Probabilistic load forecast presents more informa-
tion on the possible deviation of forecast than the point forecast.
There are sufficient regression models that can make point
forecasts. An intuitive question can be raised:
Is there a way
to combine the point forecasts to construct a probability or interval
forecast?
In this paper, a bootstrap based ensemble approach is
put forward to construct forecast intervals from multiple point
forecasts. Specifically, multiple point forecasting models are first
trained based on the bootstrap sampled training datasets and
different forecasting models. Then, bootstrap is applied again to
the multiple point forecasts. Finally, the quantiles are estimated
according to the distribution of the sampled point forecasts.
Two common machine learning methods, random forest (RF)
and gradient boosting regression tree (GBRT), are combined
to test the feasibility of the proposed forecasting framework.
Compared with quantile RF (Q-RF) and quantile GBRT (Q-
GBRT), numerical experiments demonstrate its advantage over
Q-RF and Q-GBRT.