Research Article
The Personalized Thermal Comfort Prediction Using an MH-LSTM Neural Network Method
Table 2
Information of measurement instruments.
| No. | Instrument | Parameter | Measuring range | Accuracy | Interval | Variables |
| 1 | Thermal comfort level recorder | — | −3 to 3 | — | 5 min | Target value | 2 | Temperature recorder (DHT22) | Ta | −40 to 80 | ±0.5 | 2 s | Indoor environment data | 3 | Humidity recorder (DHT22) | RH (%) | 0–100 | ±2 | 2 s | Indoor environment data | 4 | Temperature recorder (DHT22) | Ta | −40 to 80 | ±0.5 | 2 s | Indoor environment data | 5 | Humidity recorder (DHT22) | RH (%) | 0–100 | ±2 | 2 s | Indoor environment data | 6 | Skin thermometer | °C | −40 to 80 | ±0.5 | 1 s | Biometric data | 7 | Skin thermometer | °C | −40 to 80 | ±0.5 | 1 s | Biometric data | 8 | Skin thermometer | °C | −40 to 80 | ±0.5 | 1 s | Biometric data | 9 | Skin thermometer | °C | −40 to 80 | ±0.5 | 1 s | Biometric data |
|
|