Occupant-centric building controls

Last updated August 20, 2024

Occupant-centric building controls or Occupant-centric controls (OCC) is a control strategy for the indoor environment, that specifically focuses on meeting the current needs of building occupants while decreasing building energy consumption. OCC can be used to control lighting and appliances, but is most commonly used to control heating, ventilation, and air conditioning (HVAC).^[1] OCC use real-time data collected on indoor environmental conditions, occupant presence and occupant preferences as inputs to energy system control strategies.^[2] By responding to real-time inputs, OCC is able to flexibly provide the proper level of energy services, such as heating and cooling, when and where it is needed by occupants.^[3] Ensuring that building energy services are provided in the right quantity is intended to improve occupant comfort while providing these services only at the right time and in the right location is intended to reduce overall energy use.

In contrast to OCC, conventional building control strategies, known as Building Energy Management Systems (BEMS), typically use predetermined temperature setpoints and setback schedules.^[1] These temperatures and temperature schedules are often determined by industry standards with no input from the building occupants. Conventional BEMS typically have static operation parameters that give minimal flexibility to meet the changing needs of building occupants throughout the day, the changing needs of new building tenants, or the diverse thermal needs of any given group of building occupants.^[2]

The American Society for Heating, Refrigeration and Air-conditioning Engineers has outlined that thermal comfort of occupants is influenced both by environmental conditions such as radiative heat, humidity, air speed and season as well as personal factors such as physiology, clothing worn and activity level.^[4] This dynamic and personalized nature of thermal comfort has traditionally made it complex it integrate into HVAC controls but an increase in sensing and computing capabilities along with a decrease in sensing and computing costs has made it possible for OCC to be an effective and scalable means of controlling building energy systems.^[1] With buildings consuming over 33% of global energy, and producing almost 40% of CO₂ emissions, OCC could play a significant role in reducing global energy consumption and CO₂ emissions.^[5]

Background

Occupant-Centric Control Inputs

OCC relies on real-time occupancy and occupant preference data as inputs to the control algorithm. This data must be continually collected by various methods and can be collected on various scales including whole-building, floor, room, and sub-room. Often, it is most useful to collect data on a scale that matches the thermal zoning of the building. A thermal zone is a section of a building that is all conditioned under the same temperature setpoint.^[6]

Data on occupant presence (occupied or unoccupied) and occupancy levels (number of occupants) can be collected with either explicit or implicit sensors.^[7] Explicit sensors directly measure occupancy and can include passive infrared sensors, ultrasonic motion detectors, and entranceway counting cameras. Implicit sensors measure a parameter that can be correlated to occupancy through some calibrated relationship. Examples of implicit occupancy sensors includes CO₂ sensors and Wi-Fi-connected device count.^[1] The selection of occupancy sensing devices depends on the size of the space being monitored, the budget for sensors, the desired accuracy, the goal of the sensor (detecting occupant presence or count), and security considerations.

Unlike occupant presence data, acquiring occupant preference data requires direct feedback from building occupants. This feedback can be solicited or unsolicited.^[7] Unsolicited occupant preference data can include the time and magnitude of a manual thermostat setpoint change. While this can be a good indicator of occupant thermal dissatisfaction, thermostat setpoint changes can be infrequent creating a barrier to integrating occupant preference into OCC. Solicited occupant preference information is often used as a means of acquiring more occupant preference information and takes the form of just-in-time surveys or Ecological Momentary Assessments (EMA). These surveys, typically deployed to computers, smart phones, or smart watches, can ask participants about their thermal sensation, thermal satisfaction or any other factor that reflects their comfort in the space.^[7]^[8] Implementing occupant preference information into OCC is still in its early stages and its practical application is still being studied in the academic environment.

Predictive Controls

OCC can be categorized as either reactive control or predictive control.^[1] Reactive control uses the real-time occupant preference and presents feedback to immediately alter the conditions of the space. While this approach is useful for controlling systems with fast response times such as lighting systems, reactive OCC is not ideal for systems with slow response times such as HVAC. For these slow response systems, predictive control allows building services, such as heating, to be provided at the right time without a lag between the time a service is needed and the time when the service is provided.

Unlike reactive controls, predictive controls use real-time occupant preference and presence data to inform and train predictive control algorithms rather than directly impact the system operation. Predictive controls have a ‘prediction horizon’ which is the amount of time ahead that an OCC will need to change a setpoint or ventilation rate to achieve a certain temperature or indoor air quality level. The needed prediction horizon for an OCC will vary depending on the response time of the building.^[9] Building attributes that contribute to the need for a longer prediction horizon when controlling HVAC systems include large open rooms, high thermal mass, and spaces with rapid changes in occupancy levels.^[1]

For commercial HVAC OCC, predictive algorithms will be informed by the six information grades (IGs) outlined by ASHRAE. These IGs are occupant presence, occupant count, and occupant preference, each considered at the building and thermal zone level.^[3] From occupant presence data, an OCC may predict the earliest occupant arrival time and latest departure time. From occupant count, an OCC may predict the maximum expected number of building occupants and when. From occupant preference data, an OCC may predict the desired temperature and humidity of the space throughout the day. With this information, an OCC could predict when it would need to change temperature setpoints and ventilation rates to achieve a desired temperature, and air quality level at a specific time. Predictive algorithms needs a sufficient amount of data as well as relatively regular occupant preference and presence patterns to develop accurate control predictions.

Occupant-Centric Control Development

The development of OCC is currently being supported by the International Energy Agency (IEA) Energy in Buildings and Community (EBC) Annex 79.^[10] Annex 79, which will run from 2018 to 2023, is an international collaborative initiative focused on developing and deploying technology, data collection methods, simulation methods, control algorithms, implementation policies, and application strategies aimed at occupant-centric building design and controls. This collaborative is focused on applying the knowledge gained from the previous Annex 66 which ran from 2013 to 2018.^[11] Annex 66 worked to understand how occupant behavior relates to building energy consumption as well as how building operation and design influence occupant thermal comfort. This was done primarily by collecting occupant behavior data and developing occupant simulation methods.

Additional groups working to develop OCC include the ASHRAE Multidisciplinary Task Group on Occupant Behavior in Buildings (MGT.OBB), and the National Science Foundation Future of Work Center for Intelligent Environments.^[3]

Occupant-Centric Control Algorithms

OCC is still in development where the creation and evaluation of various control algorithms are the main focus of study. Algorithms that have been studied for OCC include, but are not limited to, iterative data fusion methods, unsupervised machine learning, and reinforcement learning. Each of these algorithms has varying levels of computational complexity, needed inputs, and energy reduction potential.

Iterative data fusion methods are an example of reactive OCC controls and are a means of combining data from two or more sources. For this method, preference data from multiple occupants and data on indoor environmental conditions is used to balance the two optimization goals of average occupant satisfaction and energy savings. To balance these goals, each time new data is put into the system, the algorithm will determine if any control action is needed, such as changing the temperature setpoint, based on a set of control rules that determine how well the optimization goals are being met^[12]

Unsupervised machine learning can be used to cluster occupants based on their ‘thermal personalities’. These clusters can then be used to inform reactive or predictive controls by understanding the thermal preferences of the specific occupants in the space. For this method, solicited occupant preference information is fed into an unsupervised machine algorithm that will group occupants based on how similar their thermal preferences are.^[8] The number and size of the groups depends on the type of unsupervised algorithm used as well as the data being analyzed.

Reinforcement machine learning can be used as a predictive control algorithm with the goal of optimizing occupant satisfaction and energy savings. For this method, the algorithm accepts occupant presence and preference data and uses it to learn occupant preferences without the need to train the algorithm on previous data.^[13] The algorithm will evaluate each control decision it makes in order to maximize its reward which is based on its ability to optimize occupant satisfaction and energy savings. This algorithm is capable of making continual adjustments based on new information it receives.

Related Research Articles

Heating, ventilation, and air conditioning (HVAC) is the use of various technologies to control the temperature, humidity, and purity of the air in an enclosed space. Its goal is to provide thermal comfort and acceptable indoor air quality. HVAC system design is a subdiscipline of mechanical engineering, based on the principles of thermodynamics, fluid mechanics, and heat transfer. "Refrigeration" is sometimes added to the field's abbreviation as HVAC&R or HVACR, or "ventilation" is dropped, as in HACR.

<span class="mw-page-title-main">Ventilation (architecture)</span> Intentional introduction of outside air into a space

Ventilation is the intentional introduction of outdoor air into a space. Ventilation is mainly used to control indoor air quality by diluting and displacing indoor pollutants; it can also be used to control indoor temperature, humidity, and air motion to benefit thermal comfort, satisfaction with other aspects of the indoor environment, or other objectives.

Operative temperature is defined as a uniform temperature of an imaginary black enclosure in which an occupant would exchange the same amount of heat by radiation plus convection as in the actual nonuniform environment. Some references also use the terms 'equivalent temperature" or 'effective temperature' to describe combined effects of convective and radiant heat transfer. In design, operative temperature can be defined as the average of the mean radiant and ambient air temperatures, weighted by their respective heat transfer coefficients. The instrument used for assessing environmental thermal comfort in terms of operative temperature is called a eupatheoscope and was invented by A. F. Dufton in 1929. Mathematically, operative temperature can be shown as;

Building automation (BAS), also known as building management system (BMS) or building energy management system (BEMS), is the automatic centralized control of a building's HVAC, electrical, lighting, shading, access control, security systems, and other interrelated systems. Some objectives of building automation are improved occupant comfort, efficient operation of building systems, reduction in energy consumption, reduced operating and maintaining costs and increased security.

Building science is the science and technology-driven collection of knowledge in order to provide better indoor environmental quality (IEQ), energy-efficient built environments, and occupant comfort and satisfaction. Building physics, architectural science, and applied physics are terms used for the knowledge domain that overlaps with building science. In building science, the methods used in natural and hard sciences are widely applied, which may include controlled and quasi-experiments, randomized control, physical measurements, remote sensing, and simulations. On the other hand, methods from social and soft sciences, such as case study, interviews & focus group, observational method, surveys, and experience sampling, are also widely used in building science to understand occupant satisfaction, comfort, and experiences by acquiring qualitative data. One of the recent trends in building science is a combination of the two different methods. For instance, it is widely known that occupants' thermal sensation and comfort may vary depending on their sex, age, emotion, experiences, etc. even in the same indoor environment. Despite the advancement in data extraction and collection technology in building science, objective measurements alone can hardly represent occupants' state of mind such as comfort and preference. Therefore, researchers are trying to measure both physical contexts and understand human responses to figure out complex interrelationships.

Variable air volume (VAV) is a type of heating, ventilating, and/or air-conditioning (HVAC) system. Unlike constant air volume (CAV) systems, which supply a constant airflow at a variable temperature, VAV systems vary the airflow at a constant or varying temperature. The advantages of VAV systems over constant-volume systems include more precise temperature control, reduced compressor wear, lower energy consumption by system fans, less fan noise, and additional passive dehumidification.

Underfloor heating and cooling is a form of central heating and cooling that achieves indoor climate control for thermal comfort using hydronic or electrical heating elements embedded in a floor. Heating is achieved by conduction, radiation and convection. Use of underfloor heating dates back to the Neoglacial and Neolithic periods.

Thermal comfort is the condition of mind that expresses subjective satisfaction with the thermal environment. The human body can be viewed as a heat engine where food is the input energy. The human body will release excess heat into the environment, so the body can continue to operate. The heat transfer is proportional to temperature difference. In cold environments, the body loses more heat to the environment and in hot environments the body does not release enough heat. Both the hot and cold scenarios lead to discomfort. Maintaining this standard of thermal comfort for occupants of buildings or other enclosures is one of the important goals of HVAC design engineers.

<span class="mw-page-title-main">Passive ventilation</span> Ventilation without use of mechanical systems

Passive ventilation is the process of supplying air to and removing air from an indoor space without using mechanical systems. It refers to the flow of external air to an indoor space as a result of pressure differences arising from natural forces.

An energy audit is an inspection survey and an analysis of energy flows for energy conservation in a building. It may include a process or system to reduce the amount of energy input into the system without negatively affecting the output. In commercial and industrial real estate, an energy audit is the first step in identifying opportunities to reduce energy expense and carbon footprint.

<span class="mw-page-title-main">Underfloor air distribution</span>

Underfloor air distribution (UFAD) is an air distribution strategy for providing ventilation and space conditioning in buildings as part of the design of a HVAC system. UFAD systems use an underfloor supply plenum located between the structural concrete slab and a raised floor system to supply conditioned air to supply outlets, located at or near floor level within the occupied space. Air returns from the room at ceiling level or the maximum allowable height above the occupied zone.

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Clothing insulation is the thermal insulation provided by clothing.

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ANSI/ASHRAE Standard 55: Thermal Environmental Conditions for Human Occupancy is an American National Standard published by ASHRAE that establishes the ranges of indoor environmental conditions to achieve acceptable thermal comfort for occupants of buildings. It was first published in 1966, and since 2004 has been updated every three to six years. The most recent version of the standard was published in 2023.

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References

1 2 3 4 5 6 Naylor, Sophie; Gillott, Mark; Lau, Tom (November 2018). "A review of occupant-centric building control strategies to reduce building energy use". Renewable and Sustainable Energy Reviews. 96: 1–10. Bibcode:2018RSERv..96....1N. doi:10.1016/j.rser.2018.07.019. ISSN 1364-0321. S2CID 115839796.
1 2 O'Brien, William; Wagner, Andreas; Schweiker, Marcel; Mahdavi, Ardeshir; Day, Julia; Kjærgaard, Mikkel Baun; Carlucci, Salvatore; Dong, Bing; Tahmasebi, Farhang; Yan, Da; Hong, Tianzhen (2020-07-01). "Introducing IEA EBC annex 79: Key challenges and opportunities in the field of occupant-centric building design and operation". Building and Environment. 178: 106738. Bibcode:2020BuEnv.17806738O. doi:10.1016/j.buildenv.2020.106738. ISSN 0360-1323. S2CID 218952231.
1 2 3 Langevin, Jared. "Occupant-Centric Building Controls: Assessing Potential Energy Impacts at Scale" (PDF).
↑ "ASHRAE Standard 55" (PDF).
↑ "Buildings – Topics". IEA. Retrieved 2021-12-12.
↑ "Thermal Zones". energycodeace.com. Retrieved 2021-12-12.
1 2 3 Gunay, Burak, Phd, P.Eng, et al. "Using Occupant-Centric Control for Commercial HVAC Systems." ASHRAE Journal 63.5 (2021): 30,32,34-36,38-40. ProQuest. Web. 12 Dec. 2021.
1 2 Jayathissa, Prageeth; Quintana, Matias; Sood, Tapeesh; Nazarian, Negin; Miller, Clayton (2019-11-01). "Is your clock-face cozie? A smartwatch methodology for the in-situ collection of occupant comfort data". Journal of Physics: Conference Series. 1343 (1): 012145. Bibcode:2019JPhCS1343a2145J. doi: 10.1088/1742-6596/1343/1/012145 . ISSN 1742-6588.
↑ Stopps, Helen; Huchuk, Brent; Touchie, Marianne F.; O'Brien, William (2021-01-01). "Is anyone home? A critical review of occupant-centric smart HVAC controls implementations in residential buildings". Building and Environment. 187: 107369. Bibcode:2021BuEnv.18707369S. doi:10.1016/j.buildenv.2020.107369. ISSN 0360-1323. S2CID 228892420.
↑ "IEA EBC || Annex 79 || Occupant Behaviour-Centric Building Design and Operation || IEA EBC || Annex 79". annex79.iea-ebc.org. Retrieved 2021-12-12.
↑ "Introduction | IEA-EBC Annex 66". www.annex66.org. Retrieved 2021-12-12.
↑ S. Purdon, B. Kusy, R. Jurdak and G. Challen, "Model-free HVAC control using occupant feedback," 38th Annual IEEE Conference on Local Computer Networks - Workshops, 2013, pp. 84-92, doi: 10.1109/LCNW.2013.6758502.
↑ Valladares, William; Galindo, Marco; Gutiérrez, Jorge; Wu, Wu-Chieh; Liao, Kuo-Kai; Liao, Jen-Chung; Lu, Kuang-Chin; Wang, Chi-Chuan (2019-05-15). "Energy optimization associated with thermal comfort and indoor air control via a deep reinforcement learning algorithm". Building and Environment. 155: 105–117. Bibcode:2019BuEnv.155..105V. doi:10.1016/j.buildenv.2019.03.038. ISSN 0360-1323. S2CID 117156449.

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[:0-1] 1 2 3 4 5 6 Naylor, Sophie; Gillott, Mark; Lau, Tom (November 2018). "A review of occupant-centric building control strategies to reduce building energy use". Renewable and Sustainable Energy Reviews. 96: 1–10. Bibcode:2018RSERv..96....1N. doi:10.1016/j.rser.2018.07.019. ISSN 1364-0321. S2CID 115839796.

[:1-2] 1 2 O'Brien, William; Wagner, Andreas; Schweiker, Marcel; Mahdavi, Ardeshir; Day, Julia; Kjærgaard, Mikkel Baun; Carlucci, Salvatore; Dong, Bing; Tahmasebi, Farhang; Yan, Da; Hong, Tianzhen (2020-07-01). "Introducing IEA EBC annex 79: Key challenges and opportunities in the field of occupant-centric building design and operation". Building and Environment. 178: 106738. Bibcode:2020BuEnv.17806738O. doi:10.1016/j.buildenv.2020.106738. ISSN 0360-1323. S2CID 218952231.

[:3-3] 1 2 3 Langevin, Jared. "Occupant-Centric Building Controls: Assessing Potential Energy Impacts at Scale" (PDF).

[4] "ASHRAE Standard 55" (PDF).

[5] "Buildings – Topics". IEA. Retrieved 2021-12-12.

[6] "Thermal Zones". energycodeace.com. Retrieved 2021-12-12.

[:2-7] 1 2 3 Gunay, Burak, Phd, P.Eng, et al. "Using Occupant-Centric Control for Commercial HVAC Systems." ASHRAE Journal 63.5 (2021): 30,32,34-36,38-40. ProQuest. Web. 12 Dec. 2021.

[:4-8] 1 2 Jayathissa, Prageeth; Quintana, Matias; Sood, Tapeesh; Nazarian, Negin; Miller, Clayton (2019-11-01). "Is your clock-face cozie? A smartwatch methodology for the in-situ collection of occupant comfort data". Journal of Physics: Conference Series. 1343 (1): 012145. Bibcode:2019JPhCS1343a2145J. doi: 10.1088/1742-6596/1343/1/012145 . ISSN 1742-6588.

[9] Stopps, Helen; Huchuk, Brent; Touchie, Marianne F.; O'Brien, William (2021-01-01). "Is anyone home? A critical review of occupant-centric smart HVAC controls implementations in residential buildings". Building and Environment. 187: 107369. Bibcode:2021BuEnv.18707369S. doi:10.1016/j.buildenv.2020.107369. ISSN 0360-1323. S2CID 228892420.

[10] "IEA EBC || Annex 79 || Occupant Behaviour-Centric Building Design and Operation || IEA EBC || Annex 79". annex79.iea-ebc.org. Retrieved 2021-12-12.

[11] "Introduction | IEA-EBC Annex 66". www.annex66.org. Retrieved 2021-12-12.

[12] S. Purdon, B. Kusy, R. Jurdak and G. Challen, "Model-free HVAC control using occupant feedback," 38th Annual IEEE Conference on Local Computer Networks - Workshops, 2013, pp. 84-92, doi: 10.1109/LCNW.2013.6758502.

[13] Valladares, William; Galindo, Marco; Gutiérrez, Jorge; Wu, Wu-Chieh; Liao, Kuo-Kai; Liao, Jen-Chung; Lu, Kuang-Chin; Wang, Chi-Chuan (2019-05-15). "Energy optimization associated with thermal comfort and indoor air control via a deep reinforcement learning algorithm". Building and Environment. 155: 105–117. Bibcode:2019BuEnv.155..105V. doi:10.1016/j.buildenv.2019.03.038. ISSN 0360-1323. S2CID 117156449.

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