IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v17y2024i8p1864-d1375168.html
   My bibliography  Save this article

Methodology for Multi-Step Forecasting of Electricity Spot Prices Based on Neural Networks Applied to the Brazilian Energy Market

Author

Listed:
  • Marianna B. B. Dias

    (Center of Electrical Engineering and Informatics, Federal University of Campina Grande, Campina Grande 58429900, Brazil)

  • George R. S. Lira

    (Department of Electrical Engineering, Federal University of Campina Grande, Campina Grande 58429900, Brazil)

  • Victor M. E. Freire

    (Center of Electrical Engineering and Informatics, Federal University of Campina Grande, Campina Grande 58429900, Brazil)

Abstract

Forecasting electricity spot prices holds paramount significance for informed decision-making among energy market stakeholders. This study introduces a methodology utilizing a multilayer perceptron (MLP) neural network for multivariate electricity spot price prediction. The model underwent a feature selection process to identify the most influential predictors. In the validation phase, the model’s performance was evaluated using key metrics, including trend accuracy percentage index (TAPI), normalized root mean squared error (NRMSE), and mean absolute percentage error (MAPE). The results were obtained for a four-week forecast horizon in order to serve as an auxiliary tool to facilitate decision-making processes in the short-term energy market. The relevance of short-term electricity spot price forecasting lies in its direct impact on pricing strategies during energy contract negotiations, which allows for the making of assertive decisions in the energy trading landscape.

Suggested Citation

  • Marianna B. B. Dias & George R. S. Lira & Victor M. E. Freire, 2024. "Methodology for Multi-Step Forecasting of Electricity Spot Prices Based on Neural Networks Applied to the Brazilian Energy Market," Energies, MDPI, vol. 17(8), pages 1-14, April.
    • Handle: RePEc:gam:jeners:v:17:y:2024:i:8:p:1864-:d:1375168
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/17/8/1864/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/17/8/1864/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Wang, Deyun & Luo, Hongyuan & Grunder, Olivier & Lin, Yanbing & Guo, Haixiang, 2017. "Multi-step ahead electricity price forecasting using a hybrid model based on two-layer decomposition technique and BP neural network optimized by firefly algorithm," Applied Energy, Elsevier, vol. 190(C), pages 390-407.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Ahmed, R. & Sreeram, V. & Mishra, Y. & Arif, M.D., 2020. "A review and evaluation of the state-of-the-art in PV solar power forecasting: Techniques and optimization," Renewable and Sustainable Energy Reviews, Elsevier, vol. 124(C).
    2. Yajing Gao & Xiaojie Zhou & Jiafeng Ren & Zheng Zhao & Fushen Xue, 2018. "Electricity Purchase Optimization Decision Based on Data Mining and Bayesian Game," Energies, MDPI, vol. 11(5), pages 1-19, April.
    3. Zhang, Hong & Nguyen, Hoang & Bui, Xuan-Nam & Pradhan, Biswajeet & Mai, Ngoc-Luan & Vu, Diep-Anh, 2021. "Proposing two novel hybrid intelligence models for forecasting copper price based on extreme learning machine and meta-heuristic algorithms," Resources Policy, Elsevier, vol. 73(C).
    4. Zhao, Jing & Yang, Zilan & Shi, Linyu & Liu, Dehan & Li, Haonan & Mi, Yumiao & Wang, Hongbin & Feng, Meili & Hutagaol, Timothy Joseph, 2024. "Photovoltaic capacity dynamic tracking model predictive control strategy of air-conditioning systems with consideration of flexible loads," Applied Energy, Elsevier, vol. 356(C).
    5. Khan, Waqas & Walker, Shalika & Zeiler, Wim, 2022. "Improved solar photovoltaic energy generation forecast using deep learning-based ensemble stacking approach," Energy, Elsevier, vol. 240(C).
    6. Deyun Wang & Yanling Liu & Zeng Wu & Hongxue Fu & Yong Shi & Haixiang Guo, 2018. "Scenario Analysis of Natural Gas Consumption in China Based on Wavelet Neural Network Optimized by Particle Swarm Optimization Algorithm," Energies, MDPI, vol. 11(4), pages 1-16, April.
    7. 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.
    8. Dinggao Liu & Zhenpeng Tang & Yi Cai, 2022. "A Hybrid Model for China’s Soybean Spot Price Prediction by Integrating CEEMDAN with Fuzzy Entropy Clustering and CNN-GRU-Attention," Sustainability, MDPI, vol. 14(23), pages 1-22, November.
    9. Micha{l} Narajewski & Florian Ziel, 2020. "Ensemble Forecasting for Intraday Electricity Prices: Simulating Trajectories," Papers 2005.01365, arXiv.org, revised Aug 2020.
    10. Qunli Wu & Huaxing Lin, 2019. "Short-Term Wind Speed Forecasting Based on Hybrid Variational Mode Decomposition and Least Squares Support Vector Machine Optimized by Bat Algorithm Model," Sustainability, MDPI, vol. 11(3), pages 1-18, January.
    11. Jing Bai & Jiahui Wang & Jin Ran & Xingyuan Li & Chuang Tu, 2024. "An Improved Neural Network Algorithm for Energy Consumption Forecasting," Sustainability, MDPI, vol. 16(21), pages 1-19, October.
    12. Wang, Deyun & Yue, Chenqiang & ElAmraoui, Adnen, 2021. "Multi-step-ahead electricity load forecasting using a novel hybrid architecture with decomposition-based error correction strategy," Chaos, Solitons & Fractals, Elsevier, vol. 152(C).
    13. Qin Lu & Jingwen Liao & Kechi Chen & Yanhui Liang & Yu Lin, 2024. "Predicting Natural Gas Prices Based on a Novel Hybrid Model with Variational Mode Decomposition," Computational Economics, Springer;Society for Computational Economics, vol. 63(2), pages 639-678, February.
    14. Tang, Zhenhao & Zhao, Gengnan & Ouyang, Tinghui, 2021. "Two-phase deep learning model for short-term wind direction forecasting," Renewable Energy, Elsevier, vol. 173(C), pages 1005-1016.
    15. Tschora, Léonard & Pierre, Erwan & Plantevit, Marc & Robardet, Céline, 2022. "Electricity price forecasting on the day-ahead market using machine learning," Applied Energy, Elsevier, vol. 313(C).
    16. Yang, Zhongshan & Wang, Jian, 2018. "A hybrid forecasting approach applied in wind speed forecasting based on a data processing strategy and an optimized artificial intelligence algorithm," Energy, Elsevier, vol. 160(C), pages 87-100.
    17. Yang, Haolin & Schell, Kristen R., 2022. "GHTnet: Tri-Branch deep learning network for real-time electricity price forecasting," Energy, Elsevier, vol. 238(PC).
    18. Lago, Jesus & Marcjasz, Grzegorz & De Schutter, Bart & Weron, Rafał, 2021. "Forecasting day-ahead electricity prices: A review of state-of-the-art algorithms, best practices and an open-access benchmark," Applied Energy, Elsevier, vol. 293(C).
    19. Zhu, Yongguang & Xu, Deyi & Cheng, Jinhua & Ali, Saleem Hassan, 2018. "Estimating the impact of China's export policy on tin prices: a mode decomposition counterfactual analysis method," Resources Policy, Elsevier, vol. 59(C), pages 250-264.
    20. Lin, Yu & Lu, Qin & Tan, Bin & Yu, Yuanyuan, 2022. "Forecasting energy prices using a novel hybrid model with variational mode decomposition," Energy, Elsevier, vol. 246(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:17:y:2024:i:8:p:1864-:d:1375168. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.