SavedAdd your journal

Journals / Konya Journal of Engineering Sciences / 2023 / Cilt: 11 Sayı: 4

A TWO STAGE MODEL FOR DAY-AHEAD ELECTRICITY PRICE FORECASTING: INTEGRATING EMPIRICAL MODE DECOMPOSITION AND CATBOOST ALGORITHM

Ceyhun YILDIZ
Pages
1047–1060
Publication Date
2023-12-01
DOI
—
🏛️Bibliographic metadata and full text access for this article are provided via DergiPark.
DergiPark Record ↗
✨

AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

The results of this study indicated that the proposed model outperformed the benchmark models with the lowest root mean squared error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE), and correlation coefficient (R) values of 8.3282%, 5.2210%, 6.9675%, and 86.2256%, respectively.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:Electricity price forecasting is crucial for the secure and cost-effective operation of electrical power systems.
📊
Key Findings & Data:In this study, a two-stage forecasting model was proposed in order to accurately predict day-ahead electricity prices.
💡
Conclusion & Impact:The results of this study indicated that the proposed model outperformed the benchmark models with the lowest root mean squared error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE), and correlation coefficient (R) values of 8.3282%, 5.2210%, 6.9675%, and 86.2256%, respectively.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:CatBoost regression · Electricity price forecasting · Empirical mode decomposition

Abstract

Electricity price forecasting is crucial for the secure and cost-effective operation of electrical power systems. However, the uncertain and volatile nature of electricity prices makes the electricity price forecasting process more challenging. In this study, a two-stage forecasting model was proposed in order to accurately predict day-ahead electricity prices. Historical natural gas prices, electricity load forecasts, and historical electricity price values were used as the forecasting model inputs. The historical electricity and natural gas price data were decomposed in the first stage to extract more deep features. The empirical mode decomposition (EMD) algorithm was employed for the efficient decomposition process. In the second stage, the categorical boosting (CatBoost) algorithm was proposed to forecast day-ahead electricity prices accurately. To validate the effectiveness of the proposed forecasting model, a case study was conducted using the dataset from the Turkish electricity market. The proposed model results were compared with benchmark machine learning algorithms. The results of this study indicated that the proposed model outperformed the benchmark models with the lowest root mean squared error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE), and correlation coefficient (R) values of 8.3282%, 5.2210%, 6.9675%, and 86.2256%, respectively.

🔖

Cite This Article

YILDIZ, C. (2023). A TWO STAGE MODEL FOR DAY-AHEAD ELECTRICITY PRICE FORECASTING: INTEGRATING EMPIRICAL MODE DECOMPOSITION AND CATBOOST ALGORITHM. Konya Journal of Engineering Sciences, Cilt: 11 Sayı: 4, 1047–1060. https://ajindex.com/articles/1019768

📄 Full Text PDF Viewer

📄

PDF görüntüleyici hazırlanıyor...

References (3)

  1. J. Lago, G. Marcjasz, B. De Schutter, and R. Weron, “Forecasting day-ahead electricity prices: A review of state-of-the-art algorithms, best practices and an open-access benchmark,” Appl. Energy, vol. 293, no. December 2020, p. 116983, 2021, doi: 10.1016/j.apenergy.2021.116983. R. Weron, “Electricity price forecasting: A review of the state-of-the-art with a look into the future,” Int. J. Forecast., vol. 30, no. 4, pp. 1030–1081, 2014, doi: 10.1016/j.ijforecast.2014.08.008. L. Tschora, E. Pierre, M. Plantevit, and C. Robardet, “Electricity price forecasting on the day-ahead market using machine learning,” Appl. Energy, vol. 313, no. March, p. 118752, 2022, doi: 10.1016/j.apenergy.2022.118752. P. Wang et al., “An Online Electricity Market Price Forecasting Method Via Random Forest,” IEEE Trans. Ind. Appl., vol. 58, no. 6, pp. 7013–7021, 2022.
  2. C. Xiao, D. Sutanto, K. M. Muttaqi, M. Zhang, K. Meng, and Z. Y. Dong, “Online sequential extreme learning machine algorithm for better predispatch electricity price forecasting grids,” IEEE Trans. Ind. Appl., vol. 57, no. 2, pp. 1860–1871, 2021.
  3. I. Shah, H. Bibi, S. Ali, L. Wang, and Z. Yue, “Forecasting one-day-ahead electricity prices for italian electricity market using parametric and nonparametric approaches,” IEEE Access, vol. 8, pp. 123104–123113, 2020. H. Yang and K. R. Schell, “GHTnet: Tri-Branch deep learning network for real-time electricity price forecasting,” Energy, vol. 238, p. 122052, 2022, doi: 10.1016/j.energy.2021.122052. H. Yang and K. R. Schell, “International Journal of Electrical Power and Energy Systems QCAE: A quadruple branch CNN autoencoder for real-time electricity price forecasting,” Int. J. Electr. Power Energy Syst., vol. 141, no. April, p. 108092, 2022, doi: 10.1016/j.ijepes.2022.108092. J. Lago, F. De Ridder, and B. De Schutter, “Forecasting spot electricity prices: Deep learning approaches and empirical comparison of traditional algorithms,” Appl. Energy, vol. 221, no. April, pp. 386–405, 2018, doi: 10.1016/j.apenergy.2018.02.069. S. Luo and Y. Weng, “A two-stage supervised learning approach for electricity price forecasting by leveraging different data sources,” Appl. Energy, vol. 242, no. February, pp. 1497–1512, 2019, doi: 10.1016/j.apenergy.2019.03.129. T. Zhang, Z. Tang, J. Wu, X. Du, and K. Chen, “Short term electricity price forecasting using a new hybrid model based on two-layer decomposition technique and ensemble learning,” Electr. Power Syst. Res., vol. 205, no. July 2021