Artificial Neural Network-Based Peak Load Forecasting Using Conjugate Gradient Methods

Abstract

Daily electrical peak load forecasting has been done using the feed forward neural network based upon the conjugate gradient back propagation methods by incorporating the effect of eleven weather parameters, the previous day's peak load information, and the type of day. To avoid the trapping of the network into a state of local minima, the optimization of user-defined parameters viz., leaming rate and error goal has been performed. The training data-set has been selected using a growing window concept and is reduced per the nature of the day and the season for which the forecast is made. For redundancy removal in the input variables, reduction of the number of input variables has been done using the principal component analysis method of factor extraction. The resultant data set is used for the training of a three-layered neural network To increase the leaming speed, the weights and biases are initialized according to the Nguyen and Widrow method. To avoid overfitting, an early training is stopped early at the minimum validation error.