Abstract
Price forecasting is at the center of decision making in electricity markets. Much research has been done in forecasting energy prices for a single market while little research has been reported on forecasting price difference between markets, which presents higher volatility and yet plays a critical role in applications such as virtual trading. To this end, this paper takes the first attempt at it and employs novel deep learning architecture with Bidirectional Long-Short Term Memory (LSTM) units and Sequence-to-Sequence (Seq2Seq) architecture to forecast nodal price difference between day-ahead and real-time markets. In addition to value prediction, these deep learning architectures are also used to develop classification models to predict the price difference bands/ranges. The proposed methods are tested using historical PJM market data, and evaluated using Root Mean Squared Error (RMSE) and other customized performance metrics. Case studies show that both deep learning methods outperform common methods including ARIMA, XGBoost and Support Vector Regression (SVR) methods. More importantly, the deep learning methods can capture the magnitude and timing of price difference spikes. Numerical results show the Seq2Seq model performs particularly well and demonstrates generalization capability to extended forecasting lead time.
Recommended Citation
R. Das et al., "Forecasting Nodal Price Difference between Day-Ahead and Real-Time Electricity Markets using Long-Short Term Memory and Sequence-To-Sequence Networks," IEEE Access, vol. 10, pp. 832 - 843, Institute of Electrical and Electronics Engineers (IEEE), Jan 2022.
The definitive version is available at https://doi.org/10.1109/ACCESS.2021.3133499
Department(s)
Electrical and Computer Engineering
Research Center/Lab(s)
Intelligent Systems Center
Keywords and Phrases
Convergence; Deep learning; Electricity supply industry; Forecasting; Predictive models; Time series analysis; Timing
International Standard Serial Number (ISSN)
2169-3536
Document Type
Article - Journal
Document Version
Final Version
File Type
text
Language(s)
English
Rights
© 2022 Institute of Electrical and Electronics Engineers (IEEE), All rights reserved.
Creative Commons Licensing
This work is licensed under a Creative Commons Attribution 4.0 License.
Publication Date
01 Jan 2022
Comments
This work was supported in part by DARPA under Grant D18AP00054, in part by the U.S. Department of Energy's Office of Energy Efficiency and Renewable Energy (EERE) under the Water Power Technologies Office Award DE-EE0008781, in part by the National Science Foundation under Grant OAC-1919789, in part by the Missouri University of Science and Technology Intelligent Systems Center, in part by the Mary K. Finley Missouri Endowment, in part by the National Science Foundation, in part by the Lifelong Learning Machines Program from DARPA/Microsystems Technology Office, in part by the Army Research Laboratory (ARL), and in part by the Army Night Vision Laboratory under Grant W911NF-18-2-0260 and Grant W911NF-14-2-0034.