Predicting Diverse Behaviors of Occupants When Turning Air Conditioners on/off in Residential Buildings: An Extreme Gradient Boosting Approach
Abstract
:1. Introduction
1.1. Background
1.2. Research Gap
1.3. Objectives
2. Methodology
2.1. Database and Surveyed Community Description
2.2. Meteorological Conditions
2.3. Machine Learning Methods
2.3.1. Clustering Analysis
2.3.2. XGBoost Model
3. Inter-Occupant Diversity of AC Use Behavior
3.1. Detection of Occupancy and AC Operation State
3.2. Daily AC Usage Rate
3.3. Clustering of AC Use Schedule
3.3.1. Hourly AC Operation Probability
3.3.2. Clustering of Hourly AC Operating Probabilities
3.3.3. Thermal Sensitivity to AC Use Behavior for Each Household
3.3.4. Household Clustering Based on Thermal Preference
4. AC on/off State Modeling
4.1. XGBoost Model Establishment
4.2. Hyperparameter Optimization
4.3. Modeling Performance Evaluation
5. Results and Discussion
5.1. Results
5.2. Applications and Limitations
6. Conclusions
- Great diversity in the inter-occupant behavioral preferences related to AC usage was found in the target community.
- Three and four types of households were identified for the occupants’ behaviors related to their cooling schedule and thermal sensitivity patterns, respectively.
- The proposed model considering diverse OBs, showed satisfactory prediction performance, with an AUC score of 0.845, indicating a high chance of accurate distinguishment of AC operation states.
- Instead of the outdoor temperature, the behaviors of the occupants were found to have a crucial impact on a household’s AC operation. Feature importance scores of occupants’ schedule preference and thermal preference in AC state prediction were found to be 0.384 and 0.263, respectively.
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
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Author | Investigation Target | Method | Objective | Year |
---|---|---|---|---|
Ren et al. [12] | 34 families in China | Action-based quantitative stochastic model | Air-conditioning usage conditional probability | 2014 |
Tanimoto and Hagishima [13] | 5 families and 3 single dwellings in Japan | Markov model | AC operation state transition probability | 2010 |
Yao [14] | 1 dwelling | Statistical analysis | Occupants’ stochastic behavior in AC usage | 2018 |
Diao et al. [15] | 5 typical house units in the USA | Clustering analysis Neural network model | Distinctive behavior patterns in AC usage | 2017 |
Xia et al. [16] | 102 bedrooms in China | Statistical analysis Clustering analysis | Representative patterns of occupancy and AC on/off states | 2018 |
Mun et al. [17] | 4 living rooms in South Korea | Machine learning (LR, SVM, RF models) | AC on/off states prediction | 2017 |
Yan and Liu [20] | 1325 air conditioners in China | XGBoost model | Prediction of AC energy use in residential buildings | 2020 |
Zaki et al. [22] | 38 dwellings in Malaysia | Statistical analysis | Occupants’ stochastic behavior in AC usage | 2017 |
Fukami et al. [23] | 20 dwellings in Japan | Statistical analysis | Stochastic nature of occupants’ behavior toward AC usage | 2022 |
Measurement items | Total electricity and breakdown for 18–26 branches in 586 dwellings |
Minimum measurement unit | 0.017 W |
Measurement period | 1 January 2013 to 31 December 2014 |
Measurement interval | 1 min |
Location | Settu City, Osaka, Japan |
Number of stories | 20 |
Completion date | January 2011 |
Structure | Reinforced concrete structure |
Building envelopes | External walls: internal insulation with air layer, U-value 0.441 W/m2K1 Windows: low-E double-glazing |
Number of dwellings | Total 586 dwellings 38 dwellings: 2 bedrooms + LDK * (55.1 m2) 391 dwellings: 3 bedrooms + LDK * (71.2 m2) 157 dwellings: 4 bedrooms + LDK * (83.6 m2) |
Variables | Remarks | |
---|---|---|
Input | Hour | Categorical (0, 1, 2 …23) |
Outdoor air temperature | Continuous | |
Thermal sensitivity type | Categorical (TPA, TPB, TPC, TPD) | |
Schedule preference type | Categorical (SPA, SPB, SPC) | |
Weighted mean temperature (10 days) | Continuous | |
Output | AC on/off state | 1: ON; 0: OFF |
Parameters | Range | Description | Settings |
---|---|---|---|
training group | Data for parameter learning | 70% | |
testing group | Data for performance testing | 30% | |
n_esitimators | [50, 150, 300, 500] | Number of gradient-boosted trees | 150 |
leaning rate | [0.01, 0.05, 0.1, 0.3] | Feature weights to prevent overfitting | 0.1 |
max_depth | [4, 6, 8, 10] | Maximum tree depth for base learners | 6 |
min_child_weight | [5, 6, 7, 8] | Minimum sum of instance weight | 5 |
gamma | [0.2, 0.4, 0.6, 0.8] | Minimum loss reduction required for a further partition | 0.6 |
Accuracy | Recall | Precision | F1 Score | |
---|---|---|---|---|
Training group | 0.83 | 0.76 | 0.82 | 0.79 |
Testing group | 0.82 | 0.73 | 0.85 | 0.80 |
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Lyu, J.; Hagishima, A. Predicting Diverse Behaviors of Occupants When Turning Air Conditioners on/off in Residential Buildings: An Extreme Gradient Boosting Approach. Buildings 2023, 13, 521. https://doi.org/10.3390/buildings13020521
Lyu J, Hagishima A. Predicting Diverse Behaviors of Occupants When Turning Air Conditioners on/off in Residential Buildings: An Extreme Gradient Boosting Approach. Buildings. 2023; 13(2):521. https://doi.org/10.3390/buildings13020521
Chicago/Turabian StyleLyu, Jiajun, and Aya Hagishima. 2023. "Predicting Diverse Behaviors of Occupants When Turning Air Conditioners on/off in Residential Buildings: An Extreme Gradient Boosting Approach" Buildings 13, no. 2: 521. https://doi.org/10.3390/buildings13020521