to 90%) compared with the time-driven method. The proposed method saves 10.65% of energy consumption based on the benchmark (no optimization is conducted), while the time-driven method saves 10.01%.
... The SIMBAD (SIMulator for Buildings and Devices) environment [209] allows various plug loads for the simulation and optimization purposes. However, very little attention has been given to real time building simulations, which allow for physical devices and real conditions [226]. This has been provided for through the Power Hardware-inthe-loop (PHIL) for the SEB's testing and validation. ...
... Others use building emulator like TRNSYS simulation program [3] or SIMBAD (SIMulator for Buildings and Devices) environment [4]… But, only few of them deal with real-time dwelling simulation and Power Hardware-in-the-loop (PHIL) for the BEMS test and validation. Such a simulator uses real conditions and physical devices [5]. ...
Conference Paper
Building Energy Management Systems (BEMS) have been developed in order to improve dwelling energy efficiency, which is seen as a compromise between energy cost and overall comfort. A BEMS optimize the control of energy supply and demand according to forecasting data. This paper proposes new BEMS strategies to support Demand Side Management and validate them using a real time platform: A Power-Hardware-In the-Loop (PHIL) test bench. Physical devices are introduced into the simulated system to check whether they can support the control strategies.