The DOI matches its Crossref record.
Energy Price Forecasting in the Ontario Competitive Power System Market
Abstract
This paper introduces a method for forecasting energy prices using artificial intelligence methods, such as neural networks and fuzzy logic, and a combination of the two. The new approach is compared with some of the exiting methods. Various factors affecting the market clearing price are investigated. Results for the Ontario electricity market are presented.
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Citations
Cited by
Electricity price forecasting: A review of the state-of-the-art with a look into the future
Weron, R. (2014) · International Journal of Forecasting · doi:10.1016/j.ijforecast.2014.08.008 · Cited by 1,646
Day-Ahead Price Forecasting of Electricity Markets by a New Fuzzy Neural Network
Amjady, N. (2006) · IEEE Transactions on Power Systems · doi:10.1109/tpwrs.2006.873409 · Cited by 371
Gollou, A. R., & Ghadimi, N. (2017) · Journal of Intelligent & Fuzzy Systems · doi:10.3233/jifs-152073 · Cited by 267
Metadata
- Authors
- Rodriguez, C. P., & Anders, G. J.
- Venue
- IEEE Transactions on Power Systems
- Published
- 2004-02
- Volume
- 19
- Issue
- 1
- Pages
- 366-374
- ISSN
- 0885-8950 (ISSN-L)
- OpenAlex
- W2120094018
- Cited by
- 319
- References
- 0
- FWCI
- 20.93
- Type
- journal article
- Language
- en
- Open access
- closed
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- CoChat. (2026). Citation check: Energy Price Forecasting in the Ontario Competitive Power System Market. https://app.cochat.ai/papers/10.1109/tpwrs.2003.821470
- The paper
- Rodriguez, C. P., & Anders, G. J. (2004). Energy Price Forecasting in the Ontario Competitive Power System Market. IEEE Transactions on Power Systems. https://doi.org/10.1109/tpwrs.2003.821470
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
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.
