Adaptive Data Window-based Algorithm For Power Load Decomposition
Authors: 
Hongtai Yuan, Jing Chen, Hao Sun, Yudong Geng, Shuang Liu, Qihang Cui, Qingxiao Xue, Yongkang WangAuthors Info & Claims
Pages 8 - 16
Abstract
Power load recognition can be used to conduct users’ electricity consumption. Its key is to improve the recognition accuracy of load factorization. In this paper, we propose a non-intrusive load factorization algorithm based on adaptive data windows, which adaptively selects a more suitable data windows based the limitation of load factorization time and accuracy. Two thread are set to separately determine the timeout of load factorization and the minimum value of the mean absolute percentage error (MAPE) for obtaining the optimal data windows of load factorization. Six algorithms including DAE, CRNN, GRU, RNN, Seq2Seq, Seq2Point are used to implement load factorization for validating the effectiveness and applicability of this method. And the experiments are conducted by comparing this method with the conventional method. The experimental results show that the proposed method has a small MAPE with respect to the real data of the household electric appliances within the time limitation of load decomposition. It is reliable, robust, fast, and more suitable for real-time environments where load decomposition is performed on different household electric appliances.
References
[1]
Hart G W. Nonintrusive appliance load monitoring[J]. Proceedings of the IEEE, 80 (12) : 1870 - 1891, 1992.
[2]
J. Z. Kolter and M. J. Johnson, "REDD: A public data set for energy disaggregation research," in Proc. SIGKDD, vol. 25, 2011, pp. 59-62.
[3]
Zimmermann JP, Evans M, Griggs J, Household electricity survey: A study of domestic electrical product usage. Intertek Testing & Certification Ltd, 2012, 213-214.
[4]
Anderson K, Ocneanu A, Benitez D, BLUED: A fully labeled public dataset for event-based non-intrusive load monitoring research. Proceedings of the 2nd KDD Workshop on Data Mining Applications in Sustainability (SustKDD). Beijing: ACM, 2012. 12–16.
[5]
Parson O, Fisher G, Hersey A, Dataport and NILMTK: A building data set designed for non-intrusive load monitoring. Proceedings of 2015 IEEE Global Conference on Signal and Information Processing (GlobalSIP). Orlando: IEEE, 2015. 210–214.
[6]
J. Kelly and W. Knottenbelt, "The UK-DALE dataset, domestic appliance-level electricity demand and whole-house demand from five UK homes," Sci. Data, vol. 2, Mar. 2015, Art. no. 150007.
[7]
X. Hu, Y. Peng, H. Mo, T. Cai and Q. Deng, "An Improved Time–Frequency Feature Fusion Based Nonintrusive Load Monitor for Load Identification," 2022 2nd International Conference on Electrical Engineering and Mechatronics Technology (ICEEMT), Hangzhou, China, 2022, pp. 1-5.
[8]
Y. Xiao, Y. Hu, H. He, D. Zhou, Y. Zhao and W. Hu, "Non-Intrusive Load Identification Method Based on Improved KM Algorithm," in IEEE Access, vol. 7, pp. 151368-151377, 2019.
[9]
C. Wang, W. Miao, Z. Zeng, S. Li, J. Jin and H. Yang, "Research on a non-intrusive electrical load intelligent identification algorithm," 2022 IEEE 5th International Conference on Information Systems and Computer Aided Education (ICISCAE), Dalian, China, 2022, pp. 685-689.
[10]
Zhao H, Wei G, Hu C,et al.Research on online non-intrusive load identification system based on multi-threaded CUSUM-MLP algorithm[C]//2021 IEEE Sensors.0[2023-06-15].
[11]
Yi Shu Hui,Wang Jian,Liu Jun Jie. Simultaneous Load Identification Method Based on Hybrid Features and Genetic Algorithm for Nonintrusive Load Monitoring[J]. Mathematical Problems in Engineering,2022,2022.
[12]
Hamed H.H. Aly. A proposed intelligent short-term load forecasting hybrid models of ANN, WNN and KF based on clustering techniques for smart grid[J]. Electric Power Systems Research,2020,182(C).
[13]
Feng Renhai,Xue Yuanbiao,Wang Wei,Xiao Meng. Saturated load forecasting based on clustering and logistic iterative regression[J]. Electric Power Systems Research,2022,202.
[14]
Reeves Gary R.,Lawrence Kenneth D. Combining forecasts given different types of objectives[J]. European Journal of Operational Research,1991,51(1).
[15]
Bo Yin,Zhenhuan Li,Jiali Xu,Lin Li,Xinghai Yang,Zehua Du. Non-intrusive load monitoring algorithm based on household electricity use habits[J]. Neural Computing and Applications,2021,34(18).
[16]
M. Ali, M. R. Djalal, S. Arfaah, Muhlasin, M. Fakhrurozi and R. Hidayat, "Monitoring and Identification Electricity Load Using Artificial Neural Network," 2021 7th International Conference on Electrical, Electronics and Information Engineering (ICEEIE), Malang, Indonesia, 2021, pp. 1-6.
[17]
A. Zhao, X. Zhao, J. Jing, J. Xi, and P. Cui, ‘Non-Intrusive Electric Load Identification Algorithm for Optimizing Convolutional Neural Network Hyper-Parameters’, Laser and Optoelectronics Progress, vol. 60, no. 2, Jan. 2023.
[18]
Y. Liu, T. E. Song, X. Sun, S. Gao, and X. Huang, ‘Temporal feature adaptive non-intrusive load monitoring via unsupervised probability density evolution’, Energy Reports, vol. 7, pp. 209–217, Nov. 2021.
[19]
W. Zhang, Q. Chen, J. Yan, S. Zhang, and J. Xu, ‘A novel asynchronous deep reinforcement learning model with adaptive early forecasting method and reward incentive mechanism for short-term load forecasting’, Energy, vol. 236, Dec. 2021.
[20]
C. Li, R. Yang and H. Wang, "Non-intrusive Load Monitoring in Industry Based on Gradient Boosting Algorithm," 2022 IEEE 25th International Conference on Computer Supported Cooperative Work in Design (CSCWD), Hangzhou, China, 2022, pp. 1523-1528.
[21]
Li Dan,Sun Guangfan,Miao Shuwei,Gu Yingzhong,Zhang Yuanhang,He Shuai. A short-term electric load forecast method based on improved sequence-to-sequence GRU with adaptive temporal dependence[J]. International Journal of Electrical Power and Energy Systems,2022,137.
[22]
J. -S. Kang, M. Yu, L. Lu, B. Wang and Z. Bao, "Adaptive Non-Intrusive Load Monitoring Based on Feature Fusion," in IEEE Sensors Journal, vol. 22, no. 7, pp. 6985-6994, 1 April1, 2022.
[23]
D. Wang, Z. J. Shen, X. Yin, S. Tang, J. Wang and Z. Shuai, "Neural Network Based Adaptive Model Predictive Control for Power Converters Under Load Parameter Uncertainties," 2022 4th International Conference on Electrical Engineering and Control Technologies (CEECT), Shanghai, China, 2022, pp. 124-129.
[24]
M. Yu, B. Wang, L. Lu, Z. Bao and D. Qi, "Non-Intrusive Adaptive Load Identification Based on Siamese Network," in IEEE Access, vol. 10, pp. 11564-11573, 2022.
[25]
P. Vincent, H. Larochelle, Y. Bengio, and P.-A. Manzagol, “Extracting and Composing Robust Features with Denoising Autoencoders,” in International Conference on Machine Learning, 2008, vol. 311, pp. 1–10.
[26]
Shi B, Bai X, Yao C. An End-to-End Trainable Neural Network for Image-Based Sequence Recognition and Its Application to Scene Text Recognition[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2017, 39(11): 2298–2304.
[27]
W. Zaremba, I. Sutskever, and O. Vinyals, “Recurrent neural network regularization,” in Proc. International Conference on Learning Repre-sentations (ICLR 2015), San Diego, CA, USA, May 2015, pp. 1-8.
[28]
Chaoyun Zhang,Mingjun Zhong,Zongzuo Wang,Nigel H. Goddard,Charles A. Sutton. Sequence-to-point learning with neural networks for nonintrusive load monitoring.[J]. CoRR,2016,abs/1612.09106.
[29]
I. Sutskever, O. Vinyals, and Q. V. Le, "Sequence to sequence learning with neural networks, " in Advances in Neural Information Processing Systems 27: Annu. Conf. Neural Information Processing Systems 2014, Montreal, Quebec, Canada, 2014, pp. 3104-3112.
[30]
Cho, Kyunghyun, "Learning phrase representations using RNN encoder-decoder for statistical machine translation." arXiv preprint arXiv:1406.1078 (2014).
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September 2023
441 pages
ISBN:9798400707667
DOI:10.1145/3627377
Copyright © 2023 ACM.
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Published: 04 December 2023
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- Research-article
- Research
- Refereed limited
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- Shandong Provincial College Students' Innovative Entrepreneurial Training Plan Program
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ICBDT 2023
ICBDT 2023: 2023 6th International Conference on Big Data Technologies
September 22 - 24, 2023
Qingdao, China
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