3I Buildings: Intelligent, Interactive and Immersive Buildings

Abstract

This research presents the architecture of a technology platform capable of integrating different types of data from building sensors and providing an interface to manage and operate facility devices, which is supported by advanced optimization algorithms. This interface is potentiated by a BIM-based interface presenting real-time data of the building. The solution, called 3i buildings -Intelligent, Interactive, and Immersive Buildings, is a tool to monitor and manage smart buildings, as well as optimize users experience, energy consumptions and environment quality. This is achieved by a grid of sensors and devices that continuously gather information (structural conditions of the building, occupancy, comfort of occupants, energy consumptions and CO2, COV's and Humidity levels, etc.), which is processed by predictive models able to learn over time. The 3D representation of the models allows managers to take advantage of the virtual environment, by augmenting the facility model and including information about the facility, making it easier and perceptible to users and owners, helping them to make better decisions. To support our research, the system will be installed in three different environments, Luz's hospital, Lisbon Aquarium and Norte Shopping, to test the solution under different conditions, objectives and users. In the first two cases the objectives are to monitor building air quality, consumptions and occupancy and in the Norte Shopping case the objectives are to monitor people flows, interact with tem and help the response in case of crisis according to the adopted emergency plan. These types of systems might help reducing energy consumptions as well as increasing comfort and satisfaction of occupants, maintaining a constant concentration of CO2 and humidity within the facility. The optimized algorithms will allow the system to learn, predicting and reacting to different conditions, giving a more reliable and smooth response to occupants needs.

Implementation of predictive control in a commercial building energy management system using neural networks

“…In terms of analysis, existing BEMS are only capable of carrying out simple data analysis and visualization functions [12]. Hence, they cannot learn over time [19] or predict future events or scenarios. In addition, the interoperability of the BEMS available on the market is very low, and this makes it difficult to implement external control modules [20].…”

Building Performance and Occupancy Evaluation for Public Building Stock Management: A State of the Art

“…To date, the use of sensors as a part of POEs to monitor existing buildings have been mostly focused on analyzing energy performances and consumptions and indoor environmental quality (IEQ) (Costa et al 2015, Delzendeh et al 2017, Yan et al 2017, Saralegui et al 2018, Demian et al 2018, Rogage et al 2019, Wang et al 2019a, Wang et al 2019b. Recently, applications of sensors to detect occupancy flows have been explored, mainly referring to building energy consumption analyses and predictive models (Diraco et al 2015, Yan et al 2017, Saralegui et al 2018, Rouleau et al 2019, Wang et al 2019b, as presented in Table 1.…”

“…The integration of POEs in a BIM approach enables the connection between POE data and the building model (Costa et al 2015, Machado et al 2017, Rogage et al 2019, with sensors' data and ML-based occupancy predictions assigned to each room of the model (Pin et al 2018, Rogage et al 2019. The integration in a BIM approach has the following advantages:…”

BIM and Post-occupancy Evaluations for Building Management System: Weaknesses and Opportunities

“…Diagnostic POEs are detailed analyses involving widespread and continuous monitoring, defining consistent databases (Preiser 2010). Monitoring by means of sensors systems, mostly used to verify building energy consumptions (Marzouk and Abdelaty 2014;Costa et al 2015;Demian et al 2018), can provide objective data concerning the building usage pattern. Sensors technologies allow to compare actual use and consumptions with the building performance as obtained through simulations during the design phase (Straka and Aleksic 2009).…”