Application of fuzzy neural networks and artificial intelligence for load forecasting

An integrated evolving fuzzy neural network and simulated annealing (AIFNN) 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 simulated annealing (SA) 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 optimal value. And, the SA only had a good capability for searching for a local optimal value. Therefore, we combined both methods to obtain both advantages, and so improve the shortcoming of the traditional ANN training where the weights and biases are always trapped into a local optimum. Finally, we use the AIFNN to see if we could improve the solution quality, and if we actually could reduce the error of load forecasting. The proposed AIFNN load forecasting scheme was tested using data obtained from a sample study including I year, I month and 24 h time periods. The result demonstrated the accuracy of the proposed load forecasting scheme. (C) 2004 Elsevier B.V. All rights reserved.