Title:Short-term Electricity Price Probabilistic Forecasting Based on Support Vector Quantile Regression Optimized by Simulated Annealing Algorithm
VOLUME: 14 ISSUE: 2
Author(s):Hui He*, Rui Zhang, Kaihang Li, Yongjun Jie, Runhai Jiao and Bo Chen
Affiliation:School of Control and Computer Engineering, North China Electric Power University, Beijing, School of Control and Computer Engineering, North China Electric Power University, Beijing, School of Control and Computer Engineering, North China Electric Power University, Beijing, School of Control and Computer Engineering, North China Electric Power University, Beijing, School of Control and Computer Engineering, North China Electric Power University, Beijing, China Unicom Big Data Co., Ltd, Beijing100011
Keywords:Electricity price, probabilistic forecasting, support vector quantile regression, simulated annealing algorithm, kernel
density estimation, quantile regression.
Abstract: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.
Results: 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.
