# Deep Adaptive Ensemble Filter for Non-Intrusive Residential Load Monitoring

^{*}

## Abstract

**:**

## 1. Introduction

#### 1.1. Literature Review

#### 1.2. Our Contribution

- In terms of methodology:
- -
- An adaptive disaggregation framework powered by the signal processing technique and LSTM is developed that is applicable to any load disaggregation problem;
- -
- The proposed AEFLSTM is a holistic algorithm that can easily be generalized to have more signal-processing techniques.

- From an application point of view, the proposed framework AEFLSTM is used to disaggregate a heat pump and a refrigerator from the total power of a residential building in British Columbia. The accuracy of load disaggregation is significantly improved by using the proposed AEFLSTM framework based on actual data from the use case.

## 2. Use Case

## 3. Methodology

#### 3.1. Data Cleaning

#### 3.2. Adaptive Ensemble Filtering

#### 3.2.1. Low-Pass Filter

#### 3.2.2. Discrete Wavelet Transform

#### 3.2.3. Seasonal-Trend Decomposition Method

#### 3.3. Supervised Deep-Based Load Disaggregation

## 4. Experimental Results

#### 4.1. Case 1: LPF

#### 4.2. Case 2: DWT

#### 4.3. Case 3: SD

## 5. Conclusions

## Author Contributions

## Funding

## Institutional Review Board Statement

## Informed Consent Statement

## Data Availability Statement

## Conflicts of Interest

## Abbreviations

CNN | Convolutional neural network |

DR | Demand response |

DTR | Decision tree regression |

DWT | Discrete wavelet transform |

FFT | Fast Fourier transform |

HEMS | Home energy management system |

HMM | Hidden Markov model |

ILM | Intrusive load monitoring |

KNN | k-nearest neighbors |

LR | Linear regression |

LSTM | Long short-term memory |

LPF | Low pass filter |

MAE | Mean absolute error |

NILM | Non-intrusive load monitoring |

RMSE | Root mean square error |

RNN | Recurrent neural network |

SD | Seasonal decomposition |

SVM | Support vector method |

## References

- Çimen, H.; Çetinkaya, N.; Vasquez, J.C.; Guerrero, J.M. A microgrid energy management system based on non-intrusive load monitoring via multitask learning. IEEE Trans. Smart Grid
**2020**, 12, 977–987. [Google Scholar] [CrossRef] - Azizi, E.; Shotorbani, A.M.; Hamidi-Beheshti, M.T.; Mohammadi-Ivatloo, B.; Bolouki, S. Residential household non-intrusive load monitoring via smart event-based optimization. IEEE Trans. Consum. Electron.
**2020**, 66, 233–241. [Google Scholar] [CrossRef] - Lemes, D.A.M.; Cabral, T.W.; Fraidenraich, G.; Meloni, L.G.P.; De Lima, E.R.; Neto, F.B. Load disaggregation based on time window for HEMS application. IEEE Access
**2021**, 9, 70746–70757. [Google Scholar] [CrossRef] - Coffman, A.R.; Guo, Z.; Barooah, P. Characterizing capacity of flexible loads for providing grid support. IEEE Trans. Power Syst.
**2020**, 36, 2428–2437. [Google Scholar] [CrossRef] - Erdem, H.; Uner, A. A multi-channel remote controller for home and office appliances. IEEE Trans. Consum. Electron.
**2009**, 55, 2184–2189. [Google Scholar] [CrossRef] - Zhai, S.; Zhou, H.; Wang, Z.; He, G. Analysis of dynamic appliance flexibility considering user behavior via non-intrusive load monitoring and deep user modeling. CSEE J. Power Energy Syst.
**2020**, 6, 41–51. [Google Scholar] - Munoz, O.; Ruelas, A.; Rosales, P.; Acuña, A.; Suastegui, A.; Lara, F. Design and Development of an IoT Smart Meter with Load Control for Home Energy Management Systems. Sensors
**2022**, 22, 7536. [Google Scholar] [CrossRef] - Devlin, M.A.; Hayes, B.P. Non-intrusive load monitoring and classification of activities of daily living using residential smart meter data. IEEE Trans. Consum. Electron.
**2019**, 65, 339–348. [Google Scholar] [CrossRef] - Hart, G.W. Nonintrusive appliance load monitoring. Proc. IEEE
**1992**, 80, 1870–1891. [Google Scholar] [CrossRef] - Massidda, L.; Marrocu, M. A bayesian approach to unsupervised, non-intrusive load disaggregation. Sensors
**2022**, 22, 4481. [Google Scholar] [CrossRef] - Pereira, L.; Nunes, N. Performance evaluation in non-intrusive load monitoring: Datasets, metrics, and tools—A review. Wiley Interdiscip. Rev. Data Min. Knowl. Discov.
**2018**, 8, e1265. [Google Scholar] [CrossRef] [Green Version] - Tabatabaei, S.M.; Dick, S.; Xu, W. Toward non-intrusive load monitoring via multi-label classification. IEEE Trans. Smart Grid
**2016**, 8, 26–40. [Google Scholar] [CrossRef] - Liao, J.; Elafoudi, G.; Stankovic, L.; Stankovic, V. Non-intrusive appliance load monitoring using low-resolution smart meter data. In Proceedings of the 2014 IEEE International Conference on Smart Grid Communications (SmartGridComm), Venice, Italy, 3–6 November 2014; pp. 535–540. [Google Scholar]
- Altrabalsi, H.; Stankovic, V.; Liao, J.; Stankovic, L. Low-complexity energy disaggregation using appliance load modelling. Aims Energy
**2016**, 4, 884–905. [Google Scholar] [CrossRef] - Zhao, B.; He, K.; Stankovic, L.; Stankovic, V. Improving event-based non-intrusive load monitoring using graph signal processing. IEEE Access
**2018**, 6, 53944–53959. [Google Scholar] [CrossRef] - Kim, H.; Marwah, M.; Arlitt, M.; Lyon, G.; Han, J. Unsupervised disaggregation of low frequency power measurements. In Proceedings of the SIAM International Conference on data mining (SIAM), Mesa, AZ, USA, 28–30 April 2011; pp. 747–758. [Google Scholar]
- Kolter, J.Z.; Jaakkola, T. Approximate inference in additive factorial hmms with application to energy disaggregation. In Proceedings of the Artificial Intelligence and Statistics (PMLR), La Palma, Spain, 21 March 2012; pp. 1472–1482. [Google Scholar]
- Parson, O.; Ghosh, S.; Weal, M.; Rogers, A. Non-intrusive load monitoring using prior models of general appliance types. In Proceedings of the Twenty-Sixth AAAI Conference on Artificial Intelligence, Toronto, ON, Canada, 22–26 July 2012. [Google Scholar]
- Makonin, S.; Popowich, F.; Bajić, I.V.; Gill, B.; Bartram, L. Exploiting HMM sparsity to perform online real-time nonintrusive load monitoring. IEEE Trans. Smart Grid
**2015**, 7, 2575–2585. [Google Scholar] [CrossRef] - Mauch, L.; Barsim, K.S.; Yang, B. How well can HMM model load signals. In Proceedings of the 3rd International Workshop on Non-Intrusive Load Monitoring (NILM 2016), Vancouver, BC, Canada, 14–15 May 2016. number 6. [Google Scholar]
- Zoha, A.; Gluhak, A.; Imran, M.A.; Rajasegarar, S. Non-intrusive load monitoring approaches for disaggregated energy sensing: A survey. Sensors
**2012**, 12, 16838–16866. [Google Scholar] [CrossRef] - He, K.; Stankovic, L.; Liao, J.; Stankovic, V. Non-intrusive load disaggregation using graph signal processing. IEEE Trans. Smart Grid
**2016**, 9, 1739–1747. [Google Scholar] [CrossRef] - Wittmann, F.M.; López, J.C.; Rider, M.J. Nonintrusive load monitoring algorithm using mixed-integer linear programming. IEEE Trans. Consum. Electron.
**2018**, 64, 180–187. [Google Scholar] [CrossRef] - Balletti, M.; Piccialli, V.; Sudoso, A.M. Mixed-Integer Nonlinear Programming for State-based Non-Intrusive Load Monitoring. IEEE Trans. Smart Grid
**2022**, 13, 3301–3314. [Google Scholar] [CrossRef] - Razzak, M.I.; Naz, S.; Zaib, A. Deep learning for medical image processing: Overview, challenges and the future. In Classification in BioApps. Lecture Notes in Computational Vision and Biomechanics; Dey, N., Ashour, A., Borra, S., Eds.; Springer: Cham, Switzerland, 2018; pp. 323–350. [Google Scholar]
- Dokuz, Y.; Tufekci, Z. Mini-batch sample selection strategies for deep learning based speech recognition. Appl. Acoust.
**2021**, 171, 107573. [Google Scholar] [CrossRef] - Kelly, J.; Knottenbelt, W. Neural nilm: Deep neural networks applied to energy disaggregation. In Proceedings of the 2nd ACM international Conference on Embedded Systems for Energy-Efficient Built Environments, Seoul, Republic of Korea, 4–5 November 2015; pp. 55–64. [Google Scholar]
- Ding, D.; Li, J.; Zhang, K.; Wang, H.; Wang, K.; Cao, T. Non-intrusive load monitoring method with inception structured CNN. Appl. Intell.
**2022**, 52, 6227–6244. [Google Scholar] [CrossRef] - Yang, D.; Gao, X.; Kong, L.; Pang, Y.; Zhou, B. An event-driven convolutional neural architecture for non-intrusive load monitoring of residential appliance. IEEE Trans. Consum. Electron.
**2020**, 66, 173–182. [Google Scholar] [CrossRef] - Zhou, X.; Feng, J.; Li, Y. Non-intrusive load decomposition based on CNN–LSTM hybrid deep learning model. Energy Rep.
**2021**, 7, 5762–5771. [Google Scholar] [CrossRef] - Medeiros, A.; Canha, L.; Bertineti, D.; de Azevedo, R. Event classification in non-intrusive load monitoring using convolutional neural network. In Proceedings of the 2019 IEEE PES Innovative Smart Grid Technologies Conference-Latin America (ISGT Latin America), Gramado, Brazil, 15–18 September 2019; pp. 1–6. [Google Scholar]
- Harell, A.; Makonin, S.; Bajić, I.V. Wavenilm: A causal neural network for power disaggregation from the complex power signal. In Proceedings of the ICASSP IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Brighton, UK, 12–17 May 2019; pp. 8335–8339. [Google Scholar]
- Zhang, C.; Zhong, M.; Wang, Z.; Goddard, N.; Sutton, C. Sequence-to-point learning with neural networks for non-intrusive load monitoring. In Proceedings of the AAAI Conference on Artificial Intelligence, New Orleans, LA, USA, 2–7 February 2018; Volume 32. [Google Scholar]
- Athanasiadis, C.; Doukas, D.; Papadopoulos, T.; Chrysopoulos, A. A scalable real-time non-intrusive load monitoring system for the estimation of household appliance power consumption. Energies
**2021**, 14, 767. [Google Scholar] [CrossRef] - Kim, J.; Le, T.T.H.; Kim, H. Nonintrusive load monitoring based on advanced deep learning and novel signature. Comput. Intell. Neurosci.
**2017**, 2017, 4216281. [Google Scholar] [CrossRef] [PubMed] - Kaselimi, M.; Doulamis, N.; Voulodimos, A.; Protopapadakis, E.; Doulamis, A. Context aware energy disaggregation using adaptive bidirectional LSTM models. IEEE Trans. Smart Grid
**2020**, 11, 3054–3067. [Google Scholar] [CrossRef] - Faustine, A.; Pereira, L.; Bousbiat, H.; Kulkarni, S. UNet-NILM: A deep neural network for multi-tasks appliances state detection and power estimation in NILM. In Proceedings of the 5th International Workshop on Non-Intrusive Load Monitoring, Virtual Event, Japan, 18 November 2020; pp. 84–88. [Google Scholar]
- Shin, C.; Joo, S.; Yim, J.; Lee, H.; Moon, T.; Rhee, W. Subtask gated networks for non-intrusive load monitoring. In Proceedings of the AAAI Conference on Artificial Intelligence, Atlanta, GA, USA, 8–12 October 2019; Volume 33, pp. 1150–1157. [Google Scholar]
- Makonin, S.; Ellert, B.; Bajić, I.V.; Popowich, F. Electricity, water, and natural gas consumption of a residential house in Canada from 2012 to 2014. Sci. Data
**2016**, 3, 160037. [Google Scholar] [CrossRef] - Ali, P.J.M.; Faraj, R.H.; Koya, E.; Ali, P.J.M.; Faraj, R.H. Data normalization and standardization: A technical report. Mach Learn. Tech. Rep.
**2014**, 1, 1–6. [Google Scholar] - Normalization vs. Standardization Standardization, Which One Is Better. Available online: https://towardsdatascience.com/normalization-vs-standardization-which-one-is-better-f29e043a57eb (accessed on 22 April 2020).
- How to filter noise with a low pass filter — Python. Available online: https://medium.com/analytics-vidhya/how-to-filter-noise-with-a-low-pass-filter-python-885223e5e9b7 (accessed on 27 December 2019).
- Gao, R.X.; Yan, R. Wavelets: Theory and Applications for Manufacturing; Springer: Berlin/Heidelberg, Germany, 2010. [Google Scholar]
- Singh, P.; Pradhan, G.; Shahnawazuddin, S. Denoising of ECG signal by non-local estimation of approximation coefficients in DWT. Biocybernetics and Biomedical Engineering
**2017**, 3, 599–610. [Google Scholar] [CrossRef] - Damrongkulkamjorn, P.; Churueang, P. Monthly energy forecasting using decomposition method with application of seasonal ARIMA. In Proceedings of the 2005 International Power Engineering Conference, Liege, Belgium, 22–26 August 2005; pp. 1–229. [Google Scholar]

**Figure 4.**A low pass filter in the frequency domain [42].

**Figure 5.**The structure of DWT decomposition model [44].

**Figure 7.**The correlation between total power and heat pump power consumption with calendar variables.

**Figure 8.**Comparison of the estimated power consumption of the heat pump with ground truth using (

**a**) LR, (

**b**) DTR, and (

**c**) LSTM.

**Figure 10.**Ten-day-data of aggregate power consumption signal and filtered data using DWT smoothing.

**Figure 12.**A comparison of the estimated power consumption of the heat pump with ground truth using AEFLSTM.

**Figure 13.**A comparison of the estimated power consumption of the refrigerator with ground truth using AEFLSTM.

**Figure 14.**A comparison of the estimated power consumption of the dishwasher with ground truth using AEFLSTM.

**Figure 15.**A comparison of the estimated power consumption of the elctric vehicle with ground truth using AEFLSTM.

Mean (W) | STD (W) | Min. (W) | Max. (W) | |
---|---|---|---|---|

Total power | 1396.7 | 1132.4 | 269.0 | 10,542.0 |

Heat pump | 407.2 | 737.7 | 0.0 | 3030.0 |

LSTM Parameters | ||||
---|---|---|---|---|

First layer | Second layer | |||

Nodes | 100 | 50 | ||

Dropout rate | 0.2 | 0.2 | ||

Return sequence | True | False | ||

Activation function | relu | relu | ||

Optimizer | loss | epotchs | batch_size | validation_split |

Adam | MSE | 15 | 30 | 0.3 |

HPE | MAE (W) | RMSE (W) |
---|---|---|

LR | 319.35 | 500.9 |

DTR | 97.01 | 268.6 |

LSTM | 90.2 | 208.68 |

HPE | MAE (W) | RMSE (W) |
---|---|---|

LSTM | 90.2 | 208.68 |

DWT-LSTM | 38.4 | 148.75 |

LPF-LSTM | 51.5 | 157.1 |

SD-LSTM | 39.7 | 153.2 |

**Table 5.**Performance metrics (MAE and RMSE) of the difference between real and estimated data for different appliances.

FGE | DWE | EV | ||||
---|---|---|---|---|---|---|

MAE (W) | RMSE (W) | MAE (W) | RMSE (W) | MAE (W) | RMSE (W) | |

LR | 59.48 | 75.84 | 29.32 | 106.66 | 715.76 | 975.67 |

DTR | 50.94 | 71.39 | 48.64 | 151.65 | 363.36 | 782.62 |

LSTM | 25.74 | 58.82 | 10.99 | 36.59 | 300.04 | 735.07 |

AEFLSTM | 14.4 | 50.6 | 4.89 | 28.33 | 232.37 | 668.01 |

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**MDPI and ACS Style**

Kianpoor, N.; Hoff, B.; Østrem, T.
Deep Adaptive Ensemble Filter for Non-Intrusive Residential Load Monitoring. *Sensors* **2023**, *23*, 1992.
https://doi.org/10.3390/s23041992

**AMA Style**

Kianpoor N, Hoff B, Østrem T.
Deep Adaptive Ensemble Filter for Non-Intrusive Residential Load Monitoring. *Sensors*. 2023; 23(4):1992.
https://doi.org/10.3390/s23041992

**Chicago/Turabian Style**

Kianpoor, Nasrin, Bjarte Hoff, and Trond Østrem.
2023. "Deep Adaptive Ensemble Filter for Non-Intrusive Residential Load Monitoring" *Sensors* 23, no. 4: 1992.
https://doi.org/10.3390/s23041992