روش هوشمند کنترل سیستم های تهویه با استفاده از فناوری های اینترنت اشیا و تهویه ه Towards an Intelligent Approach for Ventilation Systems Control using IoT and Big Data Technologies

1. Introduction Buildings consume over 40 percent of the total energy consumption. Among Building’s services, Heating, ventilation and air conditioning (HVAC) systems are generally used for maintaining occupants’ comfort, but they are the most considered systems in improving the energy saving1 . In this work, we focus on standalone ventilation systems, which are highly energy demanded if not well controlled2 . Several approaches have been proposed, in the past few years, to develop optimal control for ventilation systems. These approaches could be categorized into two main families: static and dynamic control approaches. Static approaches operate at a fixed rate based on either timetriggered or fixed threshold values. The fresh air is injected into the specific area by varying the ventilation rates, but this injection could result in higher energy usage with humidity increases, which could affect the indoor air quality. However, dynamic approaches use contextual data to adjust the ventilation rates accordingly. These approaches consider that wireless sensors network technologies could be integrated with context-driven services into a holistic platform for controlling ventilation systems. These approaches could lead to the most energy saving than static approaches mainly in building spaces having dynamic and unpredictable occupancy (e.g., meeting areas, auditoriums). The CO2 sensors could be, for example, used to monitor in real-time indoor CO2 concentration that will be processed and used by a context-driven control service in order to adjust the amount of needed ventilation air rates to best suit the actual building occupancy (e.g., presence, number, behavior). Mainly, this paves the way to approaches in which an antifragile platform learns and adapts which strategy to enact13. In fact, the platform makes the best of the experiences gathered while interacting with building’s occupants and the deployed things10,11. In our previous work, we have investigated several control approaches and then presented a CO2-based strategy using a state feedback for controlling ventilation systems in energy-efficient buildings3,4. The principal objective of the developed controller is to improve optimal balance between energy efficiency and indoor air quality by maintaining the indoor CO2 concentration at the comfort set point with an efficient ventilation rate while reducing energy consumption. Basically, we compared the proposed feedback control approach with a PID and an ON/OFF controller and validated their performance using the BCVTB co-simulation framework together with building environment parameters. Obtained simulation results showed that the state feedback control leads to better comfort with improved energy saving as compared to the PID and ON/OFF control strategies. Furthermore, we have implemented and deployed the CO2-based state feedback control into our EEBLab test site. Experiments have been conducted and obtained results showed the usefulness of dynamic regulation of ventilation rates. Mainly, results showed that the CO2-based state feedback control is able to maintain the CO2 concentration in the comfortable zone, while minimizing energy consumption by almost 47% against the ON/OFF based control approach.