Artificial Intelligence (AI)-Based Occupant-Centric Heating Ventilation and Air Conditioning (HVAC) Control System for Multi-Zone Commercial Buildings
Abstract
:1. Introduction
2. Related Studies
2.1. Traditional and Advanced Control Strategies
2.1.1. Soft Computing Strategies
2.1.2. Hard Computing Strategies
2.1.3. Hybrid Strategies
2.1.4. Adaptive-Predictive Control Strategies
2.2. Occupancy Related Studies
2.3. IDA Indoor Climate and Energy (ICE) Software Background
3. Aim of the Research
4. Research Methodology
5. Design and Development of the AI-Based Occupant-Centric HVAC Control System
5.1. Building Properties, Occupancy, and Environmental Information
5.2. Occupancy Prediction with ANNs
5.2.1. ANN Parameters
- Temperature: This is one of the most critical factors affecting the number of people; there are fewer visitors to the shopping mall in winter than in summer days;
- Humidity: This affects the temperature feel; when the humidity in the air is high, warm moisture stays on people’s skin longer and makes them feel hotter;
- Weather condition: This also affects the occupant number significantly; on rainy or snowy days, shopping malls attract fewer visitors;
- Time indicators: Days are also significant for shopping mall occupancy; on non-working days, the number of visitors is higher than on working days. In our study, the days are not separated into working and non-working days, as in some studies, but each day of the week is included in the calculation separately; furthermore, month and year information are considered as separate parameters since they are essential variables in the long-term use of the shopping mall;
- Special days: Public (state) and religious holidays significantly affect occupancy; the number of visitors increases on national holidays and decreases considerably on religious holidays; furthermore, the first day of religious holidays is separately considered because there are far fewer visitors on these days than others;
- Time of day: This is the most critical factor for sudden changes in the number of people visiting; for example, the occupancy number increases rapidly at the start of the lunch break and decreases rapidly when it finishes.
5.2.2. ANN Models
5.2.3. HVAC Control Scenarios for Energy Simulation
- S1: The S1 scenario represents the full-powered HVAC at all times.
- S2: The S2 scenario represents the most common traditional HVAC control mechanism based on temperature and occupancy sensors, where the HVAC control system is automatically (de)activated according to the temperature setpoints and temperature measurements from the sensors. In this scenario, occupancy is measured by CO2 sensors, which record the level of CO2 in the air. If the number of people in a space exceeds the amount of CO2 allowed, the sensor triggers the HVAC mechanism to turn on. This type of sensor is more accurate than a standard motion sensor for the measurement of occupancy.
- S3: The S3 scenario represents the proposed AI-based HVAC control system, which uses predicted occupancy numbers as produced by the ANN model. In this scenario, the HVAC system responds automatically to changes in the occupancy with no lag time, contrary to sensor-based systems. The control algorithm provides an HVAC setpoint schedule to control the system according to real weather conditions (as supplied by weather prediction services) and predicted occupant numbers. The existing sensors can still be used to monitor the real-time indoor temperature, humidity, and amount of CO2. If the actual thermal comfort parameters exceed the desired values, the control system adjusts itself according to the sensors until thermal comfort is provided. In the energy simulations performed by IDA-ICE, the effect of the real-time sensors is not used to examine the no-sensor control mechanism. For this reason, in the illustrations and graphs for S3 and S4, dashed lines are used to show this potential.
- S4: The S4 scenario represents the HVAC control system in the S3 scenario with a pre-cooling ability along with a quick response. The control algorithm provides pre-cooling time to control the system according to predicted weather conditions and occupant numbers. All other features are the same as for S3.
Algorithm 1 HVAC Schedule Algorithm of S3 for Cooling | |
1 | train ANN model |
2 | make day-ahead prediction for occupancy |
3 | take day-ahead local weather forecast information |
4 | if occupancyt < occupancyt+1 |
5 | setpointmax ® Ttarget |
6 | set HVAC setpoint to Ttarget |
7 | end |
8 | else |
9 | if weather forecast temp.t > setpointmax |
10 | setpointmax ® Ttarget |
11 | set HVAC setpoint to Ttarget |
12 | else |
13 | deactivate the cooling • deactivation |
14 | end if |
15 | end if |
Algorithm 2 HVAC Schedule Algorithm of S4 for Cooling | |
1 | train ANN model |
2 | make day-ahead prediction for occupancy |
3 | take day-ahead local weather forecast information |
4 | if occupancyt < occupancyt+1 |
5 | setpointmax ® Ttarget |
6 | set HVAC setpoint to Ttarget |
7 | end |
8 | else |
9 | if weather forecast temp.t > setpointmax |
10 | setpointmax ® Ttarget |
11 | set HVAC setpoint to Ttarget |
12 | else |
13 | if occupancyt+2 - occupanct+1 > 250 |
14 | deactivate the cooling for first t/2 • deactivation |
15 | setpointmax ® Ttarget |
16 | set HVAC setpoint to Ttarget for last t/2 • start pre-cooling |
17 | else |
18 | deactivate the cooling • deactivation |
19 | end if |
20 | end if |
21 | end if |
6. Demonstration and Evaluation of the AI-Based Occupant-Centric HVAC Control System
6.1. ANN Results
6.2. Energy Analysis Results
7. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Design Science Research (DSR) Features | |
---|---|
AI-based occupant-centric HVAC control system in commercial buildings | Design science research |
Remove inefficiencies in energy management via HVAC systems Enhance the prediction ability with AI for the determination of occupant behavior for effective energy management in commercial buildings | Design an artifact: Development of AI algorithm for occupant-centric HVAC control system Research instruments-tools: Iterative design and development process for knowledge capture and development |
Development of an innovative artifact to achieve and enhance the social context. | Answering the knowledge questions: How does the artifact adopt smart heritage project principles? |
Construction | Material Layers (from Outside to Inside) |
---|---|
External wall | Press brick and supporters—0.088 m|Gypsum board—0.02 m| Light steel wall and XPS Insulation—0.15 m|Gypsum board—0.02 m| Plywood wall panel and supporters—0.07 m |
Ground floor | Reinforced concrete—1.5 m|Concrete—0.05 m| XPS Thermal Insulation—0.06 m Concrete—0.03 m | Screed—0.005 m|Floor Covering—0.008 m |
Roof | Standing seam roof sheet metal|OSB sheet—0.015 m| Corrugated steel sheet|Steel roof supporters and XPS insulation—0.12 m |
Window | Low-e glass double—4 mm + 12 mm argon + 4 mm |
Category | Variables | Unit/Index |
---|---|---|
Environmental | Temperature | °F |
Humidity | % | |
Weather Conditions | 1: Fair|2: Partly cloudy|3: Mostly cloudy|4: Light rain 5: Rain|6: Heavy rain|7: Fog|8: Snow|9: Thunder | |
Social and Time Indicators | Weekday Month | 1–7 (1: Monday ··· 7: Sunday) 1–12 (1: January ··· 12: December) |
Day | 1–31 | |
Year | 2017–2018–2019 | |
Time | 10–21 (10: 10:00 a.m. ··· 21: 09:00 p.m.) | |
Day Type | 0: Normal day|1: Public holiday 2: First day of religious holidays 3: Other days of religious holidays |
Day of Week | Month | Day | Year | Time | Day Type | Temp. °F | Hum. % | Weather Conditions | Occupancy (Number of People) |
---|---|---|---|---|---|---|---|---|---|
Sunday | 8 | 18 | 2019 | 1000–1100 | Normal | 70 | 83 | Partly cloudy | 661 |
Sunday | 8 | 18 | 2019 | 1100–1200 | Normal | 68 | 88 | Light rain | 1346 |
Sunday | 8 | 18 | 2019 | 1200–1300 | Normal | 73 | 78 | Partly cloudy | 1448 |
Sunday | 8 | 18 | 2019 | 1300–1400 | Normal | 72 | 78 | Mostly cloudy | 2547 |
Sunday | 8 | 18 | 2019 | 1400–1500 | Normal | 77 | 50 | Partly cloudy | 2921 |
Sunday | 8 | 18 | 2019 | 1500–1600 | Normal | 79 | 47 | Partly cloudy | 3353 |
Sunday | 8 | 18 | 2019 | 1600–1700 | Normal | 79 | 47 | Partly cloudy | 3181 |
Sunday | 8 | 18 | 2019 | 1700–1800 | Normal | 77 | 47 | Partly cloudy | 2455 |
Sunday | 8 | 18 | 2019 | 1800–1900 | Normal | 77 | 50 | Partly cloudy | 2339 |
Sunday | 8 | 18 | 2019 | 1900–2000 | Normal | 75 | 50 | Partly cloudy | 2126 |
Sunday | 8 | 18 | 2019 | 2000–2100 | Normal | 72 | 60 | Partly cloudy | 1644 |
Sunday | 8 | 18 | 2019 | 2100–2200 | Normal | 70 | 68 | Fair | 777 |
Monday | 8 | 19 | 2019 | 1000–1100 | Normal | 77 | 54 | Mostly cloudy | 463 |
Monday | 8 | 19 | 2019 | 1100–1200 | Normal | 75 | 57 | Mostly cloudy | 906 |
Monday | 8 | 19 | 2019 | 1200–1300 | Normal | 77 | 54 | Mostly cloudy | 1418 |
Monday | 8 | 19 | 2019 | 1300–1400 | Normal | 81 | 48 | Partly cloudy | 1690 |
Monday | 8 | 19 | 2019 | 1400–1500 | Normal | 81 | 45 | Partly cloudy | 1643 |
Monday | 8 | 19 | 2019 | 1500–1600 | Normal | 79 | 51 | Partly cloudy | 1379 |
Monday | 8 | 19 | 2019 | 1600–1700 | Normal | 77 | 50 | Partly cloudy | 1547 |
Monday | 8 | 19 | 2019 | 1700–1800 | Normal | 79 | 51 | Partly cloudy | 1494 |
Monday | 8 | 19 | 2019 | 1800–1900 | Normal | 77 | 54 | Partly cloudy | 1907 |
Monday | 8 | 19 | 2019 | 1900–2000 | Normal | 75 | 61 | Partly cloudy | 1806 |
Monday | 8 | 19 | 2019 | 2000–2100 | Normal | 72 | 73 | Fair | 1496 |
Monday | 8 | 19 | 2019 | 2100–2200 | Normal | 70 | 78 | Fair | 727 |
⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ |
Friday | 8 | 30 | 2019 | 1000–1100 | Pub. Hol. | 77 | 69 | Partly cloudy | 1269 |
Friday | 8 | 30 | 2019 | 1100–1200 | Pub. Hol. | 81 | 54 | Partly cloudy | 1406 |
Friday | 8 | 30 | 2019 | 1200–1300 | Pub. Hol. | 81 | 48 | Partly cloudy | 1738 |
Friday | 8 | 30 | 2019 | 1300–1400 | Pub. Hol. | 82 | 48 | Partly cloudy | 2562 |
Friday | 8 | 30 | 2019 | 1400–1500 | Pub. Hol. | 82 | 48 | Partly cloudy | 2601 |
Friday | 8 | 30 | 2019 | 1500–1600 | Pub. Hol. | 81 | 54 | Partly cloudy | 2990 |
Friday | 8 | 30 | 2019 | 1600–1700 | Pub. Hol. | 81 | 51 | Partly cloudy | 2518 |
Friday | 8 | 30 | 2019 | 1700–1800 | Pub. Hol. | 79 | 54 | Partly cloudy | 2428 |
Friday | 8 | 30 | 2019 | 1800–1900 | Pub. Hol. | 77 | 54 | Partly cloudy | 2701 |
Friday | 8 | 30 | 2019 | 1900–2000 | Pub. Hol. | 75 | 65 | Partly cloudy | 2262 |
Friday | 8 | 30 | 2019 | 2000–2100 | Pub. Hol. | 73 | 65 | Fair | 1805 |
Friday | 8 | 30 | 2019 | 2100–2200 | Pub. Hol. | 72 | 69 | Fair | 818 |
Time | Sunday, 18 August 2019 | Monday, 19 August 2019 | Thursday, 29 August 2019 | Friday, 30 August 2019 | ||||
---|---|---|---|---|---|---|---|---|
Real | Pred. | Real | Pred. | Real | Pred. | Real | Pred. | |
10:00 a.m. | 661 | 963 | 463 | 881 | 642 | 697 | 1269 | 769 |
11:00 a.m. | 1346 | 1480 | 906 | 1322 | 1240 | 1345 | 1406 | 1602 |
12:00 p.m. | 1448 | 1594 | 1418 | 2139 | 2194 | 2290 | 1738 | 1980 |
01:00 p.m. | 2547 | 2412 | 1690 | 1751 | 1981 | 1657 | 2562 | 2445 |
02:00 p.m. | 2921 | 3373 | 1643 | 1680 | 1489 | 1491 | 2601 | 3452 |
03:00 p.m. | 3353 | 3384 | 1379 | 1648 | 1732 | 1557 | 2990 | 2870 |
04:00 p.m. | 3181 | 3156 | 1547 | 1749 | 1722 | 1817 | 2518 | 2598 |
05:00 p.m. | 2455 | 2833 | 1494 | 1787 | 1622 | 1565 | 2428 | 2498 |
06:00 p.m. | 2339 | 2351 | 1907 | 2134 | 2034 | 1793 | 2701 | 2411 |
07:00 p.m. | 2126 | 1892 | 1806 | 1833 | 2362 | 1821 | 2262 | 1930 |
08:00 p.m. | 1644 | 1491 | 1496 | 1519 | 2102 | 1518 | 1805 | 1305 |
09:00 p.m. | 777 | 704 | 727 | 635 | 887 | 903 | 818 | 553 |
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Yayla, A.; Świerczewska, K.S.; Kaya, M.; Karaca, B.; Arayici, Y.; Ayözen, Y.E.; Tokdemir, O.B. Artificial Intelligence (AI)-Based Occupant-Centric Heating Ventilation and Air Conditioning (HVAC) Control System for Multi-Zone Commercial Buildings. Sustainability 2022, 14, 16107. https://doi.org/10.3390/su142316107
Yayla A, Świerczewska KS, Kaya M, Karaca B, Arayici Y, Ayözen YE, Tokdemir OB. Artificial Intelligence (AI)-Based Occupant-Centric Heating Ventilation and Air Conditioning (HVAC) Control System for Multi-Zone Commercial Buildings. Sustainability. 2022; 14(23):16107. https://doi.org/10.3390/su142316107
Chicago/Turabian StyleYayla, Alperen, Kübra Sultan Świerczewska, Mahmut Kaya, Bahadır Karaca, Yusuf Arayici, Yunus Emre Ayözen, and Onur Behzat Tokdemir. 2022. "Artificial Intelligence (AI)-Based Occupant-Centric Heating Ventilation and Air Conditioning (HVAC) Control System for Multi-Zone Commercial Buildings" Sustainability 14, no. 23: 16107. https://doi.org/10.3390/su142316107