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Modeling User Behavior through Electricity Consumption Patterns

J. M. Gil
2018 Figshare  
We present our research on user behavior concerning electricity consumption in office buildings and residential environments.  ...  Reducing energy consumption in buildings of all kinds is a key challenge for researchers since it can help to notably reduce the waste of energy and its associated costs.  ...  This work is intended for describing our research when modeling user behavior through electricity consumption patterns.  ... 
doi:10.6084/m9.figshare.7132982.v1 fatcat:zfhdzpw32rgqhigggycwfu5fbm

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

Weiling Guan, Daolu Zhang, Huang Yu, Binggang Peng, Yufeng Wu, Tao Yu, Keying Wang
2021 Frontiers in Energy Research  
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.  ...  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  ... 
doi:10.3389/fenrg.2021.742993 fatcat:2lrnuacesbblxg45ojixyic5jy

The Abnormal Electricity Consumption Detection System Based on the Outlier Behavior Pattern Recognition

Yi-jia TANG, Mei-qi WEN, Tian-zhuang XU, Yi-bo WANG, Zhao-feng HUANG
2017 DEStech Transactions on Environment Energy and Earth Science  
This paper systematically studies the power-consumption data, analyzes the characteristics of the behavior of the power-stealing users, and constructs a working process to detect abnormal electricity consumption  ...  This study of the abnormal electricity consumption detection system based on the outlier behavior pattern recognition, provides a reference for the researchers of anti-power-stealing.  ...  Case Study and Summary The abnormal electricity consumption detection is realized through the outlier behavior pattern recognition.  ... 
doi:10.12783/dteees/icepe2017/11861 fatcat:gukpkevdgfdvljkxqpognhzxum

AUD-MTS: An Abnormal User Detection Approach Based on Power Load Multi-Step Clustering with Multiple Time Scales

Rongheng Lin, Fangchun Yang, Mingyuan Gao, Budan Wu, Yingying Zhao
2019 Energies  
With the rapid growth of Smart Grid, electricity load analysis has become the simplest and most effective way to divide user groups and understand user behavior.  ...  Secondly, time scale conversion is performed so that the analysis subject can be transformed from load pattern to user behavior.  ...  The whole process only needs to input 24-point electricity load data of all users and outputs abnormal user clusters, typical load patterns and user behavior classification through progressive analysis  ... 
doi:10.3390/en12163144 fatcat:dtk6ovacvfbddeqgsfoa36walm

Selecting the optimal charging strategy of electric vehicles using simulation based on users' behavior pattern data

Sangmin Yeo, Deok-Joo Lee
2021 IEEE Access  
Each user has an individually unique behavior pattern, and accordingly, the electricity consumption by time is different for each user.  ...  based on the behavioral data of users' electricity demand and to find the optimal charging strategy according to the each user's demand pattern by using the model.  ... 
doi:10.1109/access.2021.3090437 fatcat:f6blmlndo5eslayk54e2cycdoq

Analysis of User Energy Consumption Patterns Based on Data Mining

Weitao Liu, Fuqing Wang, Hang Shi, Yan Zhang, Ruobo Chen, X. Wei
2020 E3S Web of Conferences  
Then, it elaborates on the collection and aggregation of electricity consumption information, and refined user classification.  ...  Next, the comprehensive application of energy consumption behavior analysis in load forecasting, demand response modeling and other typical scenarios is deeply analyzed.  ...  Acknowledgments This paper is supported by "Science and technology project of State Grid Tianjin electric power company (KJ20-1-48)".  ... 
doi:10.1051/e3sconf/202021302040 fatcat:cjbndpukunb3jpajc4dfjm2pba

Internet of Behavior (IoB) and Explainable AI Systems for Influencing IoT Behavior [article]

Haya Elayan and Moayad Aloqaily and Mohsen Guizani
2021 arXiv   pre-print
Therefore, a system based on IoB and XAI has been proposed in a use case scenario of electrical power consumption that aims to influence user consuming behavior to reduce power consumption and cost.  ...  Nowadays, the use of the Internet of Things (IoT), cloud computing, and artificial intelligence (AI) have made it easier to track and change the behavior of users through changing IoT behavior.  ...  The researchers in [12] have proposed to predict power consumption through proposing a framework consisting of data cleaning and model building steps.  ... 
arXiv:2109.07239v1 fatcat:hekoetdcizg2vkgkahn3qhm23e

Energy Management System Based on a Gamified Application for Households

Manuel Avila, Juana Isabel Méndez, Pedro Ponce, Therese Peffer, Alan Meier, Arturo Molina
2021 Energies  
Furthermore, gamification strategies may change energy consumption patterns through energy managers, which are seen as an option to save energy and money.  ...  Cutting-edge energy managers aim to optimize electrical devices in homes, taking into account users' patterns, goals, and needs, by creating energy consumption awareness and helping current change habits  ...  This is possible modeling electrical cases through a network of interconnected agents in order to test stochastic behaviors.  ... 
doi:10.3390/en14123445 fatcat:safznm6wdbc5toiuaxjxe4k334

E-SAVE: Saving energy by smart serves

Swati Soni, Yugyung Lee
2012 2012 IEEE International Symposium on a World of Wireless, Mobile and Multimedia Networks (WoWMoM)  
The Smart Grid holds promise for transforming the behavior of individuals and communities towards a more efficient and greener use of electric power.  ...  There is a growing demand for non-intrusive monitoring of energy consumption behavior at multiple scales and also for autonomous energy saving opportunities for both individuals and communities.  ...  The model is adaptable as it learns the behavior of individuals and usage patterns of devices. As such, upcoming decisions are based on most commonly occurring behavior of the user (Fig. 4) .  ... 
doi:10.1109/wowmom.2012.6263730 dblp:conf/wowmom/SoniL12 fatcat:oclpqfqidjemnkwh5q2gd66mca

Abnormal Detection of Electricity Consumption of User Based on Particle Swarm Optimization and Long Short Term Memory With the Attention Mechanism

Jiahao Bian, Lei Wang, Rafal Scherer, Marcin Wozniak, Pengchao Zhang, Wei Wei
2021 IEEE Access  
Aiming at users' abnormal electricity consumption behavior, this paper proposes a model based on particle swarm optimization and long-short term memory with the attention mechanism (PSO-Attention-LSTM)  ...  In the process of power transmission and distribution, non-technical losses are usually caused by users' abnormal power consumption behavior.  ...  In this paper, anomaly detection is carried out through user electricity consumption.  ... 
doi:10.1109/access.2021.3062675 fatcat:zzmwzim27nah7it4xuoxaasrbm

Classification of Electricity Consumption Behavior Based on Improved K-Means and LSTM

Hua Li, Bo Hu, Yubo Liu, Bo Yang, Xuefang Liu, Guangdi Li, Zhenyu Wang, Bowen Zhou
2021 Applied Sciences  
The features can be labeled to train the deep neural network to judge the electricity consumption behavior of new users.  ...  , but also establishes a mapping relationship between unlabeled electricity consumption behavior characteristics and user types.  ...  Acknowledgments: The authors would like to acknowledge State Grid Liaoning Electric Power Company for providing the confidential and unpublished data.  ... 
doi:10.3390/app11167625 fatcat:77vq2xdhajfs5blyvsozdqawpa

Power User Profile under Multi-source Heterogeneous Data Fusion in Smart Grid

BAO-XIAN GUO, YING XU, REN-JIE LI, XING-XIONG ZHU
2018 DEStech Transactions on Computer Science and Engineering  
established a unified model of heterogeneous data fusion based on user Profiles, and fuses heterogeneous user data through user Profile technology, and so as to achieve the purpose of strengthening centralized  ...  In order to better understand the usage and distribution of power users, analyze the characteristics of power users' big data, and then realize the precise positioning of power users, in this paper, we  ...  This can help the electricity sales user understand the user's electricity consumption patterns and consumption habits.  ... 
doi:10.12783/dtcse/ceic2018/24566 fatcat:3g5i3i4oljcuhbulwddlw3gpmq

Abnormal electricity detection with hybrid deep neural network model

Jie Liu, Xiang Cao, Diangang Wang, Kejia Pan, Cheng Zhang, Xin Wang, Nader Asnafi
2018 MATEC Web of Conferences  
This paper tackles a new challenge in abnormal electricity detection: how to promptly detect stealing electricity behavior by a large-scale data from power users.  ...  Finally, a hybrid deep neural network detection model is built by combining with the power consumption gradient model and the line-losing model, which can quickly pin down to the abnormal electricity users  ...  of abnormal electrical behavior through the calculation of the growth rate of line loss rate.  ... 
doi:10.1051/matecconf/201818903001 fatcat:pn6n67rojbgynnsr33juvpszqa

A method of electricity consumption behaviour clustering and pricing packages based on data mining

2020 Information Systems and Signal Processing Journal  
The experimental result shows that the subdivision of electricity consumption behavior can realize the effective personalized electricity package recommendation service for users and improve the power  ...  Finally, recommend the most suitable electricity sales plan to the target users.  ...  Literature [3] [4] constructed the model of users' choice of electricity selling companies through psychological methods, and studied how to guide users to actively adjust their choice behavior through  ... 
doi:10.23977/isspj.2020.51004 fatcat:xwwojoic3rdanmvzsuw6kw327q

Nontechnical Loss Detection using Neural Architecture Search and Outlier Detection

Ke Fei, Qi Li, Can Cui, Xue chen, Xinxin Xu, Benshan Xue, Weifeng Cai, P. Siano, Q. Li
2021 E3S Web of Conferences  
This article analyses the features of electricity consumption, current, voltage and opening records under various electricity theft modes and proposes a new simulation method for electricity theft users  ...  Finally, the effectiveness and accuracy of the electricity theft detection method based on NAS model and outlier detection are verified through an industrial case study.  ...  Acknowledgement The Authors are grateful to State Grid Shang Qiu Electric Power Company for their support for this work.  ... 
doi:10.1051/e3sconf/202125601025 fatcat:u3q3ul4wh5hgtbgo7f4jd7t3ka
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