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

Abstract

Electricity forecasting is an essential task for energy management systems of microgrids deployed in smart grids. Accurate price forecasting will eventually enhance the economic operation of microgrids. In this regard, the literature is rich with studies focused on predicting electricity price data using artificial neural networks. However, most of them consider a single model such as multi-layer perceptron (MLP) and radial basis function (RBF) to perform electricity price forecasting. In this paper, a hybrid framework based on simultaneously utilizing MLP-RBF neural networks is presented to predict the Iranian electricity market price. In addition, few works in literature considered Iran's electricity market as their case of analysis and investigation. Forecasting results indicate that MLP neural networks outperform the RBF 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 than both 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 to the single methods.