The Rise of Sensor Networks and IoT in Building Management
Digitalization is driving the adoption of room sensing technology across the building industry. However, not all room ventilation options are created equal. This article delves into the value of real-time data captured by room sensors and its impact when paired with technologies like Constant Air Volume (CAV) and Demand-Controlled Ventilation (DCV) systems.
As one of the largest sources of carbon emissions, commercial building owners are under pressure to reduce the adverse environmental impact of their properties. Buildings account for 75% of electricity use in the US and 40% of energy consumption globally, contributing to 33% of greenhouse gas emissions. Smart building technologies like room sensors and controllers can be a significant part of the solution, enabling data-driven building automation to combat the sector’s negative environmental effects.
The onset of the COVID-19 pandemic has further accelerated the demand for healthy building-related technologies. The focus on occupant comfort and well-being requires addressing indoor air quality, which is largely driven by Heating, Ventilation, and Air-Conditioning (HVAC) equipment and associated air exchange systems. Simulations for designing innovative approaches to building operation show that occupant comfort and energy efficiency are often viewed as tradeoffs. Thus, a pressing question within the smart building industry is solving the need for maintaining Energy Conservation Measures (ECMs) while equally prioritizing occupant comfort.
Transitioning from Static to Dynamic HVAC Control
Traditional approaches to HVAC control often fall into the category of Constant Air Volume (CAV), where air is continuously provided to a space or zone based on a defined schedule or triggered using presence detection sensors. As building owners and regulators alike move towards optimizing energy use as part of sustainable building operation or net-zero efforts in commercial properties, there is a need to transition from a static air flow operation model to a dynamic air flow model.
This dynamic air flow model, using the concept of Demand-Controlled Ventilation (DCV), relies on real-time data such as CO2, occupancy, or people count to enable control decisions and efficiently run the HVAC system. DCV systems aim to provide the right amount of ventilation based on the actual occupancy of a space, in contrast to the generalized occupancy patterns and normalized estimations of heat load profiles that create uncertainty in predicted energy usage and costs.
Building energy programs such as the Building Electrification mandates and energy optimization trends such as using occupancy-based building controls have increased the need for zoning beyond the floor level. Decisions based on real-time data collected from the space can impact variables such as energy consumption. Implementing zone-level control and establishing a connected layer of sensors that deliver data points of these individual spaces provide granular access to room or space monitoring and controls, which is an efficient step towards building energy and comfort monitoring and optimization.
Leveraging Sensor Data for Improved Decision-Making
In larger buildings, sensor data is captured and logged onsite or in the cloud. These datapoints, once analyzed, provide insights that improve as historical databases grow. A cloud integration or on-premises data warehouse provides the opportunity to engage machine learning algorithms and related technologies for advanced automation and other applications in the future, such as occupancy-based energy load forecasting, space utilization tracking, and resource allocation.
Historical data tracking and analysis also supports requirements for popular energy and well-being certifications, such as LEED, WELL, and Fitwel. One of the biggest smart building industry challenges at the room level is maintaining occupant comfort while balancing ECMs. Simple energy conservation techniques, such as running lighting controls using timers or occupancy sensors, using variable speed drives, and making upgrades to HVAC equipment by adding dampers, actuators, and associated control sequences, can achieve higher levels of efficiency KPIs when leveraging room sensor data points for dynamic real-time HVAC load management.
Optimizing Energy Usage with Demand-Controlled Ventilation
Demand-Controlled Ventilation (DCV) systems are an effective solution to balance comfort and energy spend. DCV systems rely on input variables that indicate occupancy or the need for ventilation, such as CO2 levels in the space. However, a key limitation of traditional CO2-based DCV is the delayed response time, as the occupant may already be uncomfortable before the ventilation change occurs.
People count data can be used to determine the time, amount, and rate of required ventilation, whereas binary data points on room conditions (e.g., temperature, CO2 levels, occupied/unoccupied) can only suggest ventilation at the room’s design setting. This people-centric approach ensures occupant comfort without compromising on judicious energy use, as the HVAC system can be fine-tuned to the exact number of occupants in the space.
Additionally, lighting control systems based on occupancy and using light sensors can be integrated with the ventilation system as a data point. In cooling seasons, about 76% of sunlight that falls on standard double-pane windows enters to become heat, and a light sensor’s data can be used as a feed into the DCV blind control and lighting systems algorithms.
Unlocking the Potential of Non-Camera-Based People Counting
Currently available solutions for creating a DCV system in commercial buildings often rely on the use of camera-based technologies. However, due to growing concerns over privacy and compliance with regulations such as the General Data Protection Regulation (GDPR), this approach is increasingly becoming problematic.
To achieve DCV, the sensing technology should rely on non-camera-based people counting technology. Traditional passive infrared (PIR) sensors, which can only detect the presence of people in binary terms, do not fully realize the potential of DCV. Thermal imaging-based people counting sensors, on the other hand, can provide granular occupancy data while addressing privacy concerns by processing the information on-device and not storing or transferring any images.
A controlled room experiment conducted at one of Schneider Electric’s offices demonstrates the benefits of simple and raw data captured from a room sensor. The data gathered via the installation of a SpaceLogic Insight-Sensor in this room shows clear trends for percentage of time occupied, time of use, and frequency of use. Working with occupant data can support occupancy-based ventilation sequences, delivering greater sustainability achievements compared to work week schedules that are still considered DCV methodologies.
Implementing Effective Sensor Networks for Smart Buildings
When implementing an effective sensor network for smart building management, there are several key considerations to keep in mind:
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Targeted Functionality: Clearly identify the specific use cases and applications for the sensor network, such as occupancy detection, people counting, lighting control, or air quality monitoring.
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Data Reliability and Availability: Ensure the sensors can provide reliable, real-time data and offer flexible data logging and integration options to meet the specific requirements of the project.
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Sensor Placement and Footprint: Consider the optimal placement of sensors in the building, as well as the physical footprint and aesthetics to minimize the visual impact on the space.
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Power Delivery: Evaluate power options for the sensors, such as battery-powered, Power over Ethernet (PoE), or USB, based on the specific installation requirements and long-term sustainability.
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Cybersecurity and Privacy: Prioritize end-to-end security measures and privacy protection, especially for camera-based or occupancy-sensing technologies, to address any concerns from building occupants.
Sensor-driven decision support systems can play a crucial role in optimizing energy utilization and occupant comfort in smart building ecosystems. By leveraging real-time data from room-level sensors, building owners and facility managers can implement dynamic, occupancy-based HVAC control strategies to achieve significant energy savings while prioritizing the well-being of the building’s occupants.
Driving Sustainability and Human-Centric Design
The integration of multi-sensor technologies in everyday building spaces has the potential to transform how we design, operate, and interact with our built environments. Demand-controlled ventilation, enabled by accurate occupancy data, is an established and effective energy conservation strategy that can lead to substantial savings in energy consumption.
Innovation in this technology space is paving the way towards a new generation of user-centric, decarbonized, resilient, and optimized workspaces. By focusing on work patterns, building operations, and adaptive technologies, these science-based design processes are driving sustainable and comfortable environments that benefit both building owners and occupants.
As the building industry continues to evolve, sensor-driven decision support systems will play an increasingly crucial role in achieving energy efficiency, occupant well-being, and environmental stewardship – ultimately shaping the future of smart, sustainable building ecosystems.
Human-building interactive dashboards can also engage occupants in sustainability initiatives by providing real-time data on indoor environmental quality, energy usage, and the building’s performance. By empowering occupants with this information, they can become more aware of their impact and contribute to a culture of conservation and mindful resource utilization.
Conclusion
The widespread adoption of sensor networks and IoT technologies in the built environment has unlocked a new era of data-driven decision making for building management. By leveraging real-time sensor data, building owners and facility managers can implement dynamic, occupancy-based strategies to optimize energy utilization while prioritizing occupant comfort and well-being.
As the industry continues to evolve, sensor-driven decision support systems will play a crucial role in achieving sustainability, resilience, and human-centric design in smart building ecosystems. Through innovative solutions and collaborative efforts, the built environment can become a catalyst for a more energy-efficient, environmentally responsible, and inclusive future.