IEEE transactions on power systems, 2008-08, Vol.23 (3), p.1423-1432
2008
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Details
- Autor(en) / Beteiligte
- Titel
- An Adaptive Wavelet Neural Network-Based Energy Price Forecasting in Electricity Markets
- Ist Teil von
- IEEE transactions on power systems, 2008-08, Vol.23 (3), p.1423-1432
- Ort / Verlag
- New York: IEEE
- Erscheinungsjahr
- 2008
- Quelle
- IEEE Xplore
- Beschreibungen/Notizen
- 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).
- Sprache
- Englisch
- Identifikatoren
- ISSN: 0885-8950eISSN: 1558-0679DOI: 10.1109/TPWRS.2008.922251Titel-ID: cdi_proquest_journals_862826351
- Format
- –
- Schlagworte
- Adaptive systems, Adaptive wavelet neural network, Economic forecasting, electricity markets, Electricity supply industry, feed-forward neural network, Feedforward neural networks, Feedforward systems, Fuzzy neural networks, Investments, Load forecasting, locational marginal price forecasting, Neural networks, Neurons, short-term price forecasting