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A Multi-Stage Price Forecasting Model for Day-Ahead Electricity Markets

Author

Listed:
  • Radhakrishnan Angamuthu Chinnathambi

    (Department of Electrical Engineering, University of North Dakota, Grand Forks, ND 58203, USA)

  • Anupam Mukherjee

    (Department of Electrical Engineering, University of North Dakota, Grand Forks, ND 58203, USA)

  • Mitch Campion

    (Department of Electrical Engineering, University of North Dakota, Grand Forks, ND 58203, USA)

  • Hossein Salehfar

    (Department of Electrical Engineering, University of North Dakota, Grand Forks, ND 58203, USA)

  • Timothy M. Hansen

    (Department of Electrical Engineering and Computer Science, South Dakota State University, Brookings, SD 57007, USA)

  • Jeremy Lin

    (Transmission Analytics, 2025 Guadalupe St, Suite 260, Austin, TX 78705, USA)

  • Prakash Ranganathan

    (Department of Electrical Engineering, University of North Dakota, Grand Forks, ND 58203, USA)

Abstract

Forecasting hourly spot prices for real-time electricity markets is a key activity in economic and energy trading operations. This paper proposes a novel two-stage approach that uses a combination of Auto-Regressive Integrated Moving Average (ARIMA) with other forecasting models to improve residual errors in predicting the hourly spot prices. In Stage-1, the day-ahead price is forecasted using ARIMA and then the resulting residuals are fed to another forecasting method in Stage-2. This approach was successfully tested using datasets from the Iberian electricity market with duration periods ranging from one-week to ninety days for variables such as price, load and temperature. A comprehensive set of 17 variables were included in the proposed model to predict the day-ahead electricity price. The Mean Absolute Percentage Error (MAPE) results indicate that ARIMA-GLM combination performs better for longer duration periods, while ARIMA-SVM combination performs better for shorter duration periods.

Suggested Citation

  • Radhakrishnan Angamuthu Chinnathambi & Anupam Mukherjee & Mitch Campion & Hossein Salehfar & Timothy M. Hansen & Jeremy Lin & Prakash Ranganathan, 2018. "A Multi-Stage Price Forecasting Model for Day-Ahead Electricity Markets," Forecasting, MDPI, vol. 1(1), pages 1-21, July.
  • Handle: RePEc:gam:jforec:v:1:y:2018:i:1:p:3-46:d:157666
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    References listed on IDEAS

    as
    1. Bennett, Christopher J. & Stewart, Rodney A. & Lu, Jun Wei, 2014. "Forecasting low voltage distribution network demand profiles using a pattern recognition based expert system," Energy, Elsevier, vol. 67(C), pages 200-212.
    2. Weron, Rafał, 2014. "Electricity price forecasting: A review of the state-of-the-art with a look into the future," International Journal of Forecasting, Elsevier, vol. 30(4), pages 1030-1081.
    3. Vu, D.H. & Muttaqi, K.M. & Agalgaonkar, A.P., 2015. "A variance inflation factor and backward elimination based robust regression model for forecasting monthly electricity demand using climatic variables," Applied Energy, Elsevier, vol. 140(C), pages 385-394.
    4. Yongquan Dong & Zichen Zhang & Wei-Chiang Hong, 2018. "A Hybrid Seasonal Mechanism with a Chaotic Cuckoo Search Algorithm with a Support Vector Regression Model for Electric Load Forecasting," Energies, MDPI, vol. 11(4), pages 1-21, April.
    5. Weron, Rafal & Misiorek, Adam, 2008. "Forecasting spot electricity prices: A comparison of parametric and semiparametric time series models," International Journal of Forecasting, Elsevier, vol. 24(4), pages 744-763.
    6. Claudio Monteiro & L. Alfredo Fernandez-Jimenez & Ignacio J. Ramirez-Rosado, 2015. "Explanatory Information Analysis for Day-Ahead Price Forecasting in the Iberian Electricity Market," Energies, MDPI, vol. 8(9), pages 1-23, September.
    7. Hussain, Anwar & Rahman, Muhammad & Memon, Junaid Alam, 2016. "Forecasting electricity consumption in Pakistan: the way forward," Energy Policy, Elsevier, vol. 90(C), pages 73-80.
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