Background:
Electricity price forecasting is still a challenging issue as it plays an essential role in balancing
electricity generation and consumption. Probabilistic electricity price forecasting not only provides deterministic price
forecasts but also effectively quantifies the uncertainty of electricity price.
Methods:
This paper introduces a new short-term electricity price forecasting approach called SASVQR, which is based
on support vector quantile regression (SVQR) optimized by simulated annealing algorithm. In this study, SVQR is
employed to obtain the conditional quantiles of the electricity under different quantile points, while the simulated
annealing algorithm is applied to optimize each SVR model. Then the kernel density estimation takes these conditional
quantiles as inputs and generates the probability density functions for future electricity prices.
Result:
The proposed algorithm is assessed in three datasets: the GEFCom 2014, two real electricity price datasets from
the PJM market and the Singapore market. Three popular probabilistic forecasting criteria, namely prediction interval
coverage probability (PICP), prediction interval normalized average width (PINAW), and coverage width-based criterion
(CWC), are utilized to evaluate the numerical experiment results..It shows the promising forecasting performance,
robustness, and effectiveness of SASVQR on different datasets.
Conclusion:
The SASVQR method can effectively forecast the short-term electricity price compared with other methods.
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