A Comprehensive Review of Sensor-Based Smart Building Monitoring and Data Gathering Techniques
Abstract
:1. Introduction
- Indoor climate monitoring—helps to identify energy waste, allowing for corrective actions to prevent it, thereby reducing unnecessary energy costs and CO2 emissions. It also generates the data necessary for environmental, social, and governance (ESG) reporting as well as uncovers new energy efficiency insights.
- Heating optimization—facilitates real-time temperature monitoring across various sections of a building, allowing for the regulation of district heating systems. This automated, real-time optimization process leads to substantial energy savings and enhances the comfort of tenants, facility managers, and other building occupants.
- Air quality monitoring—delivers real-time data on temperature, humidity, CO2 level, volatile organic compounds (VOCs), and atmosphere pressure. This allows building owners to create a healthier and more comfortable indoor climate for tenants. Collecting temperature data from across the building enables managers to identify hot and cold spots, allowing them to take proactive measures to reduce energy consumption, CO2 emissions, and cost.
- The behaviors of building occupants;
- Sensor allocation;
- Implementation complexity and precision of the studied methods.
2. Smart Building Monitoring and Control System Architecture
3. Smart Building Monitoring Applications
3.1. Lighting Management
- Time scheduling—uses a schedule with a determined time when lights should be turned on and off automatically. This strategy is good for spaces that are constantly occupied during a predetermined time and unoccupied for the rest of the day.
- Daylight harvesting—measures total illuminance from daylight and artificial lighting to adjust luminaire brightness to reach the required illuminance level in a room. This strategy shows high effectiveness for rooms with high daylight availability.
- Occupancy control—turns on lighting automatically when human presence is detected and switches off the light when there is no human presence. This approach is typically used in areas that are occupied intermittently, such as stairwells, break rooms, conference rooms, and restrooms.
3.2. HVAC Systems Management
- Temperature;
- Humidity;
- Air distribution;
- Indoor air quality.
- Using energy-efficient equipment;
- Initially designing buildings to reduce heating and cooling loads;
- Using demand-controlled ventilation. This strategy is often implemented by using CO2 sensors. Air ventilation is adjusted based on CO2 concentration;
- Using solar cooling and refrigeration. This approach converts solar energy to power cooling appliances. This strategy’s advantage is not only that it relies on renewable energy sources but also, as cooling needs increase on the hottest summer days, the amount of available solar energy increases as well;
- Using desiccant-based cooling systems. These systems provide cooling without refrigeration.
- Presence sensors like passive infrared (PIR);
- Radio-frequency identification (RFID) tags, which must be worn by all people using the building;
- Optical cameras;
- Interior parameter measurements like air temperature, CO2, humidity, and acoustics, which are dependent on human presence.
3.3. Energy Management
- Appliances with binary power states (ON/OFF);
- Appliances with multiple and finite power states;
- Appliances with multiple power states, including infinite intermediate states;
- Appliances with a constant power consumption that stays ON for days or weeks.
3.4. Water Consumption End-Use Monitoring
- Mechanical or positive displacement flow meters: have mechanical parts that are displaced or rotated when water flows through them;
- Vortex flow meters: utilize the vortex shedding principle, where a bluff object is placed in the way of liquid flow, which creates alternating vortices downstream;
- Electromagnetic flow meters: generate an electromagnetic field, which fluctuates when conductive fluid flows through them;
- Ultrasonic flow sensors: these sensors operate similarly to radar, sending sound waves in both downstream and upstream directions to measure the time difference, which is then used to calculate the volumetric flow rate.
- Turbine flow meter: employs a turbine whose rotation speed is directly proportional to the flow rate.
- Single- and multi-jet flow meters: employ single or multiple impellers, respectively.
4. Occupancy Sensing
- Spatial (zone) resolution: building, floor, room;
- Temporal resolution: day, hour, minute, second;
- Occupancy resolution:
- Level 1: occupancy: at least one person in a zone;
- Level 2: count: the number of people present in a zone;
- Level 3: identity: who they are;
- Level 4: activity: what actions they are performing.
- PIR sensors—detect changes in thermal radiation on their surface caused by the movement of occupants. They are low-cost, energy-efficient, and easy to deploy in environments. PIRs are usually wireless, powered by batteries or photovoltaic cells. The operation of PIR sensors in lighting control works as follows: the lighting system turns on the lights when movement is detected and switches them off if no movement is detected for a specified period [46]. The notable drawback of PIR sensors is their poor performance at detecting small motions. Additionally, since PIR sensors work by detecting heat emitted from humans, they may fail when the weather is hot or when placed close to the HVAC system [18,48].
- Ultrasonic sensors—operate by continuously emitting high-frequency sound waves. When any motion is present, the sensor detects the frequency of sound shifts, called Doppler shift. Ultrasonic sensors are more power-consuming compared to PIR sensors but have a broader coverage area, which means that ultrasonic sensors are more suitable for large spaces like conference rooms while PIR sensors are often used in small rooms and corridors. Also, ultrasonic sensors have a better performance at small movement detection [18].
5. Data Aggregation Overview
5.1. Real-Time Analytics
5.2. Data Fusion
- Data association is the process of determining which measurements (or observations) correspond to each specific target being tracked. In a complex environment where multiple targets are present, and the sensor data might include false alarms or noise, it becomes critical to correctly associate each observation with its respective target.
- State estimation is a technique that tries to determine the state (such as position) of a moving target. The challenge arises from the fact that the observations are often noisy, and it is not always clear which observations correspond to the target being tracked.
- Decision fusion is a technique used to draw high-level conclusions about detected events and activities based on input from various data sources. These methods typically operate on symbolic information and aim to account for uncertainties in the reasoning process [61].
5.3. Building Information Modeling
- Three-dimensional BIM (geometry): A 3D model of a building or infrastructure asset. This is the most basic level of BIM, and it provides a visual representation of the asset.
- Four-dimensional BIM (time): A 3D model with time-related information, such as the construction schedule to see how the construction process will unfold over time.
- Five-dimensional BIM (cost): A 3D model extended by adding cost information to track and identify cost-saving opportunities.
- Six-dimensional BIM (sustainability): A 3D model that includes lifecycle information, such as the maintenance and operation requirements for the asset. It helps to plan long-term use of the asset and to optimize its performance.
- Seven-dimensional BIM (management): A 3D model that includes sustainability information. This enables users to evaluate the environmental impact of the asset and make design choices that enhance sustainability.
- Eight-dimensional BIM (safety): A 3D model that includes safety information. This enables users to detect and address safety risks during both the construction and operation of the asset.
6. Discussion
- The effectiveness of lighting management is highly influenced by the type of room and its occupancy pattern. The maximum energy savings can be obtained by applying daylight harvesting and occupancy control strategies.
- Monitoring the temperature throughout the building helps to optimize HVAC systems and avoid energy wastage. This can be achieved by time scheduling and occupancy-based methods; however, one of the challenging aspects of HVAC monitoring is the determination of an optimal sensor placement since the temperature distribution profile is not uniform within one location.
- Energy management systems control and optimize the current energy consumption of buildings by monitoring the related energy-consuming resources. This is achieved by measuring both the static parameters (building materials, orientation, placement) and dynamic parameters (weather data, occupancy characteristics, energy use, water use). There are intrusive and non-intrusive energy monitoring techniques. Although the non-intrusive method is a more advantageous and effective way of gathering load data, it has a notable drawback, such as the inability to monitor all types of loads. One of the advanced methods of energy optimization is connecting HVAC systems to the same interface for a direct fine-tuning of temperature and comfort levels.
- Aggregating the data from different IoT sensors via data fusion is remarkably important for decision-making systems. However, major challenges, such as the imperfection and correlation of data, inconsistencies, and the heterogeneity of the datasets, require the development of advanced predictive analytics algorithms. The accuracy of gathered data can be optimized by combining sensor fusion methods, based on data association, state estimation, or decision fusion.
- The convergence of BIM with an IoT-enabled BACS promotes an informed, data-driven approach to building management. It automates complex operational tasks based on the analysis of IoT-generated data, enhancing system responsiveness to occupant demands while advancing sustainability objectives by optimizing resource allocation.
7. Conclusions and Outlook
Author Contributions
Funding
Conflicts of Interest
References
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Strategy | Energy Savings |
---|---|
Manual control | 23–77% |
Time scheduling | 12% |
Daylight harvesting | 10–93% |
Occupancy control | 20–93% |
Combined daylight harvesting and occupancy | 26% |
Flow Meter Type | Cost | Accuracy, % |
---|---|---|
Mechanical | low | 1% of flow rate |
Vortex | medium | 0.65% liquid |
Electromagnetic | medium | 0.15% of flow rate |
Ultrasonic | high | 1–2% (wetted), 3–10% (non-invasive) |
Turbine | low | 0.25% of flow rate |
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Lavrinovica, I.; Judvaitis, J.; Laksis, D.; Skromule, M.; Ozols, K. A Comprehensive Review of Sensor-Based Smart Building Monitoring and Data Gathering Techniques. Appl. Sci. 2024, 14, 10057. https://doi.org/10.3390/app142110057
Lavrinovica I, Judvaitis J, Laksis D, Skromule M, Ozols K. A Comprehensive Review of Sensor-Based Smart Building Monitoring and Data Gathering Techniques. Applied Sciences. 2024; 14(21):10057. https://doi.org/10.3390/app142110057
Chicago/Turabian StyleLavrinovica, Ingrida, Janis Judvaitis, Dans Laksis, Marija Skromule, and Kaspars Ozols. 2024. "A Comprehensive Review of Sensor-Based Smart Building Monitoring and Data Gathering Techniques" Applied Sciences 14, no. 21: 10057. https://doi.org/10.3390/app142110057
APA StyleLavrinovica, I., Judvaitis, J., Laksis, D., Skromule, M., & Ozols, K. (2024). A Comprehensive Review of Sensor-Based Smart Building Monitoring and Data Gathering Techniques. Applied Sciences, 14(21), 10057. https://doi.org/10.3390/app142110057