# Non-Intrusive Load Monitoring and Controllability Evaluation of Electric Vehicle Charging Stations Based on K-Means Clustering Optimization Deep Learning

^{1}

^{2}

^{3}

^{4}

^{*}

## Abstract

**:**

## 1. Introduction

- A practical and adaptive NILM model is established based on BP neural network.
- Non-supervised learning optimization is based on the proposed NILM model to improve the scalability and robustness of the method.
- The proposed NILM solution will combine the neural network model based on unsupervised learning optimization and supervision, which can determine the load of random electric vehicle while filling an important research gap.

## 2. Modeling of Non-Intrusive Load Monitoring Algorithms

#### 2.1. K-Means Clustering Algorithm

#### 2.2. Neural Network Algorithms

#### 2.3. Neural Network Modeling for NILM

#### 2.4. NILM Model of Electric Vehicle Charging Station Based on Deep Learning

## 3. Case Analysis

#### 3.1. Electric Vehicle Charging Station Power Feature Dataset

_{0}to T

_{1}[31]. PRE, REC, and F1 scores are the basic indicators of NILM and can be used to reflect the correctness of the analysis results judged by the NILM model. MAE reflects the accuracy of results across time periods. The lower the resulting value of MAE, the higher the precision of its decomposed value.

#### 3.2. Load Perception of Electric Vehicle Charging Station Based on Deep Learning

#### 3.3. Electric Vehicle Charging Station Load Perception Based on Optimized Deep Learning for Newly Added Electric Vehicle Loads

#### 3.4. Evaluation of Adjustable Capability of Electric Vehicle Charging Station

## 4. Conclusions

## Author Contributions

## Funding

## Data Availability Statement

## Conflicts of Interest

## References

- Wang, J.; Deng, J.; Liu, Y.; Wang, Y. Non-intrusive load perception and flexibility evaluation for electric vehicle charging station: A deep learning based approach. In Proceedings of the 2022 IEEE 5th International Electrical and Energy Conference (CIEEC), Nangjing, China, 27–29 May 2022; pp. 2570–2575. [Google Scholar]
- Notice of the State Council on Printing and Distributing the Action Plan for Carbon Peaking Before 2030; Gazette of the State Council of the People’s Republic of China: Beijing, China, 2021; pp. 48–58.
- Notice of the General Office of the State Council on Printing and Distributing the New Energy Vehicle Industry Development Plan (2021–2035); Gazette of the State Council of the People’s Republic of China: Beijing, China, 2020; pp. 16–23.
- Ebrahimi, M.; Rastegar, M.; Arefi, M.M. Real-Time Estimation Frameworks for Feeder-Level Load Disaggregation and PEVs’ Charging Behavior Characteristics Extraction. IEEE Trans. Ind. Inform.
**2022**, 18, 4715–4724. [Google Scholar] [CrossRef] - Hart, G.W. Nonintrusive appliance load monitoring. Proc. IEEE
**1992**, 80, 1870–1891. [Google Scholar] [CrossRef] - Cox, R.; Leeb, S.B.; Shaw, S.R.; Norford, L.K. Transient event detection for nonintrusive load monitoring and demand side management using voltage distortion. In Proceedings of the Twenty-First Annual IEEE Applied Power Electronics Conference and Exposition, APEC ‘06, Dallas, TX, USA, 19–23 March 2006; p. 7. [Google Scholar]
- He, K.; Stankovic, L.; Liao, J.; Stankovic, V. Non-Intrusive Load Disaggregation Using Graph Signal Processing. IEEE Trans. Smart Grid
**2018**, 9, 1739–1747. [Google Scholar] [CrossRef] [Green Version] - Lin, Y.; Tsai, M. Non-Intrusive Load Monitoring by Novel Neuro-Fuzzy Classification Considering Uncertainties. IEEE Trans. Smart Grid
**2014**, 5, 2376–2384. [Google Scholar] [CrossRef] - Liu, S.; Liu, Y.; Gao, S.; Guo, H.; Song, T.; Jiang, W.; Li, Z.; Wang, J.; Song, Y. Non-invasive load decomposition method based on multi-feature objective function PCA-ILP. Electr. Power Constr.
**2020**, 41, 1–8. [Google Scholar] - Kolter, J.Z.; Jaakkola, T.S. Approximate inference in additive factorial HMMs with application to energy disaggregation. In Proceedings of the International Conference on Artificial Intelligence and Statistics, La Palma, Spain, 21–23 April 2012. [Google Scholar]
- Yan, X.F.; Zhai, S.P.; Wang, Z.H.; Wang, F.; He, G.Y. Application of Deep Neural Networks in Non-Intrusive Load Decomposition. Autom. Electr. Power Syst.
**2019**, 43, 126–132+167. [Google Scholar] - Liu, Q.; Kamoto, K.M.; Liu, X.; Sun, M.; Linge, N. Low-Complexity Non-Intrusive Load Monitoring Using Unsupervised Learning and Generalized Appliance Models. IEEE Trans. Consum. Electron.
**2019**, 65, 28–37. [Google Scholar] [CrossRef] - Li, D.; Dick, S. Residential Household Non-Intrusive Load Monitoring via Graph-Based Multi-Label Semi-Supervised Learning. IEEE Trans. Smart Grid
**2019**, 10, 4615–4627. [Google Scholar] [CrossRef] - 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] [Green Version] - Kelly, J.; Knottenbelt, W.J. 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, Korea, 4–5 November 2015. [Google Scholar]
- Tan, P.N.; Steinback, M.; Kumar, V. Introduction to Data Mining. Posts & Telecom Press: Beijing, China.
- Andrean, V.; Zhao, X.; Teshome, D.F.; Huang, T.; Lian, K. A Hybrid Method of Cascade-Filtering and Committee Decision Mechanism for Non-Intrusive Load Monitoring. IEEE Access
**2018**, 6, 41212–41223. [Google Scholar] [CrossRef] - Bonfigli, R.; Felicetti, A.; Principi, E.; Fagiani, M.; Squartini, S.; Piazza, F. Denoising autoencoders for Non-Intrusive Load Monitoring: Improvements and comparative evaluation. Energy Build.
**2018**, 158, 1461–1474. [Google Scholar] [CrossRef] - Faustine, A.; Pereira, L. Multi-Label Learning for Appliance Recognition in NILM Using Fryze-Current Decomposition and Convolutional Neural Network. Energies
**2020**, 13, 4154. [Google Scholar] [CrossRef] - Xi, X.F.; Zhou, G.D. A Survey on Deep Learning for Natural Language Processing. Acta Autom. Sin.
**2016**, 42, 1445–1465. [Google Scholar] - Monteiro, R.; Santana, J.; Teixeira, R.; Bretas, A.S.; Aguiar, R.; Poma, C. Non-intrusive load monitoring using artificial intelligence classifiers: Performance analysis of machine learning techniques. Electr. Power Syst. Res.
**2021**, 198, 107347. [Google Scholar] [CrossRef] - Hengyong, L.; Shuaibin, S.; Xuhui, X.U.; Dongguo, Z.; Ruolin, M.; Wenshan, H.U. A Non-Intrusive Load Identification Method Based on Correlation RNN Model. Power Syst. Prot. Control.
**2019**, 47, 9. [Google Scholar] - Figueiredo, M.; Ribeiro, B.; Almeida, A.d. Electrical Signal Source Separation Via Nonnegative Tensor Factorization Using On Site Measurements in a Smart Home. IEEE Trans. Instrum. Meas.
**2014**, 63, 364–373. [Google Scholar] [CrossRef] - Zhou, M.; Song, X.; Tu, J.; Li, G.; Luan, K. Residential Electricity Consumption Behavior Analysis Based on Non-Intrusive Load Monitoring. Power Grid Technol.
**2018**, 42, 1–9. [Google Scholar] - Ciancetta, F.; Bucci, G.; Fiorucci, E.; Mari, S.; Fioravanti, A. A New Convolutional Neural Network-Based System for NILM Applications. IEEE Trans. Instrum. Meas.
**2021**, 70, 1–12. [Google Scholar] [CrossRef] - Nisha; Kaur, P.J. Cluster quality based performance evaluation of hierarchical clustering method. In Proceedings of the 2015 1st International Conference on Next Generation Computing Technologies (NGCT), Dehradun, India, 4–5 September 2015. [Google Scholar]
- Mirzal, A. Statistical Analysis of Microarray Data Clustering using NMF, Spectral Clustering, Kmeans, and GMM. IEEE/ACM Trans. Comput. Biol. Bioinform.
**2022**, 19, 1173–1192. [Google Scholar] [CrossRef] - Shlien, S. A Method for Computing the Partial Singular Value Decomposition. IEEE Trans. Pattern Anal. Mach. Intell.
**1982**, PAMI-4, 671–676. [Google Scholar] [CrossRef] - He, R.; Hu, B.G.; Zheng, W.S.; Kong, X.W. Robust Principal Component Analysis Based on Maximum Correntropy Criterion. IEEE Trans. Image Process.
**2011**, 20, 1485–1494. [Google Scholar] [CrossRef] - Chien, J.; Wu, M. Adaptive Bayesian Latent Semantic Analysis. IEEE Trans. Audio Speech Lang. Process.
**2008**, 16, 198–207. [Google Scholar] [CrossRef] - Liu, Y.; Wang, J.; Deng, J.; Sheng, W.; Tan, P. Non-Intrusive Load Monitoring Based on Unsupervised Optimization Enhanced Neural Network Deep Learning. Front. Energy Res.
**2021**, 9, 718916. [Google Scholar] [CrossRef] - Xu, Q.S.; Lou, O.D.; Zheng, A.X.; Liu, J.Y. A Non-Intrusive Load Decomposition Method Based on Affinity Propagation and Genetic Algorithm Optimization. Trans. China Electrotech. Soc.
**2018**, 33, 11. [Google Scholar] - Wang, K.; Zhong, H.; Yu, N.; Xia, Q. Nonintrusive Load Monitoring based on Sequence-to-sequence Model With Attention Mechanism. Zhongguo Dianji Gongcheng Xuebao/Proc. Chin. Soc. Electr. Eng.
**2019**, 39, 75–83. [Google Scholar] - Liang, H.F.; Liu, B.; Zheng, C.; Cao, D.W.; Gao, J.Y. Research on Modeling Method of Residential Electric Vehicle Demand Response Characteristics Based on Load Identification under Smart Grid. Mod. Electr. Power
**2018**, 35, 1–9. [Google Scholar]

**Figure 3.**Non-intrusive load monitoring process of electric vehicle charging station based on deep learning.

**Figure 6.**Decomposition result of EV1 of electric vehicle charging station with no new random load by deep neural network.

**Figure 8.**Decomposition result of deep neural network on EV1 of electric vehicle charging station with no new load.

**Figure 10.**Decomposition results of deep neural network on EV6 of new load electric vehicle charging station.

**Figure 11.**K-means clustering results for electric vehicle charging stations without new random loads.

**Figure 12.**K-means clustering results of electric vehicle charging stations with newly added random loads.

**Figure 15.**Decomposition results of new random load electric vehicle charging station EV11 by deep neural network.

Index | PRE | REC | F1 | MAE |
---|---|---|---|---|

EV1 | 0.9991 | 0.9985 | 0.9988 | 0.0332 |

EV2 | 0.9986 | 0.9917 | 0.9951 | 0.1595 |

EV3 | 1 | 0.9786 | 0.9892 | 0.1295 |

EV4 | 0.9971 | 0.6705 | 0.8018 | 0.1693 |

EV5 | 0.9965 | 0.9957 | 0.9961 | 0.1073 |

EV6 | 0.8947 | 0.9989 | 0.9439 | 0.2069 |

P (EV11 Not Added) | Q (EV11 Not Added) | P (EV11 Added) | Q (EV11 Added) | |
---|---|---|---|---|

1 | 0 | 0 | 0 | 0 |

2 | 180.159 | 86.097 | 60.053 | 19.780 |

3 | 300.253 | 135.004 | 360.330 | 180.104 |

4 | 60.047 | 29.097 | 240.203 | 115.236 |

5 | 120.104 | 48.862 | 120.106 | 57.021 |

6 | 240.196 | 105.906 | 180.159 | 86.097 |

7 | 240.203 | 115.236 | 300.255 | 135.006 |

8 | 60.053 | 19.780 | 60.037 | 37.236 |

9 | 120.093 | 66.293 | 240.196 | 105.906 |

10 | 60.037 | 37.236 | 60.047 | 29.097 |

11 | 120.106 | 57.021 | 240.212 | 123.336 |

12 | 240.212 | 123.336 | 120.102 | 48.862 |

13 | 120.093 | 66.293 |

Index | PRE | REC | F1 | MAE |
---|---|---|---|---|

EV1 | 0.9891 | 0.9985 | 0.9988 | 0.0332 |

EV2 | 0.9986 | 0.9783 | 0.9890 | 0.1414 |

EV3 | 1 | 0.9773 | 0.9885 | 0.0889 |

EV4 | 0.9971 | 0.6033 | 0.7526 | 0.2153 |

EV5 | 0.9965 | 0.9947 | 0.9953 | 0.1203 |

EV6 | 0.8947 | 0.9972 | 0.9986 | 0.0557 |

EV11 | 1 | 0.5828 | 0.7364 | 0.3747 |

Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. |

© 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).

## Share and Cite

**MDPI and ACS Style**

Lu, S.; Feng, X.; Lin, G.; Wang, J.; Xu, Q.
Non-Intrusive Load Monitoring and Controllability Evaluation of Electric Vehicle Charging Stations Based on *K*-Means Clustering Optimization Deep Learning. *World Electr. Veh. J.* **2022**, *13*, 198.
https://doi.org/10.3390/wevj13110198

**AMA Style**

Lu S, Feng X, Lin G, Wang J, Xu Q.
Non-Intrusive Load Monitoring and Controllability Evaluation of Electric Vehicle Charging Stations Based on *K*-Means Clustering Optimization Deep Learning. *World Electric Vehicle Journal*. 2022; 13(11):198.
https://doi.org/10.3390/wevj13110198

**Chicago/Turabian Style**

Lu, Shixiang, Xiaofeng Feng, Guoying Lin, Jiarui Wang, and Qingshan Xu.
2022. "Non-Intrusive Load Monitoring and Controllability Evaluation of Electric Vehicle Charging Stations Based on *K*-Means Clustering Optimization Deep Learning" *World Electric Vehicle Journal* 13, no. 11: 198.
https://doi.org/10.3390/wevj13110198