The DOI matches its Crossref record; 22 of the 22 references it cites were found in a registry or an index.
A new feature selection and hybrid forecast engine for day-ahead price forecasting of electricity markets
Abstract
In this paper, a new feature selection and forecast engine is presented for day ahead prediction of electricity prices, which are so valuable for both producers and consumers in the new competitive electric power markets. In a competitive electricity market, forecast of energy prices is a key information for the market participants. However, price signal usually has a complex behavior due to its nonlinearity, non-stationary, and time variance. Also, an appropriate feature selection is crucial for accurate forecasting. In this paper, a two-step approach that identifies a set of candidate features based on the data characteristics proposed and then selects a subset of them using correlation and instance-based feature selection methods, applied in a systematic way. Then, a combination of wavelet transform (WT) and a hybrid forecast method is presented based on neural network (NN) and an optimization algorithms. The proposed method is examined on PJM electricity market and compared with some of the most recent price forecast methods. These comparisons illustrate effectiveness of the proposed strategy.
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Cites
Theoretical and Empirical Analysis of ReliefF and RReliefF
Robnik-Šikonja, M., & Kononenko, I. (2003) · Machine Learning · doi:10.1023/a:1025667309714 · Cited by 3,025 · cited 1×
“RF [27] is an instance-based feature ranking method from the Relief family of methods for feature selection applicable to both classification and forecasting tasks.”
MethodA Practical Approach to Feature Selection
Kira, K., & Rendell, L. A. (1992) · Machine Learning Proceedings 1992 · doi:10.1016/b978-1-55860-247-2.50037-1 · Cited by 2,978
Correlation-based Feature Selection for Discrete and Numeric Class Machine Learning
Hall, M. A. (2000) · Cited by 1,715
Market Operations in Electric Power Systems: Forecasting, Scheduling, and Risk Management
Shahidehpour, M., Yamin, H., & Li, Z. (2002) · Wiley eBooks · doi:10.1002/047122412x · Cited by 1,616
ARIMA models to predict next-day electricity prices
Contreras, J., Espinola, R., Nogales, F. J., & Conejo, A. J. (2003) · IEEE Transactions on Power Systems · doi:10.1109/tpwrs.2002.804943 · Cited by 1,553
Day-Ahead Electricity Price Forecasting Using the Wavelet Transform and ARIMA Models
Conejo, A. J., Plazas, M. A., Espinola, R., & Molina, A. B. (2005) · IEEE Transactions on Power Systems · doi:10.1109/tpwrs.2005.846054 · Cited by 986
ARIMA Models to Predict Next-Day Electricity Prices
Contreras, J., Espinola, R., Nogales, F. J., & Conejo, A. J. (2002) · IEEE Power Engineering Review · doi:10.1109/mper.2002.4312577 · Cited by 928
Forecasting next-day electricity prices by time series models
Nogales, F. J., Contreras, J., Conejo, A. J., & Espinola, R. (2002) · IEEE Transactions on Power Systems · doi:10.1109/tpwrs.2002.1007902 · Cited by 864
Short-term electricity prices forecasting in a competitive market: A neural network approach
Catalão, J. P. S., Mariano, S. J. P. S., Mendes, V. M. F., & Ferreira, L. A. F. M. (2007) · Electric Power Systems Research · doi:10.1016/j.epsr.2006.09.022 · Cited by 406
Energy Price Forecasting in the Ontario Competitive Power System Market
Rodriguez, C. P., & Anders, G. J. (2004) · IEEE Transactions on Power Systems · doi:10.1109/tpwrs.2003.821470 · Cited by 319
Cited by
Huang, W., & Fooladi, H. (2021) · Energy Reports · doi:10.1016/j.egyr.2021.07.036 · Cited by 44
Metadata
- Authors
- Gollou, A. R., & Ghadimi, N.
- Venue
- Journal of Intelligent & Fuzzy Systems
- Publisher
- SAGE Publications
- Published
- 2017-03-04
- Volume
- 32
- Issue
- 6
- Pages
- 4031-4045
- ISSN
- 1064-1246 (ISSN-L)
- 1875-8967
- OpenAlex
- W2593452825
- Cited by
- 267
- References
- 22
- FWCI
- 13.43
- Type
- journal article
- Language
- en
- Open access
- closed
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- Gollou, A. R., & Ghadimi, N. (2017). A new feature selection and hybrid forecast engine for day-ahead price forecasting of electricity markets. Journal of Intelligent & Fuzzy Systems. https://doi.org/10.3233/jifs-152073
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
