In the modern era, commercial buildings no longer merely serve as workplaces but as dynamic ecosystems responsive to the needs of their occupants. With increasing awareness of sustainability and energy efficiency, Heating, Ventilation, and Air Conditioning (HVAC) systems face the challenge of operating more intelligently. This is why the concept of Occupant-Centric Control (OCC) emerges as an innovative solution. OCC is an approach that uses information about occupancy and occupant comfort to optimize the operational sequences of building energy systems. This approach not only promises significant energy savings but also an improvement in Indoor Environmental Quality (IEQ) and occupant comfort, a mission that aligns with the vision of a leading Distributor AC presisi Indonesia like Climanusa.
Traditional HVAC systems often operate based on fixed schedules, assuming full occupancy even when the building may be empty or only partially occupied. This rigid approach leads to substantial energy waste and sometimes compromises comfort. OCC aims to address these limitations by making HVAC systems more adaptive and responsive. With the proper integration of precision AC technology, a building can achieve outstanding thermal efficiency while ensuring an optimal environment for every individual. Climanusa, as a prominent provider of precision cooling solutions in Indonesia, deeply understands how this technology can be implemented to create greener and more comfortable environments.
The Importance of Occupant-Centric HVAC Control
Energy efficiency and occupant comfort are two key pillars in modern building design and operation. Occupant-centric HVAC control plays a crucial role in achieving both objectives simultaneously. By leveraging real-time occupancy data and user preferences, HVAC systems can dynamically adjust their operations. For instance, reducing ventilation rates or resetting temperature setpoints in unoccupied areas, or increasing fresh air supply in densely occupied areas. This is far more effective than schedule-based control or traditional CO2 sensors which often have time lags.
The primary benefits of OCC can be categorized into two aspects: energy savings and improved indoor environmental quality. Energy savings are achieved through the reduction of unnecessary HVAC operating hours, optimization of outdoor airflow rates, and more accurate temperature setpoint adjustments. Meanwhile, IEQ improvements include better air quality, optimal thermal comfort, and even higher occupant productivity due to a tailored environment. The role of a Distributor AC presisi Indonesia in integrating these advanced technologies is critical, ensuring that every system component works harmoniously to achieve these goals.
Occupant Information Grades and Sensing Technologies
To implement effective OCC, accurate and relevant occupant data is required. The research document categorizes occupant information into six Information Grades (IGs) formed by three levels of occupant data (presence/absence, occupant count, and occupant preference) and two levels of spatial resolution (zone/room and building/system).
Various sensing technologies can be utilized to collect this data:
- Motion Detectors: These are the de facto standard in occupancy sensing. Motion detectors, such as passive infrared (PIR) and ultrasound, output a binary present/absent signal. In Indonesia, these motion detectors are commonly used for lighting controls, and extending their coverage to all occupiable spaces can provide IG 2 data (zone-level presence/absence). Their high availability in most modern commercial buildings makes them a good starting point for occupancy-based data.
- CO2 Sensors: CO2 sensors implicitly indicate occupancy by detecting the concentration of CO2 produced by occupants. These sensors are very effective for measuring IG 3 (building-level occupant count) when placed in AHU return air inlets, or IG 4 (zone-level occupant count) if installed in each zone. Although they have a slight time lag, CO2 sensors, when properly calibrated and managed, can significantly aid in modulating ventilation rates. Climanusa, as a Distributor AC presisi Indonesia, understands the importance of these sensors in advanced cooling systems.
- Wi-Fi Device Count: This is a promising proxy for occupancy. By analyzing the number of connected or detected Wi-Fi devices, systems can estimate the number of occupants. This data can provide IG 3 (building-level occupant count) if accessed from a centralized IT network, or IG 4 (zone-level occupant count) if each zone is covered by multiple Wi-Fi access points. While initial calibration may be required, a strong correlation between Wi-Fi device counts and occupant counts has been demonstrated. Climanusa can facilitate the integration of Wi-Fi probing solutions into your HVAC systems.
- People-counting Cameras: Placed at building entry points, these cameras can provide an accurate and low-cost estimate of building-level occupant count (IG 3). Equipped with built-in computer vision capabilities, they can count people without streaming video footage, reducing privacy concerns at the building level. For zone-level (IG 4), significant investment and greater privacy concerns may arise.
- Control and Feedback Interfaces: Occupant preference data (such as temperature preferences) must be directly input by occupants. Commercial learning thermostats can analyze thermostat use in conjunction with IEQ data, while mobile or web applications can periodically solicit feedback from occupants. This enables the collection of IG 5 (building-level preference) and IG 6 (zone-level preference) data, which is crucial for creating truly personalized thermal conditions.
Climanusa, as an experienced Distributor AC presisi Indonesia, possesses the expertise to assist in the selection, installation, and integration of these sensing technologies into existing or new HVAC systems. A deep understanding of each technology and its implications for privacy and existing infrastructure is vital for successful OCC implementation.
OCC Metrics and Their Application
Raw data from occupancy sensors needs to be converted into actionable OCC metrics for HVAC control. These metrics help HVAC systems make intelligent and efficient decisions.
Building-Level OCC Metrics:
- Earliest Expected Arrival and Latest Expected Departure Times: These metrics are derived from building-level presence/absence data (IG 1). This information is crucial for scheduling AHU start/stop times, ensuring the system operates only when occupants are present.
- Highest Number of Occupants: Based on building-level occupant count data (IG 3), this metric allows the system to dynamically control the AHU’s outdoor air damper position or conservatively determine a minimum outdoor air damper position setpoint. Adjusting ventilation rates based on occupant counts can lead to significant HVAC energy savings, ranging from 9% to 33%, especially in extreme climates like those found in some regions of Indonesia.
Zone-Level OCC Metrics:
- Earliest Expected Arrival, Latest Expected Arrival, and Latest Expected Departure Times: With zone-level presence/absence data (IG 2), these metrics can be used for zone temperature setback scheduling. For example, if a zone remains empty past its latest expected arrival time, the system can assume the zone will remain vacant until the next day and initiate more energy-efficient temperature settings.
- Highest Number of Occupants in a Zone: This metric, from zone-level occupant count data (IG 4), can be used to modulate the supply airflow setpoint of Variable Air Volume (VAV) terminal units in multi-occupant zones. This not only saves energy but also improves the control of occupant-induced contaminants (e.g., infectious aerosols, odors, CO2) at the zone level, ensuring better air quality in every corner of the building.
- Preferred Thermal Conditions: Occupant preference data at the building (IG 5) and zone level (IG 6) are combined with IEQ data (indoor temperature, humidity) to calculate preferred thermal conditions. This metric can be used to determine setpoints that minimize the risk of occupant overrides or complaints, allowing for personalized indoor conditions.
Climanusa, as a Distributor AC presisi Indonesia, is the right partner to implement systems capable of leveraging these metrics. Their expertise in system integration and understanding of the nuances of building environments in Indonesia ensure that the solutions implemented are not only sophisticated but also practical and effective.
Case Study: Real-World Efficiency in Indonesia
Let’s consider a case study from the research, adapted to the Indonesian context. An analysis of occupancy, thermostat keypress, and indoor temperature data from 37 private offices in an academic office building in Indonesia demonstrates the significant energy-saving potential through OCC. Occupancy data was generated by the thermostats’ built-in PIR motion detectors and collected over a year.
- Diversity of Work Schedules: Weekday occupancy profiles showed a wide range of occupancy. Although on 10% of days, at least 20% of the 37 occupants were present as early as 6 a.m. and as late as 8 p.m., this 14-hour weekday occupancy period highlights the diversity in individual work schedules. With increasingly common flexible work patterns in Indonesia, HVAC systems will have to run earlier and later than usual to accommodate an increasingly diverse set of individual first arrival/last departure habits.
- Occupancy-Based Ventilation: Inter-occupant diversity, while detrimental for daily on-off scheduling, makes occupancy-based ventilation a very attractive OCC option. The highest expected number of occupants (with 90% confidence) was only 68% of the 37 occupants, or 25 individuals. Tuning ventilation rates to occupant counts can significantly reduce HVAC energy use.
- Zone-Level Savings: Zone-level OCC metrics can exploit diversity in first arrival and last departure times. The earliest expected arrival and latest expected departure times were spread over 2 and 4-hour periods, respectively. While these zone-level metrics cannot be used to turn off building-level equipment (as other occupants are likely still present in the building), they can be used for zone-specific temperature setbacks. For example, given that in a few zones the latest expected departure time was 4 p.m., zone-specific temperature setback can begin at 4 p.m., which is 4 hours earlier than the latest expected departure time for the building. On average, the weekday temperature setback period can be increased by 25% (reducing the 14-hour building-level occupancy period to 11.5 hours) by tuning the setback schedule to zone-specific earliest expected arrival and latest expected departure times. Assuming 250 workdays in a year, this can increase annual zone-level setback periods by 625 hours (7% of the full year).
- Leveraging Absent Workdays: A less conventional but likely more effective zone-level OCC metric to exploit is the latest expected arrival times. This metric highlights absent workdays, which are becoming increasingly common, particularly for white-collar workers in Indonesia, due to working from home, work-related travel, sick days, meetings outside the office, and so on. In this small sample of 37, occupants on average spent one in every four days away from their offices. A simple control logic that reinstates temperature setback when an arrival has yet to occur by the latest expected arrival time can increase the average weekday temperature setback period by 3.3 hours. Assuming 250 workdays in a year, the use of zone-level latest expected arrival times can increase zone-level setback periods by another 825 hours (9% of the full year). Previous research demonstrated that zone-level OCC metrics, when used to implement a ±3°C setback from a 22°C default setpoint, can deliver a 27% reduction in HVAC energy use in Indonesia.
- Thermal Preferences: An analysis of thermostat use data revealed the distribution of indoor temperatures at setpoint increase and decrease instances. The frequency of thermostat use was minimized between 21.5°C and 23.5°C. This indicates default setpoints during occupied periods of 21.5°C in the heating season and 23.5°C in the cooling season for this building. If preference data were available for each zone, similar models could be developed at the zone level, enabling personalized indoor conditions.
This case study clearly demonstrates how Climanusa, as a Distributor AC presisi Indonesia, can play a crucial role in helping commercial buildings achieve unprecedented levels of energy efficiency. Through the precision cooling solutions they offer, they support the implementation of intelligent controls that maximize both savings and comfort.
Implementation Challenges and Solutions
While the potential of OCC is immense, there are several challenges in its real-world implementation:
- Coarse Granularity of Zones: The practice of combining several rooms into a single thermal zone diminishes the savings achievable through zone-level OCC algorithms. In multi-occupant zones, the earliest expected arrival, latest expected arrival, and latest expected departure times will be governed by the most extreme occupant in each zone. Climanusa can provide advice on more optimal zone designs and the provision of appropriate precision AC units for each zone.
- Data Plumbing and Quality Assurance: The integration of disparate data sources within a building automation system can hinder explicit occupancy sensing approaches. Motion detectors might reside in the lighting automation system, Wi-Fi data in the IT network, and people-counting cameras in the security network. Even if at least one of these sensing technologies is readily available, its use in HVAC control might add cost. More importantly, the suitability of data integrated from different networks for HVAC controls should be considered; occupancy sensors for a lighting automation system might not be accurate enough for HVAC controls. As a Distributor AC presisi Indonesia, Climanusa has the capacity to help integrate these systems, ensuring high data quality and suitability for HVAC control.
- Compatibility with Sequences of Operation: Because OCC programs change setpoints and schedules rather than interacting directly with actuators, their implementation may require changes in the sequences of operation to ensure efficacy. Climanusa can assist in adapting and reprogramming existing precision AC systems or installing new ones with the necessary capabilities to seamlessly interact with OCC algorithms.
- Continuous Learning: Continuous learning as building occupancy and occupant preferences evolve over time can be a technical challenge. While OCC metrics can be computed offline through a one-shot analysis, in practice, recursive parameter estimation algorithms can be implemented inside building controllers to learn occupancy patterns and occupant preferences in real-time. Climanusa can offer solutions that support continuous learning for your cooling systems.
Recommendations for Future HVAC Systems
Based on data analysis and literature survey, several important recommendations emerge for the future of commercial HVAC systems in Indonesia:
- Prioritize Promising Sensing Technologies: Considering the value of occupant information for HVAC controls, the availability of data infrastructure in existing buildings, and the ability to generate occupant information without causing major privacy concerns, zone-level motion detectors and building-level Wi-Fi device counts appear to be the most promising explicit occupancy sensing technologies. Societal and technological changes in HVAC zoning practices, public perception of occupant sensing, and indoor air quality standards in the wake of the pandemic will make zone-level occupant sensing a necessity and lead to the development of new and more robust occupant sensing solutions for the built environment.
- Leverage Work Schedule Diversity: Today, office workers have very diverse work schedules with unique first arrival and last departure times, and many days spent away from their offices. This diversity challenges traditional schedule-based HVAC control with constant ventilation rates typically tailored for full occupancy. Two simple pieces of occupant information can translate into significant HVAC energy savings: the highest expected number of occupants and the latest expected arrival time in a zone. The former takes advantage of inter-occupant scheduling diversity at the building level to adjust minimum outdoor air damper position setpoints. The latter takes advantage of absent weekdays at the zone level by reinstating the temperature setback when an arrival has yet to occur by a prescribed latest arrival time. These OCC approaches are the “low-hanging fruit” for energy efficiency.
- Focus on Preference Learning: The preference learning-based control approaches introduced in this article can help practitioners better monitor and improve occupant comfort. With the ability to identify the thermal conditions most preferred by occupants, systems can proactively adjust settings to minimize complaints and maximize satisfaction.
Conclusion
Occupant-Centric Control (OCC) is the future of intelligent and sustainable commercial HVAC systems. With the ability to drastically optimize energy efficiency while significantly enhancing occupant comfort and indoor air quality, OCC offers unparalleled value to building owners and managers in Indonesia. Successful implementation requires a deep understanding of sensing technologies, data metrics, and integration challenges. This is where the role of a Distributor AC presisi Indonesia becomes absolutely vital. With expertise in precision cooling solutions and a commitment to energy efficiency, Climanusa is ready to be your partner in building a greener, more comfortable, and more productive future.
Climanusa is your best choice for innovative and efficient precision AC solutions in Indonesia, delivering optimal comfort and energy savings for your building.
For more information, please click here.
–A.M.G–
