This work presents parameterized deep quantile regression for short-term probabilistic net-load forecasting at the distribution level. To be precise, we use a Deep Neural Network (DNN) to learn both ...

This paper proposes a practical methodology to generate probabilistic load forecasts by performing quantile regression averaging on a set of sister point forecasts. There are two major benefits of the ...

python machine-learning scikit-learn sklearn jupyter-notebook regression confidence-intervals uncertainty-quantification quantile-regression conformal-prediction prediction-intervals ...

Recent research has focused on distributed quantile regression (dQR) in sensor networks. In distributed sensor networks, where data from different sensors can vary significantly in terms of noise and ...