The aim of the thesis is to examine and analyze different aggregation algorithms to the forecasts obtained from individual neural network (NN) models in an ensemble. In this study an ensemble of 100 NN models are constructed with a heterogeneous architecture. The outputs from the individual NN models were combined by four different aggregation algorithms in NNs ensemble. These algorithms include equal weights combination of Best NN models, combination of trimmed forecasts, combination through Variance-Covariance method and Bayesian Model Averaging. The aggregation algorithms were employed on the forecasts obtained from all individual NN models as well as on a number of the best forecasts obtained from the best NN models. The output of the aggregation algorithms of NNs ensemble were analyzed and compared with each other and with the individual NN models used in NNs ensemble. The results of the aggregation algorithms of NNs ensemble are also compared with the Simple Averaging method. The performances of these aggregation algorithms of NNs ensemble were evaluated with the mean absolute percentage error and symmetric mean absolute percentage error. In the empirical analysis, the methodologies developed were tested on the Universiti Teknologi PETRONAS load data set of five years from 2006 to 2010 for forecasting. It can be concluded from the results that the aggregation algorithms of NNs ensemble can improve the accuracy of forecast than the individual NN models with a test data set. Furthermore, in the comparison with the Simple Averaging method, the aggregation algorithms of NNs ensemble demonstrate slightly better performance than the Simple Averaging. It has also been observed during the empirical analysis that; reducing the size of ensemble increases the diversity and, hence, accuracy. Moreover, it has been concluded that more benefits can be achieved by the utilization of an advanced method for forecast combinations.