Integrated genetic algorithm/tabu search and neural fuzzy networks for short-term load forecasting

An integrated genetic algorithm (GA) /fuzzy system (FS), tabu search (TS) and neural fuzzy network (NFN) method for load forecasting is presented in this paper. A fuzzy-hyper-rectangular composite neural networks (FHRCNNs) was used for the initial load forecasting. Then we used CGAFS and TS to find the optimal solution of the parameters of the FHRCNNs, instead of back-propagation (BP)( including parameters such as synaptic weights, biases, membership functions, sensitivity factor in membership functions and adjustable synaptic weights). First the CGAFS generates a set of feasible solution parameters and then puts the solution into the TS. The CGAFS has good global optimal search capabilities, but poor local optimal search capabilities. The TS method on the other hand has good local optimal search capabilities. We combined both methods to try and obtain both advantages, and in doing so eliminate the drawback of the traditional ANN training by BP. Finally, we used the (GAFSTS-NFN) to see if we could improve the quality of the solution, and if we actually could reduce the error of load forecasting. The proposed CGAFS-TS load forecasting scheme was tested using data obtain from a sample study, including one year, one week and 24-hours time periods. The results demonstrated the accuracy of the proposed load forecasting scheme.