دانلود رایگان مقاله انگلیسی به سوی روش هوشمند برای کنترل سیستم های تهویه با استفاده از فناوری های اینترنت اشیا و کلان داده - الزویر 2018

Abstract

Heating, ventilation and air conditioning systems are generally deployed in buildings for maintaining occupants’ comfort. They are the most considered systems in improving the energy saving while sustaining occupants’ comfort. Several approaches have been proposed, in the past few years, to develop an optimal control for ventilation systems. However, these approaches could not be efficiently performed under diverse contexts. In fact, we introduce an intelligent approach that selects the most appropriate control among three existing strategies. This paves the way to approaches in which an antifragile platform learns and adapts which strategy to enact. The proposed approach is implemented using IoT devices and recent Big-data technologies for real-time monitoring and data processing. A case study was deployed in our EEBLab test site for real testing. Experiments have been conducted and preliminary results show the effectiveness of using adaptive control approaches for ventilation systems control.

5. Case study and experiment results

The main goal of this experiment is to control the ventilation system in order to maintain the indoor air quality and the thermal comfort of the building’s occupants while improving energy savings. We considered only one day for easy visualization of the results. We have evaluated two main metrics: i) the comfort metrics (e.g. the actual indoor CO2 concentration, the indoor air temperature and the relative humidity), ii) the energy metrics (e.g. ventilation rates, rotation of fans and the power consumption). A real testing scenario was deployed in our EEBLab to measure and control the ventilation system in the winter period as illustrated in Figure 5.

Preliminary results are depicted in Figure 6 as follows: CO2 concentration (Figure 6 (a)), the relative humidity (Figure 6 (b)) and the indoor air temperature (Figure 6 (c)). As shown in these figures, unlike using individual control strategy the proposed control approach selects among three different control strategies: state feedback, the PID and the ON/OFF. When the room is unoccupied and the indoor air quality is good there is no need to operate the ventilation system. For instance, the ventilation rate responds rapidly in ON state when the CO2 is more than 1000ppm, while the PID and the state feedback still switching in order to satisfy the thermal comfort among the average of the outside temperature. However, as illustrated in Figure 6 (d), the speed of the ventilation is varying according to the control strategy used, but it does not exceed its maximum rate (fixed to 0.5 m3 /s). Furthermore, as depicted in Figure 6 (e), the average energy consumption is reduced by 30.25% when using an individual control strategy.