Integrated evolving fuzzy neural networks and artificial intelligence for load forecasting

An Integrated Artificial Intelligence and Fuzzy Neural Network (IEFNN-AI) for load forecasting method is presented in this paper. First we used Fuzzy Hyper-Rectangular Composite Neural Networks (FHRCNNs) for the initial load forecasting. Then we used evolutionary programming (EP) and Tabu Search (TS) to find the optimal solution of the parameters of FHRCNNs (including parameters such as synaptic weights, biases, membership functions, sensitivity factor in membership functions and adjustable synaptic weights). We knew that the EP has a good capability for searching for globe optimal value, but a poor capability for searching for the local area optimal value. And, the TS had a good capability for searching for a local area optimal value. Therefore we combined both methods to obtain both advantages. Finally, we use the (IEFNN-AI) to see if we could improve the solution quality, and if we actually could reduce the error of load forecasting. The proposed IEFNN-AI load forecasting scheme was tested using data obtained from a sample study including one year, one month and 24-hours time periods. And compare with ANN, GA-ANN (Genetic Algorithm-ANN). The result demonstrated the accuracy of the proposed load forecasting scheme.