# Short-term prediction of market-clearing price of electricity in the presence of wind power plants by a hybrid intelligent system

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## Abstract

This paper provides a new hybrid intelligent method for short-term prediction of the market-clearing price of electricity in the presence of wind power plants. The proposed method uses a data filtering technique based on wavelet transform and a radial basis function neural network, which is utilized for primary prediction. The main prediction engine comprises three MLP neural networks with different learning algorithms. To get rid of local minimums and to optimize the all neural networks, the meta-heuristic Imperialist Competitive Algorithm method is used. The input data for network training belong to the Nord Pool power market. The information includes a complete set of the historical record on electricity price and wind power generation. Moreover, the simultaneous impact of wind power generation is analyzed to predict the market-clearing price. Besides, the correlation coefficient factor is provided to consider the impact of wind power in forecasting the electricity price. Simulation results show the supremacy of the proposed method over other methods, to which it has been compared in this study. Also, the prediction error decreases significantly.

## Keywords

Neural networks Imperialist competitive algorithm Power system market Price forecasting## Notes

### Compliance with ethical standards

### Conflict of interest

The authors certify that they have no affiliations with or involvement in any organization or entity with any financial interest or non-financial interest in the subject matter or materials discussed in this manuscript.

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