Abstract
Probabilistic forecasting is an appropriate tool that helps electricity markets participants to improve their decision making. Due to changes in electricity prices, the point forecasting accuracy cannot be guaranteed. Hence, participants are more interested in results of probabilistic forecasting methods such as prediction intervals method. In this paper, a hybrid approach for probabilistic electricity price forecasting is presented. This model is based on using improved clonal selection algorithm and extreme learning machine for neural networks training process and wavelet preprocess. The wavelet is utilized to decompose data into well behaved subsets, which increases accuracy of the model. Also, due to the high required computational time for training the neural networks, autocorrelation function is used to reduce the number of neural networks inputs. Finally, in order to evaluate the proposed probabilistic forecasting method, the Ontario and Australian electricity markets data are used.
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Website of Ontario electricity market http://www.ieso.ca
Website of Australian electricity market. http://www.aemo.com.au
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Rafiei, M., Niknam, T. & Khooban, M.H. Probabilistic electricity price forecasting by improved clonal selection algorithm and wavelet preprocessing. Neural Comput & Applic 28, 3889–3901 (2017). https://doi.org/10.1007/s00521-016-2279-7
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DOI: https://doi.org/10.1007/s00521-016-2279-7