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Found 3 papers, 1 papers with code
An Explainable Framework for Machine learning-Based Reactive Power Optimization of Distribution Network
To reduce the heavy computational burden of reactive power optimization of distribution networks, machine learning models are receiving increasing attention.
Benchmarks and Custom Package for Electrical Load Forecasting
Based on this, we conducted extensive experiments on load data at different levels, providing a reference for researchers to compare different load forecasting models.
DiffLoad: Uncertainty Quantification in Load Forecasting with Diffusion Model
The uncertainties in load forecasting can be divided into two types: epistemic uncertainty and aleatoric uncertainty.
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