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Smart grids and machine learning: Uncovering pitfalls and improving the robustness of models
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DOI：10.1016/j.egyr.2026.109483.png)
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Abstract
En 中文
• Identify key errors in smart grid ML, such as flawed preprocessing and evaluation. • Improper scaling, splitting, and sampling cause data leakage and inflated metrics. • Tuning hyperparameters on labeled data breaks the core unsupervised assumption. • Best-practice workflow to mitigate these pitfalls for real-world applicability.
Keywords:
Smart grids
Machine learning
Privacy
Data preprocessing
Unsupervised learning
Journal
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5.1
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658
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0
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