IAQ Prediction in Apartments Using Machine Learning Techniques and Sensor Data
Abstract
:Featured Application
Abstract
1. Introduction
2. Experimental Design
2.1. Sensor Device for IAQ Monitoring
2.2. Apartments
2.3. IAQ Monitoring Study
3. Methods
3.1. Prediction Model Structure
3.2. Prediction Models–MLT Models and Naive Approach
3.2.1. Decision Tree (DT)
3.2.2. Random Forest Regression (RFR)
3.2.3. K-Nearest Neighbors (KNN)
3.2.4. Multilayer Perceptron (MLP)
3.2.5. Multiple Linear Regression (MLR)
3.2.6. Naive Approach
3.3. Prediction Model Validation
3.4. Performance Metrics
4. Results
4.1. Selected Results of IAQ Monitoring in Apartments
4.2. Temperature Prediction
4.3. Relative Humidity Prediction
4.4. CO2 Concentration Prediction
4.5. TVOC Content Prediction (SGP30 Sensor Response)
4.6. TVOC Content Prediction (SGPC3 Sensor Response)
5. Discussion
6. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Sensor | Measured Parameter | Detection Principle | Measurement Range | Accuracy | Resolution | Repeatability | Long-Term Drift |
---|---|---|---|---|---|---|---|
SHT 25 | T | Bandgap temperature sensor | −40 to 125 °C | Typ. ±0.2 °C | 0.04 °C | ±0.1 °C | <0.02 °C/yr |
RH | Capacity-type humidity sensor | 0 to 95% RH | ±1.8%RH | 0.04%RH | ±0.1%RH | <0.25%RH/yr | |
SCD30 | CO2 | Non-dispersive infrared (NDIR) | 0–5000 ppm | ±(30 ppm + 3% meas. Value) | - | ±10 ppm | ±50 ppm |
SGP30 | TVOCs and CO2eq | Metal oxide gas sensor (chemical resistor) | 0.3–30 ppm ethanol 0–1000 ppm ethanol | Typ. 15% of meas. value | Typ. 0.2% of meas. value | - | Typ. 1.3% of meas. value |
SGPC3 | TVOCs | Metal oxide gas sensor (chemical resistor) | 0.3–30 ppm ethanol 0–1000 ppm ethanol | Typ. 15% of meas. value | Typ. 0.2% of meas. value | - | Typ. 1.3% of meas. value |
Feature | Apartment 1 | Apartment 2 | Apartment 3 | Apartment 4 | Apartment 5 |
---|---|---|---|---|---|
Flat size | 56 m2 | 35 m2 | 64 m2 | 27 m2 | 22.75 m2 |
Type of the kitchen | Open kitchen | Open kitchen | Closed kitchen | Open kitchen | Closed kitchen |
Kitchen size | 7 m × 5.5 m | 3.5 m × 2 m | 3 m × 2 m | 4 m × 4 m | 1.5 m × 3 m |
Living room size | 3.5 m × 3 m | 2 m × 2.5 m | 2.5 m × 3.5 m | ||
Floor cover | Kitchen: panels Living room: panels and no carpet | Kitchen: tiles Living room: wood and no carpet | Kitchen: tiles Living room: panels with baby mattress | Kitchen: tiles Living room: tiles and woolen carpet | Kitchen: tiles Living room: panels and no carpet |
Furniture | Not many items in the room. The furniture is new and made of fabric and wood. | Crowded with old furniture made of wood. | Crowded with old furniture made of wood and fiberboard. | Crowded with new furniture made of fabric and wood. | Crowded with old furniture made of wood and fabric. |
Door between the kitchen and living room | None | None | Daytime: door opens while cooking. Nighttime: door mostly closed. | None | 60% open 40% close |
Kitchen window | None | None | None | Opens for 24 h | Opens for 24 h |
Living room windows | Open for 24 h | Open for 24 h | Open from 6.00 a.m. to 7.00 p.m. | Open from 7.00 a.m. to 8.00 p.m. | Open for 24 h |
Kitchen exhaust | No exhaust | Passive exhaust | Mechanical exhaust | Hood. | Hood and passive exhaust |
Cooker | Induction | Gas | Gas | Induction | Gas |
Cooking intensity | Twice a day (around 11.00 and 20.00) | Twice a day (around 8.00 and 18.00) | 2–3 times a day (around 5.00, 12.00, 18.00) | 1–2 times a day (around 9.00 and 14.00) | Twice a day (around 11.00 and 18.00) |
Dishwashing | Dishwasher | Manually | Manually | Dishwasher | Manually |
Location | Residential area | In the garden | In the green area (tress) | Residential area | By the main street |
Type of building/age | Apartment building/new | Block of flats/old | Block of flats/old | Apartment building/new | Block of flats/old |
Floor | 5th | 1st | 1st | 1st | 5th |
Occupants | 2 adults | 2 adults | 2 adults with a baby | 2 adults | 2 adults |
Additional information | One occupant fully works from home. | Occupants work from home 3 days a week. Smoker in the flat. | One occupant works from home 2 days a week. | Occupants fully work from home. | One occupant fully works from home. |
MLT/ Naive | R2 | RMSE | MAPE [%] | ||||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
T | RH | CO2 | SGP30 | SGPC3 | T [°C] | RH [%] | CO2 [ppm] | SGP30 [s.r.u.] | SGPC3 [s.r.u.] | T | RH | CO2 | SGP30 | SGPC3 | |
DT | 0.09 | 0.31 | 0.10 | 0.23 | 0.16 | 2.3 | 6.3 | 335 | 289 | 351 | 6.4 | 8.0 | 33.7 | 1.7 | 1.4 |
RFR | 0.12 | 0.40 | 0.16 | 0.32 | 0.25 | 2.1 | 5.3 | 278 | 256 | 306 | 5.7 | 6.9 | 30.2 | 1.5 | 1.2 |
KNN | 0.06 | 0.40 | 0.08 | 0.18 | 0.26 | 2.0 | 5.2 | 252 | 263 | 353 | 6.0 | 7.4 | 29.2 | 1.4 | 1.2 |
MLP1 | 0.43 | 0.58 | 0.05 | 0.41 | 0.44 | 1.6 | 4.6 | 360 | 290 | 328 | 5.3 | 7.2 | 39.7 | 1.7 | 1.5 |
MLP2 | 0.7 | 0.71 | 0.52 | 0.75 | 0.55 | 0.9 | 3.1 | 265 | 195 | 257 | 2.8 | 4.2 | 34.3 | 0.9 | 1.0 |
LR | 0.72 | 0.71 | 0.51 | 0.75 | 0.54 | 0.9 | 3.3 | 277 | 197 | 255 | 2.7 | 4.4 | 36.4 | 0.9 | 0.9 |
Naive | 0.7 | 0.72 | 0.38 | 0.55 | 0.56 | 1.2 | 4.1 | 491 | 303 | 715 | 2.7 | 6.6 | 66.1 | 1.7 | 3.7 |
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Maciejewska, M.; Azizah, A.; Szczurek, A. IAQ Prediction in Apartments Using Machine Learning Techniques and Sensor Data. Appl. Sci. 2024, 14, 4249. https://doi.org/10.3390/app14104249
Maciejewska M, Azizah A, Szczurek A. IAQ Prediction in Apartments Using Machine Learning Techniques and Sensor Data. Applied Sciences. 2024; 14(10):4249. https://doi.org/10.3390/app14104249
Chicago/Turabian StyleMaciejewska, Monika, Andi Azizah, and Andrzej Szczurek. 2024. "IAQ Prediction in Apartments Using Machine Learning Techniques and Sensor Data" Applied Sciences 14, no. 10: 4249. https://doi.org/10.3390/app14104249
APA StyleMaciejewska, M., Azizah, A., & Szczurek, A. (2024). IAQ Prediction in Apartments Using Machine Learning Techniques and Sensor Data. Applied Sciences, 14(10), 4249. https://doi.org/10.3390/app14104249