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A Machine Learning-Based Algorithm for Short-Term SMP Forecasting Using 2-Step Method

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Abstract

System marginal price (SMP) forecasting is essential for power generation companies or virtual transaction operators participating in the electricity market. In forecasting SMP, the load data is one of the most important pieces of information. Therefore, this paper presents a 2-Step method for SMP forecasting after load forecasting. This paper performed feature selection using Shapley additional explanations and Pearson correlation coefficient and adjusted the hyperparameters of the forecasting model by Grid search. Through this, the total load and SMP in 2021 were forecasted, and the forecasting results were presented through MAPE (mean absolute percentage error), nMAE (normalized mean absolute error), and nRMSE (normalized root mean square error). This paper used XGBoost for load forecasting and XGBoost, random forest, light gradient boosting machine, and linear regression for SMP forecasting. As a result of forecasting SMP, in the average of 2021, LGBM and RF ensemble models showed the best performance with 3.33% in MAPE. In addition, when the forecasting results were shown by dividing it into 4 seasons, the ensemble model of LGBM and random forest showed the best performance in MAPE except for spring.

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Acknowledgements

This work was supported by the Human Resources Program in Energy Technology of the Korea Institute of Energy Technology Evaluation and Planning (KETEP) and the Ministry of Trade, Industry and Energy (MOTIE) of the Republic of Korea (No.20204010600220). This work was supported in part by the Korea Institute of Energy Technology Evaluation and Planning (KETEP) and the Ministry of Trade, Industry, and Energy (MOTIE), Republic of Korea (No.20194310100060).

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Authors and Affiliations

  1. Department of Electrical and Electronic Engineering, Konkuk University, Seoul, Korea

    Sang Woo Shim, Da Han Lee, Jae Hyung Roh & Jong-Bae Park

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  1. Sang Woo Shim
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  2. Da Han Lee
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Correspondence to Jong-Bae Park.

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Shim, S.W., Lee, D.H., Roh, J.H. et al. A Machine Learning-Based Algorithm for Short-Term SMP Forecasting Using 2-Step Method. J. Electr. Eng. Technol. 18, 1493–1501 (2023). https://doi.org/10.1007/s42835-023-01473-4

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  • DOI: https://doi.org/10.1007/s42835-023-01473-4

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