journal article·2023-05-01

RETRACTED: AI-Based Detection of Power Consumption Behavior of People in a Smart City

Dongmei Yang, Yueyuan Zhang, Hongming He

Journal of Testing and Evaluation· Volume 51· Issue 3
This record is marked as retracted by the source. Review the publisher’s notice before using its findings.
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Abstract

ABSTRACT Following an investigation undertaken by the publisher, we have determined that this paper was accepted on the basis of a compromised peer review process. We hereby retract the paper. The corresponding author has been notified of the retraction. The retraction statement can be found here: https://doi.org/10.1520/JTE20269996. In the past, the power consumption behavior of customers was not considered, so the research of power consumption behavior based on artificial intelligence technology was put forward. Combined with artificial intelligence technology, the structure block diagram of electricity consumption analysis is constructed to complete multivariable and multidimensional data storage and management. The initial cluster center is randomly selected from the sample data set to determine the distance between the sample point and the cluster center. Based on the reasonable evaluation index of electric power behavior, the analysis model of electric power consumption behavior is established. The data set is divided by similarity measure, the fuzzy weighted index is initialized, the membership matrix is initialized and updated, the objective function is calculated, and the types of data objects are determined according to the calculation results. Based on this, the user screening process for abnormal power consumption is designed. The k-means clustering algorithm is used to design the diagnostic analysis process, and the closed-loop diagnostic mechanism for abnormal electricity consumption such as stealing and leakage is studied. Experimental results show that the proposed method can effectively distinguish the normal operation mode from the abnormal operation mode of power consumers.
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Research context

OpenAlex topics: Energy Load and Power Forecasting · Air Quality Monitoring and Forecasting · Advanced Technologies in Various Fields

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

Affiliations

Power Marketing Service and Operation Management Center, Inner Mongolia Power (Group) Co., Ltd 1 , No. 28 of Xilinnan Rd., Hohhot 010000 ,

Power Marketing Service and Operation Management Center, Inner Mongolia Power (Group) Co., Ltd 2 , No. 28 of Xilinnan Rd., Hohhot 010000 ,

References (15)

Show source-provided references
1. Unlocking Energy Flexibility of Municipal Wastewater Aeration Using Predictive Control to Exploit Price Differences in Power Markets10.1016/j.apenergy.2020.115965 ↗
2. Commercial Load Profile Sensitivity Analysis to Electricity Tariffs and Battery Characteristics10.1109/TIA.2019.2959000 ↗
3. Seasonal Variability of Price Elasticity of Demand of Households Using Zonal Tariffs and Its Impact on Hourly Load of the Power System10.1016/j.energy.2020.117175 ↗
4. Prediction of Electrical Energy Consumption Based on Machine Learning Technique10.1007/s00202-020-01126-z ↗
5. Mid-term Electricity Load Prediction Using CNN and Bi-LSTM10.1007/s11227-021-03686-8 ↗
6. Spatiotemporal Analysis of Extracted Groundwater Volumes Estimated from Electricity Consumption10.1111/gwat.13008 ↗
7. Trading Power Instead of Energy in Day-Ahead Electricity Markets10.1016/j.apenergy.2018.09.205 ↗
8. A Multi-time-scale Power Prediction Model of Hydropower Station Considering Multiple Uncertainties10.1016/j.scitotenv.2019.04.430 ↗
9. Environmental Degradation: The Role of Electricity Consumption, Economic Growth and Globalisation10.1016/j.jenvman.2019.109742 ↗
10. Prediction of Lymph Node Metastasis in Early Gastric Cancer Using Artificial Intelligence Technology10.1200/JCO.2020.38.4_suppl.289 ↗
11. Regulatory Considerations for Artificial Intelligence Technologies in GI Endoscopy10.1016/j.gie.2020.05.040 ↗
12. Rapid Identification of Rainstorm Disaster Risks Based on an Artificial Intelligence Technology Using the 2DPCA Method10.1016/j.atmosres.2019.05.006 ↗
13. Artificial Intelligence in Innovation: How to Spot Emerging Trends and Technologies10.1109/TEM.2020.2989214 ↗
14. Artificial Intelligence as an Emerging Technology in the Current Care of Neurological Disorders10.1007/s00415-019-09518-3 ↗
15. Demand-Side Management Strategy in Stand-Alone Hybrid Photovoltaic Systems with Real-Time Simulation of Stochastic Electricity Consumption Behavior10.1016/j.apenergy.2019.113530 ↗