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Day-Ahead Electricity Price Forecasting Using the Wavelet Transform and ARIMA Models
Abstract
This paper proposes a novel technique to forecast day-ahead electricity prices based on the wavelet transform and ARIMA models. The historical and usually ill-behaved price series is decomposed using the wavelet transform in a set of better-behaved constitutive series. Then, the future values of these constitutive series are forecast using properly fitted ARIMA models. In turn, the ARIMA forecasts allow, through the inverse wavelet transform, reconstructing the future behavior of the price series and therefore to forecast prices. Results from the electricity market of mainland Spain in year 2002 are reported.
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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
Lago, J., De Ridder, F., & De Schutter, B. (2018) · Applied Energy · doi:10.1016/j.apenergy.2018.02.069 · Cited by 642
A moving-average filter based hybrid ARIMA–ANN model for forecasting time series data
Babu, C. N., & Reddy, B. E. (2014) · Applied Soft Computing · doi:10.1016/j.asoc.2014.05.028 · Cited by 503
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
- Conejo, A. J., Plazas, M. A., Espinola, R., & Molina, A. B.
- Venue
- IEEE Transactions on Power Systems
- Published
- 2005-05
- Volume
- 20
- Issue
- 2
- Pages
- 1035-1042
- ISSN
- 0885-8950 (ISSN-L)
- OpenAlex
- W2126709108
- Cited by
- 986
- References
- 0
- FWCI
- 26.38
- Type
- journal article
- Language
- en
- Open access
- closed
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- Conejo, A. J., Plazas, M. A., Espinola, R., & Molina, A. B. (2005). Day-Ahead Electricity Price Forecasting Using the Wavelet Transform and ARIMA Models. IEEE Transactions on Power Systems. https://doi.org/10.1109/tpwrs.2005.846054
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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.
