Ant Colony Based Feed Forward NN Short-Term Load Forecasting Model with Input Selection and DA Clustering

More accurate load forecasting is more and more important for economic generation and system security in power systems. In this paper, an ant colony algorithm based feed forward neural network model to integrate maximum correlation theory and deterministic annealing clustering technique for load forecasting problem is proposed. As we all know, if so many load influential factors are consider as network input neurons, it will result in complicated structure of network, long learning time and inaccurate prediction. So, first, maximum correlation method is used for selecting main load influential factors which have more relevant to load parameter. It can reduce irrelevant load influential factors and the input variables of the input layer for neural network. Next, deterministic annealing (DA) clustering technique is applied for dividing the practical load data into representative groups in order to reduce training time. Finally, the feed forward neural network with ant colony training algorithm is provided for short term load forecasting problem since this training algorithm can effective avoid local optimum and improve training accuracy. The experiments and the performance with presented neural network model are given, and the results showed the method could provide a satisfactory improvement of the forecasting accuracy.