Dynamic filter weights neural network model integrated with differential evolution for day-ahead price forecasting in energy market

In this paper a new dynamic model for forecasting electricity prices from 1 to 24 h in advance is proposed. The model is a dynamic filter weight Adaline using a sliding mode weight adaptation technique. The filter weights for this neuron constitute of first order dynamic filter with adjustable parameters. Sliding mode invariance conditions determine a least square characterization of the adaptive weights average dynamics whose stability features may be studied using standard time varying linear system results. The approach is found to exhibit robustness characteristics and first convergence properties. Comparison of results with a local linear wavelet neural network model is also presented in this paper. The hourly electricity prices of California and Spanish energy markets are taken as experimental data and the Mean Absolute Percentage Error (MAPE) and Root Mean Square Error (RMSE) are computed to find out the forecasting performance of both the models. In both the cases the MAPE and RMSE are found to be within the tolerable limits. As dynamic filter weight neural network gives better results in comparison to local linear wavelet neural network, the former has been further integrated with differential evolution algorithm to enhance the performance.

► A dynamic filter neuron model is proposed for electricity price forecasting.
► Sliding mode strategy is used for weight updation.
► Differential evolution is used to optimize the learning parameters.
► Forecasting accuracy is within 3%.