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Hybrid Forecasting Model for Short-Term Electricity Market Prices with Renewable Integration

Author

Listed:
  • Gerardo J. Osório

    (Center for Mechanical and Aerospace Science and Technologies, University of Beira Interior, 6201-001 Covilhã, Portugal)

  • Mohamed Lotfi

    (INESC TEC, 4200-465 Porto, Portugal
    Faculty of Engineering, University of Porto, 4200-465 Porto, Portugal)

  • Miadreza Shafie-khah

    (INESC TEC, 4200-465 Porto, Portugal)

  • Vasco M. A. Campos

    (Faculty of Engineering, University of Porto, 4200-465 Porto, Portugal)

  • João P. S. Catalão

    (INESC TEC, 4200-465 Porto, Portugal
    Faculty of Engineering, University of Porto, 4200-465 Porto, Portugal)

Abstract

In recent years, there have been notable commitments and obligations by the electricity sector for more sustainable generation and delivery processes to reduce the environmental footprint. However, there is still a long way to go to achieve necessary sustainability goals while ensuring standards of robustness and the quality of power grids. One of the main challenges hindering this progress are uncertainties and stochasticity associated with the electricity sector and especially renewable generation. In this paradigm shift, forecasting tools are indispensable, and their utilization can significantly improve system operation and minimize costs associated with all related activities. Thus, forecasting tools have an essential key role in all decision-making stages. In this work, a hybrid probabilistic forecasting model (HPFM) was developed for short-term electricity market prices (EMP) combining wavelet transforms (WT), hybrid particle swarm optimization (DEEPSO), adaptive neuro-fuzzy inference system (ANFIS), and Monte Carlo simulation (MCS). The proposed hybrid probabilistic forecasting model (HPFM) was tested and validated with real data from the Spanish and Pennsylvania-New Jersey-Maryland (PJM) markets. The proposed model exhibited favorable results and performance in comparison with previously published work considering electricity market prices (EMP) data, which is notable.

Suggested Citation

  • Gerardo J. Osório & Mohamed Lotfi & Miadreza Shafie-khah & Vasco M. A. Campos & João P. S. Catalão, 2018. "Hybrid Forecasting Model for Short-Term Electricity Market Prices with Renewable Integration," Sustainability, MDPI, vol. 11(1), pages 1-15, December.
    • Handle: RePEc:gam:jsusta:v:11:y:2018:i:1:p:57-:d:192550
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    References listed on IDEAS

    as
    1. Nowotarski, Jakub & Weron, Rafał, 2018. "Recent advances in electricity price forecasting: A review of probabilistic forecasting," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P1), pages 1548-1568.
    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.
    Full references (including those not matched with items on IDEAS)

    Citations

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    Cited by:

    1. Sevda Kuşkaya & Faik Bilgili, 2026. "Forecasting electricity price index with machine learning models and strategies," Quality & Quantity: International Journal of Methodology, Springer, vol. 60(1), pages 2651-2678, February.
    2. Wang, Yong & Fan, Neng & Wen, Shixiong & Kuang, Wenyu & Yang, Zhongsen & Xiao, Wenlian & Li, Hong-Li & Narayanan, Govindasami & Sapnken, Flavian Emmanuel, 2025. "A novel structural adaptive discrete grey Euler model and its application in clean energy production and consumption," Energy, Elsevier, vol. 323(C).
    3. Ciaran O'Connor & Mohamed Bahloul & Steven Prestwich & Andrea Visentin, 2025. "The Evolution of Probabilistic Price Forecasting Techniques: A Review of the Day-Ahead, Intra-Day, and Balancing Markets," Papers 2511.05523, arXiv.org.
    4. Mohamed Lotfi & Mohammad Javadi & Gerardo J. Osório & Cláudio Monteiro & João P. S. Catalão, 2020. "A Novel Ensemble Algorithm for Solar Power Forecasting Based on Kernel Density Estimation," Energies, MDPI, vol. 13(1), pages 1-19, January.
    5. Zhang, Jinliang & Tan, Zhongfu & Wei, Yiming, 2020. "An adaptive hybrid model for short term electricity price forecasting," Applied Energy, Elsevier, vol. 258(C).
    6. Ghimire, Sujan & Deo, Ravinesh C. & Casillas-Pérez, David & Salcedo-Sanz, Sancho, 2024. "Two-step deep learning framework with error compensation technique for short-term, half-hourly electricity price forecasting," Applied Energy, Elsevier, vol. 353(PA).
    7. Chao-Rong Chen & Faouzi Brice Ouedraogo & Yu-Ming Chang & Devita Ayu Larasati & Shih-Wei Tan, 2021. "Hour-Ahead Photovoltaic Output Forecasting Using Wavelet-ANFIS," Mathematics, MDPI, vol. 9(19), pages 1-14, October.
    8. Ghimire, Sujan & Deo, Ravinesh C. & Casillas-Pérez, David & Sharma, Ekta & Salcedo-Sanz, Sancho & Barua, Prabal Datta & Rajendra Acharya, U., 2024. "Half-hourly electricity price prediction with a hybrid convolution neural network-random vector functional link deep learning approach," Applied Energy, Elsevier, vol. 374(C).

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