Abstract
Electricity price forecasting is important to the
energy companies in planning and decision making. Gaussian
process (GP) regression is a powerful tool for probabilistic fore-
casts of time series data. In this paper, we employ GP regression
for prediction interval (PI) based forecasting of electricity spot
prices. At each hour of the day, a new parameter set is computed
incorporating most recent available electricity price data. We
compare performance of several kernels. Likelihood ratio (LR)
test statistics are used to measure goodness of the out-of-sample
forecasts. Results show that our scheme outperforms other
schemes in literature. In one case, LR statistics are slightly better
for an existing quantile regression averaging (QRA) based scheme
.But QRA scheme employs 12 other forecasting schemes followed
by performing regression on the forecasts by those 12 schemes.
However, our results significantly better than other individual
forecasting schemes such as ARX/SNARX and averaging schemes
such as SIMPLE/LAD.