Scholarlink

<p>With the advancement of power grid informatization, the utilization of Internet of Things (IoT) technology has become increasingly widespread. This study primarily focuses on monitoring the operational status of the power grid, specifically targeting load status. Environmental and load data were collected using IoT technology. Then, the least squares support vector machine (LSSVM) was selected as the predictive model. An improved beluga whale optimization (IBWO) algorithm was developed to optimize the parameters of the LSSVM model, resulting in an IBWO-LSSVM model for load status prediction. An experiment was conducted using the collected data. It was found that the load state predictions generated by the IBWO-LSSVM model closely matched the actual values. The mean absolute error achieved was 30.56&#xa0;MW, the root mean square error was 38.45&#xa0;MW, and the mean absolute percentage error was 2.12%. These results also surpassed those of several other prediction methods, demonstrating the effectiveness of this model in load state prediction and its capability to enhance load state data monitoring. The findings validate the efficacy of the IBWO-LSSVM model and highlight its potential application in actual data monitoring of grid operational status.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Research on Data Monitoring of Power Grid Operation Status Based on Internet of Things Technology

  • 期刊论文

  • 发表日期：

    2026年2月12日

查看全文
  • Qiang Li,
  • Weijian Zhang,
  • Weizhi Lu,
  • Yuan Liu,
  • Di Cai

摘要

With the advancement of power grid informatization, the utilization of Internet of Things (IoT) technology has become increasingly widespread. This study primarily focuses on monitoring the operational status of the power grid, specifically targeting load status. Environmental and load data were collected using IoT technology. Then, the least squares support vector machine (LSSVM) was selected as the predictive model. An improved beluga whale optimization (IBWO) algorithm was developed to optimize the parameters of the LSSVM model, resulting in an IBWO-LSSVM model for load status prediction. An experiment was conducted using the collected data. It was found that the load state predictions generated by the IBWO-LSSVM model closely matched the actual values. The mean absolute error achieved was 30.56 MW, the root mean square error was 38.45 MW, and the mean absolute percentage error was 2.12%. These results also surpassed those of several other prediction methods, demonstrating the effectiveness of this model in load state prediction and its capability to enhance load state data monitoring. The findings validate the efficacy of the IBWO-LSSVM model and highlight its potential application in actual data monitoring of grid operational status.