Performance of a Hybrid Neural-Based Framework for Alternative Electricity Price Forecasting in the Smart Grid

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Electricity forecasting is an essential task for energy management systemsof microgrids deployed in smart grids. Accurate price forecasting will eventuallyenhance the economic operation of microgrids. In this regard, theliterature is rich with studies focused on predicting electricity price datausing artificial neural networks. However, most of them consider a singlemodel such as multi-layer perceptron (MLP) and radial basis function (RBF)to perform electricity price forecasting. In this paper, a hybrid frameworkbased on simultaneously utilizing MLP-RBF neural networks is presented topredict the Iranian electricity market price. In addition, few works in literatureconsidered Iran’s electricity market as their case of analysis and investigation.Forecasting results indicate that MLP neural networks outperform theRBF neural networks. The values for the coefficient of determination (R)
 corresponding to MLP and RBF neural networks are obtained 0.55 and 0.44,respectively. However, the proposed hybrid framework performed better thanboth MLP and RBF models with R-value equal to 0.71. In addition to this,the MSE and RMSE values show the superiority of the proposed method tothe single methods.