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Electricity price short-term forecasting using artificial neural networks
Abstract
This paper presents the system marginal price (SMP) short-term forecasting implementation using the artificial neural networks (ANN) computing technique. The described approach uses the three-layered ANN paradigm with backpropagation. The retrospective SMP real-world data, acquired from the deregulated Victorian power system, was used for training and testing the ANN. The results presented in this paper confirm considerable value of the ANN based approach in forecasting the SMP.
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Citations
Cited by
Lago, J., De Ridder, F., & De Schutter, B. (2018) · Applied Energy · doi:10.1016/j.apenergy.2018.02.069 · Cited by 642
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
Self-scheduling of a hydro producer in a pool-based electricity market
Conejo, A. J., Arroyo, J. M., Contreras, J., & Villamor, F. A. (2002) · IEEE Transactions on Power Systems · doi:10.1109/tpwrs.2002.804951 · Cited by 326
Metadata
- Authors
- Szkuta, B. R., Sanabria, L. A., & Dillon, T. S.
- Venue
- IEEE Transactions on Power Systems
- Published
- 1999
- Volume
- 14
- Issue
- 3
- Pages
- 851-857
- ISSN
- 0885-8950 (ISSN-L)
- OpenAlex
- W2170831719
- Cited by
- 494
- References
- 0
- FWCI
- 9.79
- Type
- journal article
- Language
- en
- Open access
- closed
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- CoChat. (2026). Citation check: Electricity price short-term forecasting using artificial neural networks. https://app.cochat.ai/papers/10.1109/59.780895
- The paper
- Szkuta, B. R., Sanabria, L. A., & Dillon, T. S. (1999). Electricity price short-term forecasting using artificial neural networks. IEEE Transactions on Power Systems. https://doi.org/10.1109/59.780895
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