  Short-term load forecasting is a critical element of power systems energy
management systems. In recent years, probabilistic load forecasting (PLF) has
gained increased attention for its ability to provide uncertainty information
that helps to improve the reliability and economics of system operation
performances. This paper proposes a two-stage probabilistic load forecasting
framework by integrating point forecast as a key probabilistic forecasting
feature into PLF. In the first stage, all related features are utilized to
train a point forecast model and also obtain the feature importance. In the
second stage the forecasting model is trained, taking into consideration point
forecast features, as well as selected feature subsets. During the testing
period of the forecast model, the final probabilistic load forecast results are
leveraged to obtain both point forecasting and probabilistic forecasting.
Numerical results obtained from ISO New England demand data demonstrate the
effectiveness of the proposed approach in the hour-ahead load forecasting,
which uses the gradient boosting regression for the point forecasting and
quantile regression neural networks for the probabilistic forecasting.
