A novel GA-based neural network for short-term load forecasting

Abstract

This paper presents a GA-based neural network with a novel neuron model. In this model, the neuron has two activation transfer functions and exhibits a node-by-node relationship in the hidden layer. This neural network provides a better performance than a traditional feed-forward neural network and fewer hidden nodes are needed. The parameters of the proposed neural network are tuned by GA with arithmetic crossover and non-uniform mutation. An application on short-term load forecasting is given to show the merits of the proposed neural network.