使用StarNet组合模型提高智能电网的预测可靠性和自动化.
Amit Chhabra1, Sunil K Singh1, Sudhakar Kumar1
1CSE, Chandigarh College of Engineering and Technology, Sector 26, Chandigarh, India.
Scientific reports
|February 19, 2026
概括
星网集体模型 (StarNet Ensemble Model) 通过集体学习来提高智能电网的可靠性,用于自动监测和预测. 这种智能框架提高了电网稳定性和运营效率,以实现稳定的发电.
科学领域:
- 电气工程 电气工程
- 计算机科学 计算机科学
- 人工智能的人工智能
背景情况:
- 智能电网需要高预测可靠性和自动化,以实现稳定的电力分配.
- 现有的方法可能缺乏在各种电网条件下强度和通用性.
研究的目的:
- 引入StarNet合奏模型,这是一个基于Web的智能框架,用于增强智能电网稳定性.
- 开发一个自动化,实时监控和预测系统,用于电网性能.
- 在合成和现实世界的基准数据集上验证模型的有效性.
主要方法:
- 开发了一个基于堆叠的合奏学习框架 (StarNet合奏模型).
- 集成了一种机器学习驱动的图形用户界面 (GUI),用于自动化操作.
- 使用CatBoost,AdaBoost,随机森林,SVM和KNN作为随机森林元模型的基础学习者.
- 员工分层10倍交叉验证,用于严格的模型评估.
主要成果:
- 在合成数据集上实现了99.43%的准确性.
- 在UCI智能电网稳定性数据集上获得了98.94%的准确性.
- 在IEEE 14-Bus测试系统上达到97.83%的准确性.
- 证明了跨数据集传输准确率为95.41%.
结论:
- 星网合奏模型表现出强大的稳定性和强大的概括能力.
- 该框架有效地提高了智能电网的预测可靠性和自动化.
- 该模型为提高智能电网运行稳定性和效率提供了可行的解决方案.
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