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GHTnet: Tri-Branch deep learning network for real-time electricity price forecasting

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  • Yang, Haolin
  • Schell, Kristen R.

Abstract

A highly accurate electricity price prediction model is of the utmost importance for multiple power systems tasks, such as generation dispatch and bidding. Due to the liberalization of the electricity market, as well as high renewable penetration, the properties of electricity price time series are becoming more stochastic and complex. Traditional statistical methods and machine learning algorithms cannot model such volatile market conditions with high fidelity. In this paper, we propose a data-driven deep learning network (GHTnet) to capture the temporal distribution of real-time price data. A new CNN module, based on GoogLeNet, is developed to capture the high-frequency features of this data, while inclusion of time series summary statistics is shown to improve the forecasting of volatile price spikes. The deep learning model is developed and validated on real-time price time series from 49 generators in the New York Independent System Operator (NYISO), achieving significant performance improvements over that of state-of-the-art benchmark methods, with an average 17.34% improvement in MAPE.

Suggested Citation

  • Yang, Haolin & Schell, Kristen R., 2022. "GHTnet: Tri-Branch deep learning network for real-time electricity price forecasting," Energy, Elsevier, vol. 238(PC).
    • Handle: RePEc:eee:energy:v:238:y:2022:i:pc:s0360544221023008
    DOI: 10.1016/j.energy.2021.122052
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    4. Shi, Tao & Li, Chongyang & Zhang, Wei & Zhang, Yi, 2023. "Forecasting on metal resource spot settlement price: New evidence from the machine learning model," Resources Policy, Elsevier, vol. 81(C).

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