機械学習による多層スタック・アンサンブルによる発電所の負荷損失の分類予測
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
まとめ
機械学習を用いた 説明可能な多層スタック・アンサンブルを開発し 負荷の減少による 発電所の故障を予測しました このモデルは,不均衡なデータセットの潜在的な問題を正確に特定することで,信頼性を向上させます.
科学分野:
- エンジニアリング
- コンピュータ科学
背景:
- 発電所の負荷損失は発電所の信頼性を大幅に低下させ,故障を引き起こす可能性があります.
- これらの故障を正確に予測することは,効果的な意思決定とメンテナンスに不可欠です.
研究 の 目的:
- 負荷損失による発電所の故障を予測するための機械学習モデルを開発し評価する.
- 不均衡なデータセットの予測パフォーマンスを改善するための最適の分類器の組み合わせを特定する.
主な方法:
- 6つの発電所の5年間のデータセットが利用されました.
- 6つの機械学習分類器を実験し,データ不均衡に対処するためにクラス重みを採用しました.
- パラメータの最適化のために,クロス検証によるランダム化検索が使用された.
- 説明可能な多層スタックアンサンブルモデルが開発され,評価された.
主要な成果:
- Explainable Multilayer Stack Ensembleは個々のモデルを上回りました
- Gaussian Naïve Bayesを第2層から除外すると,偽ネガティブを減らすことでパフォーマンスを改善しました.
- バランスのとれた精度,ROC,PR AUCは標準的な指標よりも不均衡なデータの評価においてより信頼性が高いことが示された.
結論:
- 開発されたアンサンブルモデルは,負荷損失による発電所の故障を効果的に予測します.
- 低性能の分類器を除去することでアンサンブル組成を最適化すると,予測精度が向上します.
- 不均衡なデータセットでのモデルのパフォーマンスを評価するには,適切な評価メトリックが不可欠です.
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