Clean Technology (청정기술)

The Korean Society of Clean Technology (한국청정기술학회)
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Research on Forecasting Framework for System Marginal Price based on Deep Recurrent Neural Networks and Statistical Analysis Models

  • Kim, Taehyun (Department of Chemical Engineering, Gyeongsang National University) ;
  • Lee, Yoonjae (Department of Chemical Engineering, Gyeongsang National University) ;
  • Hwangbo, Soonho (Department of Chemical Engineering, Gyeongsang National University)
  • Received : 2022.04.11
  • Accepted : 2022.05.20
  • Published : 2022.06.30
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Abstract

Electricity has become a factor that dramatically affects the market economy. The day-ahead system marginal price determines electricity prices, and system marginal price forecasting is critical in maintaining energy management systems. There have been several studies using mathematics and machine learning models to forecast the system marginal price, but few studies have been conducted to develop, compare, and analyze various machine learning and deep learning models based on a data-driven framework. Therefore, in this study, different machine learning algorithms (i.e., autoregressive-based models such as the autoregressive integrated moving average model) and deep learning networks (i.e., recurrent neural network-based models such as the long short-term memory and gated recurrent unit model) are considered and integrated evaluation metrics including a forecasting test and information criteria are proposed to discern the optimal forecasting model. A case study of South Korea using long-term time-series system marginal price data from 2016 to 2021 was applied to the developed framework. The results of the study indicate that the autoregressive integrated moving average model (R-squared score: 0.97) and the gated recurrent unit model (R-squared score: 0.94) are appropriate for system marginal price forecasting. This study is expected to contribute significantly to energy management systems and the suggested framework can be explicitly applied for renewable energy networks.

Keywords

Acknowledgement

This work was supported by the projects NRF-2021R1F1A1059919 and NRF-2020M1A2A2080858 through the National Research Foundation of Korea (NRF) and the Ministry of Science, ICT & Future Planning.

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