# A Nonintrusive Load Monitoring Method for Office Buildings Based on Random Forest

^{1}

^{2}

^{*}

## 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

_{i}(x) are the outputs of every tree, and y are the results of RF.

#### 2.1. Data Collection

#### 2.2. Feature Selection

_{i}is the ith principal component’s score vector, l

_{i}is the ith principal component’s loading vector, and R represents an (M N) residual matrix. S is an (M Q) matrix of M scores on Q principal components, and L is an (N Q) matrix of N loadings on Q principal components.

#### 2.3. Model Construction

#### 2.4. Implementation of the Method

## 3. Case Study

^{2}, with 23 floors above the ground, and the physical model of the building was modeled in Google Sketchup, as shown in Figure 2. The window-wall ratio of the whole building was approximately 74% and the story height was 4 m.

_{chiller}, E

_{chilled_pump}, E

_{cooling_pump}, and E

_{HVAC}are the energy consumption of the chillers, chilled water pumps, cooling water pumps, and HVAC systems, respectively; Q

_{cooling/heating}is the cooling and heating loads of the office building; COP is the coefficient of performance of the chillers; x is the part load ratio of the chillers; a, b, c, and d are fitting coefficients between the COP and part load ratio of the chillers; g is the gravitational acceleration; m is the mass flow rate; h is the pump lift; c is the heat capacity of water; and ∆t is the temperature difference between supply and return water from the chillers.

^{2}and h was 60 m. For Equation (9), c was 4.2kJ/kg K and ∆t was 5 K.

## 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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**Figure 4.**The total loads and loads of four types of subsystems in a typical week for (

**a**) summer and (

**b**) winter.

**Figure 5.**Testing results of the NIM model based on Approach I: (

**a**) the comparison results of lighting system load; (

**b**) the comparison results of plug-in system load; (

**c**) the comparison results of elevator system load; (

**d**) the comparison results of HVAC system load.

**Figure 6.**Testing results of the NIM model based on Approach I in a typical week: (

**a**) the comparison results of lighting system load; (

**b**) the comparison results of plug-in system load; (

**c**) the comparison results of elevator system load; (

**d**) the comparison results of HVAC system load.

**Figure 7.**Testing results of the NIM model based on Approach II: (

**a**) the comparison results of lighting system load; (

**b**) the comparison results of plug-in system load; (

**c**) the comparison results of elevator system load; (

**d**) the comparison results of HVAC system load.

**Figure 8.**Testing results of the NIM model based on Approach II in a typical week: (

**a**) the comparison results of lighting system load; (

**b**) the comparison results of plug-in system load; (

**c**) the comparison results of elevator system load; (

**d**) the comparison results of HVAC system load.

**Figure 9.**Testing results of the NIM model based on Approach III: (

**a**) the comparison results of lighting system load; (

**b**) the comparison results of plug-in system load; (

**c**) the comparison results of elevator system load; (

**d**) the comparison results of HVAC system load.

**Figure 10.**Testing results of the NIM model based on Approach III in a typical week: (

**a**) the comparison results of lighting system load; (

**b**) the comparison results of plug-in system load; (

**c**) the comparison results of elevator system load; (

**d**) the comparison results of HVAC system load.

Building Envelope | |||
---|---|---|---|

Item | Model Thermal Property (W/m^{2} 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 m^{2}/personHall: 20 m ^{2}/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/m^{2}Hall: 11 W/m ^{2} | 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/m^{2}Hall: 5 W/m ^{2} | 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/m^{2} | 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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## Share and Cite

**MDPI and ACS Style**

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

**AMA Style**

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 Style**

Ling, 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