A Nonintrusive Load Monitoring Method for Office Buildings Based on Random Forest
Abstract
:1. Introduction
- Most research has mainly concentrated on how to identify the states, types, and energy use of devices in residential buildings. There are limited studies on system-level disaggregation in office buildings, which can provide detailed information on system operation optimization.
- Existing NIM research on commercial buildings has focused on one type of subsystem, while studies on multiple subsystem load disaggregation from building-level energy data are limited. Thus, it is necessary to propose a new NIM method to determine the energy consumption of multiple subsystems (i.e., the four main subsystems).
2. The NIM Approach Based on Random Forest
2.1. Data Collection
2.2. Feature Selection
2.3. Model Construction
2.4. Implementation of the Method
3. Case Study
4. Results and Analysis
4.1. Disaggregation Results Based on Approach I
4.2. Disaggregation Results Based on Approach II
4.3. Disaggregation Results Based on Approach III
4.4. Performance Comparison of the Three Approaches
5. Conclusions
- The proposed NIM method based on RF can achieve subsystem load disaggregation accurately. The RMSEs and MREs of the NIM results are less than 46.4 kW and 12.7%, respectively.
- All four subloads can be disaggregated with high accuracy. For the lighting system, plug-in system, elevator system, and HVAC system loads, the RMSEs (MREs) range from 16.8 kW to 25.0 kW (11.0% to 12.7%), 12.7 kW to 16.8 kW (8.2% to 10.1%), 4.4 kW to 6.4 kW (7.2% to 9.3%), and 28.8 kW to 46.4 kW (7.1% to 12.1%), respectively.
- The three proposed approaches can achieve subsystem load disaggregation accurately. When weather data are obtained, Approach I achieves the most accurate NIM results with RMSEs and MREs of less than 28.8 kW and 11.0%, respectively. When weather data are inaccessible, the NIM method based on Approach II and Approach III is recommended with acceptable accuracy, with RMSEs and MREs of less than 46.4 kW and 12.7%, respectively.
- For periodic loads (loads of the elevator system, plug-in system, and lighting system), the differences in the accuracy of the three approaches are small. For the nonperiodic HVAC system loads, Approach I outperforms Approach II and Approach III.
Author Contributions
Funding
Conflicts of Interest
References
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Building Envelope | |||
---|---|---|---|
Item | Model Thermal Property (W/m2 K) | Reference | |
Interior floor | 1.5 | [42] | |
Interior wall | 0.16 | ||
Interior ceiling | 1.5 | ||
Exterior door | 1.2 | ||
Exterior floor | 0.23 | ||
Exterior window | 2.3 | ||
Exterior wall | 0.45 | ||
Internal heat gain | |||
Item | Design density | Schedule | Reference |
Occupant | Office: 8 m2/person Hall: 20 m2/person | Weekdays: 1:00–600: 0% 7:00: 10% 8:00: 20% 9:00–12:00: 95% 13:00: 50% 14:00–17:00: 95% 18:00: 30% 19:00–22:00: 10% 23:00–00:00: 5% Weekends: 1:00–6:00: 0% 7:00–18:00: 5% 19:00–00:00: 0% | [42,43,44] |
Lighting | Office: 18 W/m2 Hall: 11 W/m2 | Weekdays: 0:00–5:00: 5% 6:00–7:00: 10% 8:00: 30% 9:00–17:00: 90% 18:00: 50% 19:00–20:00: 30% 21:00–22:00: 20% 23:00: 10% Weekends: 0:00–23:00: 5% | |
Plug-in devices | Office: 13 W/m2 Hall: 5 W/m2 | Weekdays: 0:00–8:00: 2% 9:00: 40% 10:00–14:00: 90% 15:00: 80% 16:00: 70% 17:00–18:00: 50% 19:00–20:00: 30% 21:00–23:00: 2% Weekends: 0:00–23:00: 20% | |
Elevator | 30 W/m2 | Weekdays: 0:00–8:00: 32% 9:00–20:00: 100% 21:00–23:00: 32% Weekends: 0:00–23:00: 34% |
Hyperparameters | Setting | ||
---|---|---|---|
Approach I | Approach II | Approach III | |
The number of estimators | 152 | 143 | 181 |
The maximum depth of individual trees | 13 | 18 | 21 |
The number of features | auto | ||
The minimum samples for a split | 2 | ||
The minimum sample leaf | 1 |
Item | Training Results | Testing Results | ||
---|---|---|---|---|
RMSE (kW) | MRE (%) | RMSE (kW) | MRE (%) | |
Lighting | 4.4 | 3.4 | 16.8 | 11.0 |
Plug-in | 4.0 | 3.0 | 12.7 | 8.2 |
Elevator | 1.3 | 2.3 | 4.4 | 7.2 |
HVAC | 7.8 | 1.3 | 28.8 | 7.1 |
Item | Training Results | Testing Results | ||
---|---|---|---|---|
RMSE (kW) | MRE (%) | RMSE (kW) | MRE (%) | |
Lighting | 7.1 | 5.0 | 24.0 | 12.7 |
Plug-in | 4.9 | 4.0 | 16.5 | 10.1 |
Elevator | 1.8 | 3.1 | 5.5 | 8.8 |
HVAC | 13.5 | 2.3 | 46.2 | 12.1 |
Item | Training Results | Testing Results | ||
---|---|---|---|---|
RMSE (kW) | MRE (%) | RMSE (kW) | MRE (%) | |
Lighting | 5.5 | 3.6 | 25.0 | 11.9 |
Plug-in | 3.8 | 2.7 | 16.8 | 9.8 |
Elevator | 1.4 | 2.3 | 6.4 | 9.3 |
HVAC | 10.3 | 1.7 | 46.4 | 11.4 |
Item | Difference of MRE between Approach I and Approach II (%) | Difference of MRE between Approach I and Approach III (%) |
---|---|---|
Lighting | −1.7 | −0.9 |
Plug-in | −1.9 | −1.6 |
Elevator | −1.6 | −2.1 |
HVAC | −5 | −4.3 |
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Ling, Z.; Tao, Q.; Zheng, J.; Xiong, P.; Liu, M.; Xiao, Z.; Gang, W. A Nonintrusive Load Monitoring Method for Office Buildings Based on Random Forest. Buildings 2021, 11, 449. https://doi.org/10.3390/buildings11100449
Ling Z, Tao Q, Zheng J, Xiong P, Liu M, Xiao Z, Gang W. A Nonintrusive Load Monitoring Method for Office Buildings Based on Random Forest. Buildings. 2021; 11(10):449. https://doi.org/10.3390/buildings11100449
Chicago/Turabian StyleLing, Zaixun, Qian Tao, Jingwen Zheng, Ping Xiong, Manjia Liu, Ziwei Xiao, and Wenjie Gang. 2021. "A Nonintrusive Load Monitoring Method for Office Buildings Based on Random Forest" Buildings 11, no. 10: 449. https://doi.org/10.3390/buildings11100449