Author(s)

Wang Baoyi ¹, Lv Jin ¹, Zhang Shaomin ¹

Affiliation(s)

¹ School of Control and Computer Engineering, North China Electric Power University, Baoding, 071003, China

Corresponding Author

Wang Baoyi

ABSTRACT

The study of power load patterns is the premise and basis of power distribution network maintenance. In view of the shortage of the existing power load model focusing on industry, agriculture, commerce and other large users, not on residents, In this paper, a method of residential power load patterns based on clustering and deep belief network is proposed. Firstly, use the improved k-means clustering algorithm for the residential electricity load clustering analysis to extract the typical load curve of each cell; and then a depth belief network classifier is constructed to classify the typical load curves of each cell, to identify the residential power load patterns and provide reliable support for distribution network maintenance. The effectiveness of the method is demonstrated by experiments on power data.

KEYWORDS

Residential Power, Load Patterns, Clustering, Classification

CITE THIS PAPER

Baoyi, W., Jin, L.,Shaomin, Z. (2017) Study of Residential Power Load Patterns Based on Clustering and Deep Belief Network. Journal of Network Computing and Applications (2017) Vol.2, Num. 1: 7-13.

REFERENCES

[1] Yundong Gu, Sujie Zhang, Junshu Feng. Multi-model fuzzy synthesis forecasting of electric power loads for larger consummers[J]. Transactions of China Electrotechnical Society, 2015, 30(23):110-115.
[2] Na Yu, Lezheng Yu, Guoqing Li. Controllable load management strategy for commercial users based on multi-agent in smart grid environment[J]. Automation of Electric Power Systems, 2015, 39(17): 89- 96.
[3] Suxiang Zhang, Jianming Liu, Bingzhen Zhao, et al. Cloud computing-based analysis on residential electricity consumption behavior[J]. Power System Technology, 2013, 37(6): 1542-1546
[4] Wenjun Zhu, Yi Wang, Min Luo, et al. Distributed Clustering Algorithm for Awareness of Electricity Consumption Characteristics of Massive Consumers[J]. Automation of Electric Power Systems, 2016, 40(12):21-27.
[5] Karun P Warrior, Shrenik M, Nimish Soni. Short-term electrical load forecasting using predictive machine learning models[C]// India Conference. IEEE, 2017.
[6] Bin Yu, Shaozi Li, Suxia Xu, et al. Deep learning: a key of stepping into the era of big data[J]. Journal of Engineering Studies, 2014, 6(3): 233-243
[7] Haben Stephen, Singleton Colin, Grindrod Peter. Analysis and Clustering of Residential Customers Energy Behavioral Demand Using Smart Meter Data[J]. IEEE Transactions on Smart Grid, 2016, 7(1):136-144.
[8] Fahiman Fateme, M.Erfani Sarah, Rajasegarar Sutharshan, et al. Improving load forecasting based on deep learning and K-shape clustering[C]// International Joint Conference on Neural Networks. IEEE, 2017:4134-4141.
[9] Sihai Li. Zibin Man. K-means Clustering Algorithm Based on Adaptive Feature Weighted[J]. Computer Technology and Development, 2013, 23(6):98-101.
[10]Yong Ma, Changchun Bao, Bingyin Xia. Speaker segmentation based on discriminative deep belief networks[J]. Journal of Tsinghua University (Science and Technology), 2013, 53(6): 804-807.
[11]Xin Shi, Yongli Zhu, Churila Sa, et al. Power transformer fault classifying model based on deep belief network[J]. Power System Protection and Control, 2016, 44(1): 71-76.

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