Cross-Market Price Difference Forecast using Deep Learning for Electricity Markets
Price forecasting is in the center of decision making in electricity markets. Many researches have been done in forecasting energy prices while little research has been reported on forecasting price difference between day-ahead and real-time markets due to its high volatility, which however plays a critical role in virtual trading. To this end, this paper takes the first attempt to employ novel deep learning architecture with Bidirectional Long-Short Term Memory (LSTM) units to forecast the price difference between day-ahead and real-time markets for the same node. The raw data is collected from PJM market, processed and fed into the proposed network. The Root Mean Squared Error (RMSE) and customized performance metric are used to evaluate the performance of the proposed method. Case studies show that it outperforms the traditional statistical models like ARIMA, and machine learning models like XGBoost and SVR methods in both RMSE and the capability of forecasting the sign of price difference. In addition to cross-market price difference forecast, the proposed approach has the potential to be applied to solve other forecasting problems such as price spread forecast in DA market for Financial Transmission Right (FTR) trading purpose.
R. Das et al., "Cross-Market Price Difference Forecast using Deep Learning for Electricity Markets," Proceedings of the 2020 IEEE PES Innovative Smart Grid Technologies Conference Europe, pp. 854-858, Institute of Electrical and Electronics Engineers (IEEE), Nov 2020.
The definitive version is available at https://doi.org/10.1109/ISGT-Europe47291.2020.9248867
2020 IEEE PES Innovative Smart Grid Technologies Europe, ISGT-Europe (2020: Oct. 26-28, Virtual)
Electrical and Computer Engineering
Center for High Performance Computing Research
Keywords and Phrases
DA/RT Price Difference; Deep Learning; Electricity Markets; Forecasting; Long-Short Term Memory; LSTM
International Standard Book Number (ISBN)
Article - Conference proceedings
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10 Nov 2020