A study on using two-stage multiple support vector machines to forecast medium-term electricity market prices

Jiabei Zhang

doi:10.1117/12.3026885

15 March 2024 A study on using two-stage multiple support vector machines to forecast medium-term electricity market prices

Jiabei Zhang

Author Affiliations +

Proceedings Volume 13075, Second International Conference on Physics, Photonics, and Optical Engineering (ICPPOE 2023); 130752K (2024) https://doi.org/10.1117/12.3026885
Event: Second International Conference on Physics, Photonics, and Optical Engineering (ICPPOE 2023), 2023, Kunming, China

Abstract

Using a two-stage multiple Support Vector Machine (SVM) model as the foundation, this work proposes a unique forecasting approach focused on the medium-term electricity Market-Clearing Price (MCP) forecasting issue. The model aims to efficiently capture the complex nonlinear dynamic features of electricity market prices in order to provide accurate price forecasting information to electricity market participants and decision makers. Firstly, we introduce the core idea of the two-stage SVM, where the first stage SVM is used for the initial fitting of the data, while the second- stage SVM further fine-tunes the model to accommodate price fluctuations under different market conditions. Through this two-stage modelling approach, we are able to better address the non-linear and dynamic nature of electricity market prices. In this study, we pay special attention to two key SVM control parameters: the coefficients of the Gaussian radial basis kernel function (σ and the regularization constant (γ). The selection and optimization of these parameters is a key factor in the success of this model. We propose a method for parameter tuning using test data to ensure optimal performance of the SVM model. By optimizing the parameters, we can minimize the Mean square Absolute Error (MAE) of the SVM model and thus improve its accuracy in price forecasting. By applying it in medium-term electricity market price forecasting, we can better understand market trends and price fluctuations.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Jiabei Zhang "A study on using two-stage multiple support vector machines to forecast medium-term electricity market prices", Proc. SPIE 13075, Second International Conference on Physics, Photonics, and Optical Engineering (ICPPOE 2023), 130752K (15 March 2024); https://doi.org/10.1117/12.3026885

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available

Members: $17.00

Non-members: $21.00 ADD TO CART

PROCEEDINGS
10 PAGES

DOWNLOAD PAPER SAVE TO MY LIBRARY

GET CITATION

RIGHTS & PERMISSIONS

Get copyright permission Get copyright permission on Copyright Marketplace

KEYWORDS

Performance modeling

Data modeling

Support vector machines

Mathematical optimization

Microchannel plates

Machine learning

Education and training

Show All Keywords

Keywords/Phrases

Search In:

Publication Years