Abstract:

[Objective] To estimate the uncertainty of user-level short-term load in the night economy, this paper designs a user-level short-term load probability prediction method based on the light gradient boosting machine (LightGBM) and kernel density estimation (KDE). [Methods] First, the LightGBM is applied to model the historical load and influencing factors (such as weather, day type, etc.). Then, the trained LightGBM-based model predicts the user's future load. The predictions produced by the trees in LightGBM are used as input to the kernel density estimation (KDE) to generate the probabilistic density and prediction intervals of the user's future load. [Conclusions] The experimental results on the actual load of multiple night economic users verify the performance of this framework.

Key words: user-level load forecasting, probabilistic load forecasting, Light Gradient Boosting Machine, gradient boost decision tree, kernel density estimation