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Price forecast in the competitive electricity market by support vector machine

Author

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  • Gao, Ciwei
  • Bompard, Ettore
  • Napoli, Roberto
  • Cheng, Haozhong

Abstract

The electricity market has been widely introduced in many countries all over the world and the study on electricity price forecast technology has drawn a lot of attention. In this paper, with different parameter Ci and εi assigned to each training data, the flexible Ci Support Vector Regression (SVR) model is developed in terms of the particularity of the price forecast in electricity market. For Day Ahead Market (DAM) price forecast, the load, time of use index and index of day type are taken as the major factors to characterize the market price, therefore, they are selected as the inputs for the flexible SVR forecast model. For the long-term price forecast, we take the reserve margin Rm, HHI and the fuel price index as the inputs, since they are the major factors that drive the market price variation in long run. For short-term price forecast, besides the detailed analysis with the young Italian electricity market, the new model is tested on the experimental stage of the Spanish market, the New York market and the New England market. The long-term forecast with the SVR model presented is justified by the forecast with the data from the Long Run Market Simulator (LREMS).

Suggested Citation

  • Gao, Ciwei & Bompard, Ettore & Napoli, Roberto & Cheng, Haozhong, 2007. "Price forecast in the competitive electricity market by support vector machine," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 382(1), pages 98-113.
    • Handle: RePEc:eee:phsmap:v:382:y:2007:i:1:p:98-113
    DOI: 10.1016/j.physa.2007.03.050
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    Citations

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    Cited by:

    1. Meng, Anbo & Wang, Peng & Zhai, Guangsong & Zeng, Cong & Chen, Shun & Yang, Xiaoyi & Yin, Hao, 2022. "Electricity price forecasting with high penetration of renewable energy using attention-based LSTM network trained by crisscross optimization," Energy, Elsevier, vol. 254(PA).
    2. Gao, Ciwei & Bompard, Ettore & Napoli, Roberto & Wan, Qiulan & Zhou, Jian, 2008. "Bidding strategy with forecast technology based on support vector machine in the electricity market," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 387(15), pages 3874-3881.
    3. Jasiński, Tomasz, 2020. "Use of new variables based on air temperature for forecasting day-ahead spot electricity prices using deep neural networks: A new approach," Energy, Elsevier, vol. 213(C).
    4. C. Gao & E. Bompard & R. Napoli & Q. Wan, 2007. "Bidding Strategy with Forecast Technology Based on Support Vector Machine in Electrcity Market," Papers 0709.3710, arXiv.org.
    5. Zhao, Geya & Xue, Minggao & Cheng, Li, 2023. "A new hybrid model for multi-step WTI futures price forecasting based on self-attention mechanism and spatial–temporal graph neural network," Resources Policy, Elsevier, vol. 85(PB).

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