Research on Electricity Consumption Behavior of Electric Power Users Based on Tag Technology and Clustering Algorithm

Zhong, Chunlin; Shao, Jiayi; Zheng, Fei; Zhang, Kaizhen; Lv, Hui

doi:10.1109/icisce.2018.00102

Cited by 19 publications

(8 citation statements)

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“…In order to prevent experimental errors, we repeat the experiment 10 times, and the timestamps are set to = (12,5,3). Table 1 illustrates the quantitative results in terms of MAP, Recall, F1-Score performed by different methods on test set, where the best performance is boldfaced.…”

Section: Results Analysismentioning

confidence: 99%

“…Li et al [4] proposed a purchase prediction method based on large-scale user behavior logs, which can predict users' "on-off line" purchase behavior in the future by using the integrated decision tree model. Zhong et al [5] constructed a user tag library based on a large number of user attribute data, and used k-means algorithm to classify user groups. Chen et al [6] and Liu et al [7] proposed collaborative filtering recommendation algorithm based on user attribute clustering to predict user tags, respectively.…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Multiple Time Series Perceptive Network for User Tag Suggestion in Online Innovation Community

et al. 2021

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User tag suggestion technique, aiming at learning users' preferences over knowledge products from their historical behaviors, plays an important role in generating personalized recommendation in online innovation community. However, most current user tagging solutions only utilize a single kind of behavior to predict a single tag for users, resulting in weak generalization of user profile. In this paper, we propose a multiple time series perceptive network (MTSPN) for user tagging tasks in online innovation community. In particular, MTSPN takes multiple kinds of user behaviors into consideration for collaborative perception purpose, in which multi-scale sequential features are extracted from different sequential behaviors, and a multi-label classification module is built-in the proposed MTSPN model to predict multiple tags for users. Our encouraging experimental results on a real-world dataset collected from "Thingiverse" community validate the superiority of the our MTSPN model over several existing user tagging methods.

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Section: Results Analysismentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Multiple Time Series Perceptive Network for User Tag Suggestion in Online Innovation Community

et al. 2021

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“…Thanks to the development of smart grid, IoTs applied in power industry can help operators to monitor and gain fine-grained data. Thus portrait techniques were gradually introduced to power industry, especially in the areas including outlier detection (Tang et al, 2014), equipment ledger management (Li et al, 2020), business management for commercial Internet platform of stateowned corporation (Yu et al, 2019), transmission line management (Zhang, 2019), electricity enterprise supplier management (Huang et al, 2021), user management (Shi et al, 2016;Wang et al, 2017;Feng et al, 2018;Lu et al, 2018;Zhong et al, 2018;ShiLu and Tian, 2020;Hu et al, 2021;Kong et al, 2021;Li et al, 2021;Yan et al, 2021). Users' tariff recovery risk, credit rating and energy efficiency rating was assessed with power consumption, payment and arrears data in (Shi et al, 2016;Wang et al, 2017;Yan et al, 2021) respectively.…”

Section: Introductionmentioning

confidence: 99%

Demand-response oriented multi-dimension refined portrait of adjustable resources based on load and survey data fusion

Chen

Gan²,

Chen³

et al. 2022

Front. Energy Res.

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Adjustable resources on the demand side of power system plays a vital role to improve operational flexibility of future low-carbon power system integrated with high-penetration renewable generations. While, these demand-side resources may underperform their expected potentials, due to the lack of understanding on consumers’ refined behaviors. Facing the flexibility improvement of future power system, refined portrait structure of single user combining load characteristics and subjective behavior, is constructed with multi-dimension label system from 4 aspects, including energy consumption and load characteristics, adjustable potential, behavioral awareness and user’s nature. Aiming at supplying demand response service, several key indexes are selected and further evaluated here, via data-driven load character analysis and social-survey-driven user’s subjective consciousness mining based on comprehensive evaluation with combination weighting approach. For practical application to demand response decision making, large-scale user adjustable resource is evaluated and classified based on multivariate density-based clustering algorithm. Numerical results show the feasibility and rationality of the proposed assessment method.

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“…In the early stage of the study, traditional user portraits were mainly used in the marketing portal to build user electricity sensitivity or credit portraits to guide the electricity recovery work (Sanchez et al, 2008;Han et al, 2014;Ampimah et al, 2017). To meet the requirements of demand response, the methods of establishing electricity consumption behavior tag library and realizing the portrait of different types of users' electricity consumption behavior patterns were proposed in (Qiu et al, 2017) and (Zhong et al, 2018). With the gradual popularization of new energy (Yang et al, 2016;Yang et al, 2017;Yang et al, 2018;Yang et al, 2019a;Yang et al, 2019b;Yang et al, 2020), the load change and influence mechanism on the user side are becoming more and more complex.…”

Section: Introduction Development Of Customer Portraitmentioning

confidence: 99%

Customer Load Forecasting Method Based on the Industry Electricity Consumption Behavior Portrait

Guan

Zhang

et al. 2021

Front. Energy Res.

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With the dramatic increase of energy demand and the continuous increase of power system operation pressure, higher requirements are put forward for the development of power grid planning and optimization operation. It is important for the refinement of distribution network planning to deeply extract the characteristics of user load. First, the process of load characteristic analysis method from the user level to the industry level is proposed, which achieves the division of electricity consumption patterns of various industries, thus building a panoramic portrait of industry electricity consumption behavior. Then, by expanding the information filled in by traditional customers, the feature vector of each user is extracted, and the users' industry electricity consumption patterns are used as the label. Therefore, a method for identifying the electricity consumption pattern of the customer based on the BB-stacking model fusion framework is proposed, which yields the preliminary forecast results of customer load based on the actual load accounting results of the customers. Finally, comparative simulations with different methods verify the effectiveness of the proposed algorithm, which can provide prominent guidance for the actual distribution network planning work.

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Research on Electricity Consumption Behavior of Electric Power Users Based on Tag Technology and Clustering Algorithm

Cited by 19 publications

References 0 publications

Multiple Time Series Perceptive Network for User Tag Suggestion in Online Innovation Community

Multiple Time Series Perceptive Network for User Tag Suggestion in Online Innovation Community

Demand-response oriented multi-dimension refined portrait of adjustable resources based on load and survey data fusion

Customer Load Forecasting Method Based on the Industry Electricity Consumption Behavior Portrait

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