Author

Wen-Hua Zhang

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Open access Aug 2026

Benchmark-grounded self-supervised contrastive learning for AMI-based distribution grid monitoring

Introduction Self-supervised contrastive learning is effective for time-series representation learning, but its deployment in industrial environments remains challenging because of spatiotemporal heterogeneity, domain-specific noise, and label scarcity. We study these challenges in the smart-grid domain, a representati...

Ling-Hao Zhang, Bo-Wei Yang, Xing-Si Ke et al. · Frontiers in Artificial Inte... · 0 citations

Open access Aug 2026

RBF-SVR Significantly Outperforms Tree-Based Models for Weather-Sensitive Air Conditioning Load Prediction Under Extreme Conditions

Accurate air conditioning (AC) load prediction under extreme weather conditions is critical for power grid stability and energy management. While tree-based ensemble methods such as XGBoost, LightGBM, and Gradient Boosting have become the dominant paradigm in short-term load forecasting, their effectiveness for weather...

Chuan Long, Xin-Ting Yang, Yun-Che Su et al. · Energies · 0 citations