使用机器学习的多层堆组合对电站负载损失的分类预测
Bathandekile M Boshoma1, Oluwole S Akinola1, Peter Olukanmi1,2
1Johannesburg Business School, University of Johannesburg, Johannesburg, South Africa.
Frontiers in artificial intelligence
|August 25, 2025
概括
我们使用机器学习开发了一种可解释的多层堆, 这种模型通过准确地识别不平衡数据集中的潜在问题来提高可靠性.
科学领域:
- 工程
- 计算机科学
背景情况:
- 电站的负载损失显著降低了电站的可靠性,并可能导致故障.
- 准确预测这些故障对于有效的决策和维护至关重要.
研究的目的:
- 开发和评估用于预测因负载损失造成的发电站故障的机器学习模型.
- 在不平衡的数据集上确定最佳分类器组合以提高预测性能.
主要方法:
- 使用了六个电站的五年数据集.
- 六个机器学习分类器进行了实验,使用类权重来解决数据不平衡.
- 使用随机搜索与交叉验证进行参数优化.
- 开发和评估了一个可解释的多层堆组合模型.
主要成果:
- 可以解释的多层堆组合的表现优于单个模型.
- 从第二层排除高斯天真贝叶斯通过减少虚假负数来提高性能.
- 在不平衡的数据评估中,平衡精度,ROC和PR AUC比标准指标更可靠.
结论:
- 开发的组合模型有效地预测电站因负载损失而发生的故障.
- 通过删除表现不佳的分类器来优化组合组合,从而提高预测准确性.
- 适当的评估指标对于评估不平衡数据集的模型性能至关重要.
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