Short-term Electricity Price Probabilistic Forecasting Based on Support Vector Quantile Regression Optimized by Simulated Annealing Algorithm

Author(s): Hui He*, Rui Zhang, Kaihang Li, Yongjun Jie, Runhai Jiao, Bo Chen

Journal Name: Recent Advances in Electrical & Electronic Engineering
Formerly Recent Patents on Electrical & Electronic Engineering

Volume 14 , Issue 2 , 2021


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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.

Keywords: Electricity price, probabilistic forecasting, support vector quantile regression, simulated annealing algorithm, kernel density estimation, quantile regression.

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Article Details

VOLUME: 14
ISSUE: 2
Year: 2021
Published on: 18 February, 2021
Page: [156 - 170]
Pages: 15
DOI: 10.2174/2352096513999200724174751
Price: $25

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