The DOI matches its Crossref record; 15 of the 15 references it cites were found in a registry or an index.
Day-Ahead Price Forecasting of Electricity Markets by a New Fuzzy Neural Network
Abstract
In this paper, an efficient method based on a new fuzzy neural network is proposed for short-term price forecasting of electricity markets. This fuzzy neural network has inter-layer and feed-forward architecture with a new hypercubic training mechanism. The proposed method predicts hourly market-clearing prices for the day-ahead electricity markets. By combination of fuzzy logic and an efficient learning algorithm, an appropriate model for the nonstationary behavior and outliers of the price series is presented. The proposed method is examined on the Spanish electricity market. It is shown that the method can provide more accurate results than the other price forecasting techniques, such as ARIMA time series, wavelet-ARIMA, MLP, and RBF neural networks.
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Cites
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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
A GARCH Forecasting Model to Predict Day-Ahead Electricity Prices
Garcia, R. C., Contreras, J., vanAkkeren, M., & Garcia, J. B. C. (2005) · IEEE Transactions on Power Systems · doi:10.1109/tpwrs.2005.846044 · Cited by 706
Electricity price short-term forecasting using artificial neural networks
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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
Optimal Charging Strategies for Unidirectional Vehicle-to-Grid
Sortomme, E., & El-Sharkawi, M. A. (2011) · IEEE Transactions on Smart Grid · doi:10.1109/tsg.2010.2090910 · Cited by 782
A hybrid approach of intelligent systems to help predict absenteeism at work in companies
Araujo, V. S., Rezende, T. S., Guimarães, A. J., Araujo, V. J. S., & de Campos Souza, P. V. (2019) · SN Applied Sciences · doi:10.1007/s42452-019-0536-y · Cited by 25
Metadata
- Authors
- Amjady, N.
- Venue
- IEEE Transactions on Power Systems
- Published
- 2006-05
- Volume
- 21
- Issue
- 2
- Pages
- 887-896
- ISSN
- 0885-8950 (ISSN-L)
- OpenAlex
- W2132591205
- Cited by
- 371
- References
- 15
- FWCI
- 16.87
- Type
- journal article
- Language
- en
- Open access
- closed
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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.
