| Title | Predicting Smart Cities? Electricity Demands Using K-Means Clustering Algorithm in Smart Grid |
|---|---|
| ID_Doc | 41548 |
| Authors | Wang, SR; Song, AF; Qian, YF |
| Title | Predicting Smart Cities? Electricity Demands Using K-Means Clustering Algorithm in Smart Grid |
| Year | 2023 |
| Published | Computer Science And Information Systems, 20, 2 |
| Abstract | This work aims to perform the unified management of various departments engaged in smart city construction by big data, establish a synthetic data collection and sharing system, and provide fast and convenient big data services for smart applications in various fields. A new electricity demand prediction model based on back propagation neural network (BPNN) is proposed for China's electricity industry according to the smart city's big data characteristics. This model integrates meteorological, geographic, demographic, corporate, and economic information to form a big intelligent database. Moreover, the K-means clustering algorithm mines and analyzes the data to optimize the power consumers' information. The BPNN model is used to extract features for prediction. Users with weak daily correlation obtained by the K-means clustering algorithm only input the historical load of adjacent moments into the BPNN model for prediction. Finally, the electricity market is evaluated by exploring the data correlation in-depth to verify the proposed model's effectiveness. The results indicate that the K-mean algorithm can significantly improve the segmentation accuracy of power consumers, with a maximum accuracy of 85.25% and average accuracy of 83.72%. The electricity consumption of different regions is separated, and the electricity consumption is classified. The electricity demand prediction model can enhance prediction accuracy, with an average error rate of 3.27%. The model's training significantly speeds up by adding the momentum factor, and the average error rate is 2.13%. Therefore, the electricity demand prediction model achieves high accuracy and training efficiency. The findings can provide a theoretical and practical foundation for electricity demand prediction, personalized marketing, and the development planning of the power industry. |
| http://www.doiserbia.nb.rs/ft.aspx?id=1820-02142300013W |
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