Journal of Global Optimization
Differential evolution approach for optimal reactive power dispatch
Applied Soft Computing
Automatic image pixel clustering with an improved differential evolution
Applied Soft Computing
Differential evolution algorithm with strategy adaptation for global numerical optimization
IEEE Transactions on Evolutionary Computation
Automatic Clustering Using an Improved Differential Evolution Algorithm
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Expert Systems with Applications: An International Journal
Vehicle-to-grid communication system for electric vehicle charging
Integrated Computer-Aided Engineering - Anniversary Volume: Celebrating 20 Years of Excellence
Hi-index | 12.05 |
In this paper a new dynamic model for forecasting electricity prices from 1 to 24h 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.