The DOI matches its Crossref record; 24 of the 24 references it cites were found in a registry or an index.
An Adaptive Wavelet Neural Network-Based Energy Price Forecasting in Electricity Markets
Abstract
In a competitive electricity market, an accurate forecasting of energy prices is an important activity for all the market participants either for developing bidding strategies or for making investment decisions. An adaptive wavelet neural network (AWNN) is proposed in this paper for short-term price forecasting (STPF) in the electricity markets. A commonly used Mexican hat wavelet has been chosen as the activation function for hidden-layer neurons of feed-forward neural network (FFNN). To demonstrate the effectiveness of the proposed approach, day-ahead prediction of market clearing price (MCP) of Spain market, which is a duopoly market with a dominant player, and locational marginal price (LMP) forecasting in PJM electricity market, are considered. The forecasted results clearly show that AWNN has good prediction properties compared to other forecasting techniques, such as wavelet-ARIMA, multi-layer perceptron (MLP) and radial basis function (RBF) neural networks as well as recently proposed fuzzy neural network (FNN).
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Cites
Zhang, Q., & Benveniste, A. (1992) · IEEE Transactions on Neural Networks · doi:10.1109/72.165591 · Cited by 2,091
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
Market operations in electric power systems: forecasting, scheduling, and risk management
(2002) · Choice Reviews Online · doi:10.5860/choice.40-1574 · Cited by 811
Using wavelet network in nonparametric estimation
Zhang, Q. (1997) · IEEE Transactions on Neural Networks · doi:10.1109/72.557660 · Cited by 727
Wavelet neural networks for function learning
Zhang, J., Walter, G. G., Miao, Y., & Lee, W. N. W. (1995) · IEEE Transactions on Signal Processing · doi:10.1109/78.388860 · Cited by 716
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
Cited by
Electricity price forecasting: A review of the state-of-the-art with a look into the future
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Hoang, D.-T., & Kang, H.-J. (2019) · Cognitive Systems Research · doi:10.1016/j.cogsys.2018.03.002 · Cited by 450
Electricity Price Forecasting Using Recurrent Neural Networks
Ugurlu, U., Oksuz, I., & Tas, O. (2018) · Energies · doi:10.3390/en11051255 · Cited by 299
A model predictive approach for community battery energy storage system optimization
Pezeshki, H., Wolfs, P., & Ledwich, G. (2014) · 2014 IEEE PES General Meeting | Conference & Exposition · doi:10.1109/pesgm.2014.6938788 · Cited by 22
Metadata
- Authors
- Pindoriya, N. M., Singh, S. N., & Singh, S. K.
- Venue
- IEEE Transactions on Power Systems
- Published
- 2008-08
- Volume
- 23
- Issue
- 3
- Pages
- 1423-1432
- ISSN
- 0885-8950 (ISSN-L)
- 1558-0679
- OpenAlex
- W2096220984
- Cited by
- 305
- References
- 24
- FWCI
- 14.94
- Type
- journal article
- Language
- en
- Open access
- closed
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