Algorithm Design and Application of Intelligent Metering Device in Solving the Problem of Changing Households

With the improvement of the intelligent level of power system, the problem of changing households has become a key factor affecting the stability and economic operation of power grid. In order to solve this problem, this paper proposes an intelligent metering algorithm based on neural network, which can effectively identify and deal with abnormal electricity consumption behavior by monitoring and accurately metering user electricity consumption data in real time. Firstly, this paper introduces the basic principle and technology of intelligent metering device, including data acquisition, transmission, processing and storage, and data display and control. The application of these technologies enables intelligent metering devices to monitor users’ electricity consumption data in real time, and quickly discover abnormal changes in electricity consumption behavior through advanced data analysis technology. In the part of algorithm design and implementation, this paper expounds the steps of data preprocessing, feature extraction, model construction, training and optimization in detail. Data preprocessing includes data cleaning, feature extraction and standardization to ensure data quality and consistency. Multi-layer feed-forward neural network (MFNN) is used to build the model, ReLU is used as the activation function, and the model is trained by the mean square error (MSE) loss function and Adam optimization algorithm, so as to realize the prediction and identification of abnormal electricity consumption behavior. The experimental results show that the intelligent metering algorithm based on neural network has achieved remarkable results in solving the problem of changing households. The accuracy of the model is $\mathbf{9 5. 8 \%}$ in the test set, with the average absolute error (MAE) of 5.3 kWh and the root mean square error (RMSE) of 7.1 kWh, which shows good prediction ability and stability. By real-time monitoring and predicting abnormal electricity consumption behavior, intelligent metering device provides a strong guarantee for the stable operation of power system. This research result provides a new idea and method for solving the problem of changing households in current power system.

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Published
2024-08-14
DOI
https://doi.org/10.1109/peeec63877.2024.00102
Primary Topic
Smart Parking Systems Research
Type
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Algorithm Design and Application of Intelligent Metering Device in Solving the Problem of Changing Households

Bo Zhu, Xiaoyu Wei, Xingbo Qiu
Smart Parking Systems Research
article

Algorithm Design and Application of Intelligent Metering Device in Solving the Problem of Changing Households

Bo Zhu, Xiaoyu Wei, Xingbo Qiu
article en

Abstract

With the improvement of the intelligent level of power system, the problem of changing households has become a key factor affecting the stability and economic operation of power grid. In order to solve this problem, this paper proposes an intelligent metering algorithm based on neural network, which can effectively identify and deal with abnormal electricity consumption behavior by monitoring and accurately metering user electricity consumption data in real time. Firstly, this paper introduces the basic principle and technology of intelligent metering device, including data acquisition, transmission, processing and storage, and data display and control. The application of these technologies enables intelligent metering devices to monitor users’ electricity consumption data in real time, and quickly discover abnormal changes in electricity consumption behavior through advanced data analysis technology. In the part of algorithm design and implementation, this paper expounds the steps of data preprocessing, feature extraction, model construction, training and optimization in detail. Data preprocessing includes data cleaning, feature extraction and standardization to ensure data quality and consistency. Multi-layer feed-forward neural network (MFNN) is used to build the model, ReLU is used as the activation function, and the model is trained by the mean square error (MSE) loss function and Adam optimization algorithm, so as to realize the prediction and identification of abnormal electricity consumption behavior. The experimental results show that the intelligent metering algorithm based on neural network has achieved remarkable results in solving the problem of changing households. The accuracy of the model is $\mathbf{9 5. 8 \%}$ in the test set, with the average absolute error (MAE) of 5.3 kWh and the root mean square error (RMSE) of 7.1 kWh, which shows good prediction ability and stability. By real-time monitoring and predicting abnormal electricity consumption behavior, intelligent metering device provides a strong guarantee for the stable operation of power system. This research result provides a new idea and method for solving the problem of changing households in current power system.

China Southern Power Grid (China) (CN)
Openalex Percentile: Top 72%
Smart Parking Systems Research
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