Enhancing Space Management through Digital Twin: A Case Study of the Lazio Region Headquarters
Abstract
:1. Introduction
1.1. Advanced Digital Technologies
1.2. Space Management in Smart Working Adoption
2. Materials and Methods
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- Strategy 1: Office first. The majority of employees work in the office, with the option of working remotely for a few days a week (e.g., 3–4 days in the office and 1–2 remotely).
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- Strategy 2: Virtual first. The majority of employees work remotely, with the option of travelling to the office for specific purposes, such as internal meetings or client-facing activities.
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- Strategy 3: Total hybrid. Employees are afforded the autonomy to determine the extent of their work in the office or remotely, according to their individual needs. This approach is the most flexible, yet also the most complex to manage in terms of infrastructure and methodology.
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- Enable interoperability with room reservation systems, shared calendars, and room edge systems to monitor reservations.
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- Enable real-time monitoring of occupancy by tracking occupancy flows. The facility manager can not only plan the reconfiguration of rooms, but also set up customized cleaning and disinfection systems for individual areas.
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- Provide real-time monitoring of environmental quality in terms of health and comfort conditions (temperature, humidity, lighting, and IAQ).
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- Provide data correlation analysis through business intelligence (BI) systems to define occupancy strategies aimed at correct space allocation and reducing energy and service costs.
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- Climate;
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- Building envelope;
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- M&E systems;
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- Interior design criteria and operation;
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- Maintenance of building energy use;
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- Occupant behavior.
- Variations due to the active interaction of occupants with building systems. The active interaction of occupants with building energy systems is a complex mechanism, where numerous parameters, including the number of occupants, occupant behavior, type of space, type of work, day, and time, exert a significant influence. The aforementioned parameters can be classified into three distinct groups: temporal, spatial, and occupancy. Temporal parameters indicate the time scale, while spatial parameters indicate spatial information. Occupancy parameters, on the other hand, indicate occupant behavior. It can be reasonably argued that psychological and sociological aspects exert a considerable influence on occupant behavior. Given the inherent unpredictability of occupants’ behavior, it remains challenging to identify any specific circumstances that may influence the consumption of energy. This uncertainty has a significant impact on the indoor environment, which in turn contributes to the observed variations in energy consumption. Other factors that influence occupants’ behavior include temperature, humidity, illuminance factor, and several others, which collectively affect the building’s energy consumption. The most common forms of occupant behavior are adjusting the thermostat, dimming the lighting, switching off lights, and using electronic devices.
- Variations due to the passive interaction of occupants with building systems. Passive interaction depends on the presence of occupants, reported as hours of occupancy in the building, and the absence of occupants, reported as hours of unoccupied building operation. It is possible that there is inefficient operation of the electrical and mechanical systems, which would result in high energy wastage during the unoccupied/occupied hours; this issue was further investigated to estimate the amount of energy wasted during unoccupied hours, with between 26% and 65% of energy being used during unoccupied/occupied hours, compared to working hours between 7.30 a.m. and 5 p.m. Of the total consumption, between 19% and 28% (mainly electricity and HVAC) is used during the weekend when the building is unoccupied [44].
2.1. Methodology
- Space analysis via the BIM model: Through the use of IoT sensors, which can be easily installed and configured, it is possible to start the occupancy assessment process.
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- Use analysis: Evaluation of office space use to identify inefficiencies or cases of overutilization.
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- Occupancy measurement: Determine the occupancy percentage for each floor, with a detailed map of space use.
- Space analysis and management (statistics): Using data obtained from a dedicated booking application, through the utilization of BIM-integrated software, facilitates the examination of the actual use of workspaces, with the objective of optimizing their management. The specific objectives are as follows:
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- Presence forecasting: Estimating building occupancy over the next 30 days based on historical booking data, allowing better maintenance services and the anticipation of space and resource requirements.
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- Savings analysis: Quantification of potential maintenance cost savings through appropriate operational downsizing. By analyzing space utilization, it is possible to identify areas that can be consolidated or downsized, thus reducing operating costs.
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- Duration of bookings: Calculation of the average duration of room bookings to optimize space availability. Knowing the typical duration of bookings allows you to better plan the use of meeting rooms, avoiding overlaps and improving efficiency in resource allocation.
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- Temporal assessment of room use: Examination of meeting room usage by time of day and day of the week and the identification of high- and low-occupancy periods. This provides a better understanding of space utilization patterns, allowing for more flexible and adaptive management.
- Insight and automated alerts: Provide automatic alerts to the building manager [55] to improve space management [56]. These alerts signal when there is a high probability that a particular floor or the entire building will reach 100% occupancy on a particular day or period, allowing for more efficient planning of the necessary resources.
- Machine learning: This stage uses ML to make reliable predictions and generate insights [57], going beyond the traditional programming approach [58]. The system learns from the relationships between data and outcomes, identifying patterns and connections that may not be obvious to human intelligence [59]. This ‘knowledge discovery’ process not only provides useful results, but can also reveal new, previously unknown/ignored/unexpected information.
2.1.1. Analysis of Spaces via the BIM Model—Phase 1
2.1.2. Optimizing Space Management through Data Analysis (Statistics)—Phase 2
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- A dataset of Italian holidays up to the year 2100 (Figure 9);
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- Maximum capacity of each floor of the building;
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- Detailed links between location codes, floors, and tenants.
2.1.3. Insight and Automatic Alarms—Phase 3
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- Season (winter, spring, summer, autumn);
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- Reference month;
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- Bank holidays, weekdays, 1-day bank holidays, and 2-day bank holidays;
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- Days of the week.
2.1.4. Machine Learning for Enhanced Predictive Insights—Phase 4
3. Results and Discussion
3.1. Case Study: Lazio Region Headquarters
3.2. Digital Transformation in Modern Work Environments
4. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Floor | Available Stations | Maximum Detected | Unused Stations |
---|---|---|---|
0 | 50 | 36 | 14 |
1 | 120 | 104 | 16 |
2 | 120 | 105 | 15 |
3 | 120 | 97 | 23 |
4 | 120 | 102 | 18 |
5 | 120 | 88 | 32 |
6 | 120 | 91 | 29 |
7 | 120 | 106 | 14 |
8 | 120 | 78 | 42 |
9 | 120 | 58 | 62 |
10 | 120 | 66 | 54 |
11 | 120 | 60 | 60 |
Total | 1370 | 991 | 379 |
ANTE, 28.600 m2 | POST, 20.800 m2 | |||||||
---|---|---|---|---|---|---|---|---|
kWh/Year | MWh/Year | Costs [EUR/Year] | t CO2/Year | kWh/Year | MWh/Year | Costs [EUR/Year] | t CO2/Year | |
Lighting, electrical devices | 400,400.00 | 400.40 | 100,100.00 | 774.77 | 291,200.00 | 291.20 | 72,800.00 | 563.47 |
Domestic hot water | 286,000,00 | 286.00 | 100,100.00 | 27.17 | 208,000.00 | 208.00 | 72,800.00 | 19.76 |
Cooling | 143,000,00 | 143.00 | 35,750.00 | 774.77 | 104,000.00 | 104.00 | 26,000.00 | 563.47 |
Heating | 1,115,400.00 | 1115.40 | 278,850.00 | 774.77 | 811,200.00 | 811.20 | 202,800.00 | 563.47 |
Total | 1,944,800.00 | 1944.80 | 514,800.00 | 2351.49 | 1,414,400.00 | 1414.40 | 374,400.00 | 1710.18 |
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Piras, G.; Muzi, F.; Tiburcio, V.A. Enhancing Space Management through Digital Twin: A Case Study of the Lazio Region Headquarters. Appl. Sci. 2024, 14, 7463. https://doi.org/10.3390/app14177463
Piras G, Muzi F, Tiburcio VA. Enhancing Space Management through Digital Twin: A Case Study of the Lazio Region Headquarters. Applied Sciences. 2024; 14(17):7463. https://doi.org/10.3390/app14177463
Chicago/Turabian StylePiras, Giuseppe, Francesco Muzi, and Virginia Adele Tiburcio. 2024. "Enhancing Space Management through Digital Twin: A Case Study of the Lazio Region Headquarters" Applied Sciences 14, no. 17: 7463. https://doi.org/10.3390/app14177463