Short-term load forecasting using optimized neural network with genetic algorithm

An optimized neural network modeling approach with genetic algorithm for short-term load forecasting based on only multiple delayed historical power load data is proposed. Genetic algorithm is used to globally optimize the number of delayed input neurons and the number of neurons in the hidden layer of the neural network architecture. Modification of Levenberg-Marquardt algorithm with Bayesian regularization is used to improve the generalization ability of the neural network. The performance of our proposed approach has been compared using actual power load data sets. Numerical results show that our proposed power load forecasting approach is comparable to the existing approaches that use multiple input variables such as power load data, day type load patterns and weather conditions

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