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Day-Ahead Spot Market Price Forecast Based on a Hybrid Extreme Learning Machine Technique: A Case Study in China

Author

Listed:
  • Jun Dong

    (Department of Economic Management, North China Electric Power University, Beijing 102206, China)

  • Xihao Dou

    (Department of Economic Management, North China Electric Power University, Beijing 102206, China)

  • Aruhan Bao

    (Department of Economic Management, North China Electric Power University, Beijing 102206, China)

  • Yaoyu Zhang

    (Department of Economic Management, North China Electric Power University, Beijing 102206, China)

  • Dongran Liu

    (Department of Economic Management, North China Electric Power University, Beijing 102206, China)

Abstract

With the deepening of China’s electricity spot market construction, spot market price prediction is the basis for making reasonable quotation strategies. This paper proposes a day-ahead spot market price forecast based on a hybrid extreme learning machine technology. Firstly, the trading center’s information is examined using the Spearman correlation coefficient to eliminate characteristics that have a weak link with the price of power. Secondly, a similar day-screening model with weighted grey correlation degree is constructed based on the grey correlation theory (GRA) to exclude superfluous samples. Thirdly, the regularized limit learning machine (RELM) is tuned using the Marine Predators Algorithm (MPA) to increase RELM parameter accuracy. Finally, the proposed forecasting model is applied to the Shanxi spot market, and other forecasting models and error computation methodologies are compared. The results demonstrate that the model suggested in this paper has a specific forecasting effect for power price forecasting technology.

Suggested Citation

  • Jun Dong & Xihao Dou & Aruhan Bao & Yaoyu Zhang & Dongran Liu, 2022. "Day-Ahead Spot Market Price Forecast Based on a Hybrid Extreme Learning Machine Technique: A Case Study in China," Sustainability, MDPI, vol. 14(13), pages 1-24, June.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:13:p:7767-:d:847918
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    References listed on IDEAS

    as
    1. Brusaferri, Alessandro & Matteucci, Matteo & Portolani, Pietro & Vitali, Andrea, 2019. "Bayesian deep learning based method for probabilistic forecast of day-ahead electricity prices," Applied Energy, Elsevier, vol. 250(C), pages 1158-1175.
    2. Weronika Nitka & Tomasz Serafin & Dimitrios Sotiros, 2021. "Forecasting Electricity Prices: Autoregressive Hybrid Nearest Neighbors (ARHNN) method," WORking papers in Management Science (WORMS) WORMS/21/06, Department of Operations Research and Business Intelligence, Wroclaw University of Science and Technology.
    3. Sachin Kahawala & Daswin De Silva & Seppo Sierla & Damminda Alahakoon & Rashmika Nawaratne & Evgeny Osipov & Andrew Jennings & Valeriy Vyatkin, 2021. "Robust Multi-Step Predictor for Electricity Markets with Real-Time Pricing," Energies, MDPI, vol. 14(14), pages 1-20, July.
    4. Sajjad Khan & Shahzad Aslam & Iqra Mustafa & Sheraz Aslam, 2021. "Short-Term Electricity Price Forecasting by Employing Ensemble Empirical Mode Decomposition and Extreme Learning Machine," Forecasting, MDPI, vol. 3(3), pages 1-18, June.
    5. Lehna, Malte & Scheller, Fabian & Herwartz, Helmut, 2022. "Forecasting day-ahead electricity prices: A comparison of time series and neural network models taking external regressors into account," Energy Economics, Elsevier, vol. 106(C).
    6. Tan, Zhongfu & Zhang, Jinliang & Wang, Jianhui & Xu, Jun, 2010. "Day-ahead electricity price forecasting using wavelet transform combined with ARIMA and GARCH models," Applied Energy, Elsevier, vol. 87(11), pages 3606-3610, November.
    7. Liu, Luyao & Bai, Feifei & Su, Chenyu & Ma, Cuiping & Yan, Ruifeng & Li, Hailong & Sun, Qie & Wennersten, Ronald, 2022. "Forecasting the occurrence of extreme electricity prices using a multivariate logistic regression model," Energy, Elsevier, vol. 247(C).
    8. Shuang Wu & Li He & Zhaolong Zhang & Yu Du, 2021. "Forecast of Short-Term Electricity Price Based on Data Analysis," Mathematical Problems in Engineering, Hindawi, vol. 2021, pages 1-14, February.
    9. Sergio Cantillo-Luna & Ricardo Moreno-Chuquen & Harold R. Chamorro & Jose Miguel Riquelme-Dominguez & Francisco Gonzalez-Longatt, 2022. "Locational Marginal Price Forecasting Using SVR-Based Multi-Output Regression in Electricity Markets," Energies, MDPI, vol. 15(1), pages 1-14, January.
    10. Yang, Wendong & Wang, Jianzhou & Niu, Tong & Du, Pei, 2019. "A hybrid forecasting system based on a dual decomposition strategy and multi-objective optimization for electricity price forecasting," Applied Energy, Elsevier, vol. 235(C), pages 1205-1225.
    11. Westgaard, Sjur & Fleten, Stein-Erik & Negash, Ahlmahz & Botterud, Audun & Bogaard, Katinka & Verling, Trude Haugsvaer, 2021. "Performing price scenario analysis and stress testing using quantile regression: A case study of the Californian electricity market," Energy, Elsevier, vol. 214(C).
    12. Díaz, Guzmán & Coto, José & Gómez-Aleixandre, Javier, 2019. "Prediction and explanation of the formation of the Spanish day-ahead electricity price through machine learning regression," Applied Energy, Elsevier, vol. 239(C), pages 610-625.
    13. Xinyue Zhao & Baoxing Shen & Lin Lin & Daohong Liu & Meng Yan & Gengyin Li, 2022. "Residential Electricity Load Forecasting Based on Fuzzy Cluster Analysis and LSSVM with Optimization by the Fireworks Algorithm," Sustainability, MDPI, vol. 14(3), pages 1-15, January.
    14. Stephen Haben & Julien Caudron & Jake Verma, 2021. "Probabilistic Day-Ahead Wholesale Price Forecast: A Case Study in Great Britain," Forecasting, MDPI, vol. 3(3), pages 1-37, August.
    15. Ankit Kumar Srivastava & Ajay Shekhar Pandey & Rajvikram Madurai Elavarasan & Umashankar Subramaniam & Saad Mekhilef & Lucian Mihet-Popa, 2021. "A Novel Hybrid Feature Selection Method for Day-Ahead Electricity Price Forecasting," Energies, MDPI, vol. 14(24), pages 1-16, December.
    16. Qiao, Weibiao & Yang, Zhe, 2020. "Forecast the electricity price of U.S. using a wavelet transform-based hybrid model," Energy, Elsevier, vol. 193(C).
    17. Faheem Jan & Ismail Shah & Sajid Ali, 2022. "Short-Term Electricity Prices Forecasting Using Functional Time Series Analysis," Energies, MDPI, vol. 15(9), pages 1-15, May.
    18. Ismail Shah & Hasnain Iftikhar & Sajid Ali & Depeng Wang, 2019. "Short-Term Electricity Demand Forecasting Using Components Estimation Technique," Energies, MDPI, vol. 12(13), pages 1-17, July.
    19. Jin Sol Hwang & Jung-Su Kim & Hwachang Song, 2022. "Handling Load Uncertainty during On-Peak Time via Dual ESS and LSTM with Load Data Augmentation," Energies, MDPI, vol. 15(9), pages 1-20, April.
    20. Antoine Ferré & Guillaume de Certaines & Jérôme Cazelles & Tancrède Cohet & Arash Farnoosh & Frédéric Lantz, 2021. "Short-term electricity price forecastingmodels comparative analysis : Machine Learning vs. Econometrics," Working Papers hal-03262208, HAL.
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    2. Jun Dong & Xihao Dou & Dongran Liu & Aruhan Bao & Dongxue Wang & Yunzhou Zhang & Peng Jiang, 2023. "Benefit Sharing of Power Transactions in Distributed Energy Systems with Multiple Participants," Sustainability, MDPI, vol. 15(11), pages 1-23, June.

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