Towards a Real-Time Predictive Management Approach of Indoor Air Quality in Energy-Efficient Buildings

Ventilation, heating and air conditioning systems are the main energy consumers in building sector. Improving the energy consumption of these systems, while satisfying the occupants’ comfort, is the major concern of control and automation designers and researchers. Model predictive control (MPC) methods have been widely studied in order to reduce the energy usage while enhancing the occupants’ comfort. In this paper, a generalized predictive control (GPC) algorithm based on controlled auto-regressive integrated moving average is investigated for standalone ventilation systems’ control. A building’s ventilation system is first modeled together with the GPC and MPC controllers. Simulations have been conducted for validation purposes and are structured into two main parts. In the first part, we compare the MPC with two traditional controllers, while the second part is dedicated to the comparison of the MPC against the GPC controller. Simulation results show the effectiveness of the GPC in reducing the energy consumption by about 4.34% while providing significant indoor air quality improvement

A predictive control approach for thermal energy management in buildings

Building equipment accounts for almost 40% of total global energy consumption. More than half of which is used by active systems, such as heating, ventilation and air conditioning (HVAC) systems. These latter are responsible for the occupants’ well-being and considered among the main consumers of electricity in buildings. In order to improve both occupants’ comfort and energy efficiency in buildings, optimal control oriented models, such as Model Predictive Control (MPC), have proven to be promising techniques for developing intelligent control strategies for building energy management systems. This paper presents a real-time predictive control approach of an air conditioning (AC) system for thermal regulation in a single-zone building using MPC control framework. The proposed approach takes into account the physical parameters of the building, weather predictions (i.e. ambient temperature and solar radiation) and time-varying thermal comfort constraints to maintain optimal energy consumption of the AC while enhancing occupants’ comfort. For this purpose, a control-oriented thermal model for a room integrated with AC system is first developed using physics-based (white box) technique and then used to design and develop the MPC controller model. A numerical case study has been investigated and simulation results show the effectiveness of the proposed approach in reducing the energy consumption by about 68% while providing a significant indoor thermal improvement. A conventional On–Off controller was used as a baseline reference to evaluate the system performance against the proposed approach

Internet-of-Things Based Hardware-in-the-Loop Framework for Model-Predictive-Control of Smart Building Ventilation

International audienceIn this work, a Hardware-In-the-Loop (HIL) framework is introducedfor the implementation and the assessment of predictive controlapproaches in smart buildings. The framework combines recentInternet of Things (IoT) and big data platforms together withmachine-learning algorithms and MATLAB-based Model PredictiveControl (MPC) programs in order to enable HIL simulations. As acase study, the MPC algorithm was deployed for control of astandalone ventilation system (VS). The objective is to maintainthe indoor Carbon Dioxide (CO2) concentration at the standardcomfort range while enhancing energy efficiency in the building.The proposed framework has been tested and deployed in a real-casescenario of the EEBLab test site. The MPC controller has beenimplemented on MATLAB/Simulink and deployed in a Raspberry Pi (RPi)hardware. Contextual data are collected using the deployed IoT/bigdata platform and injected into the MPC and LSTM machine learningmodels. Occupants’ numbers were first forecasted and then sent tothe MPC to predict the optimal ventilation flow rates. Theperformance of the MPC control over the HIL framework has beenassessed and compared to an ON/OFF strategy. Results show theusefulness of the proposed approach and its effectiveness inreducing energy consumption by approximately 16%, while maintaininggood indoor air quality