@inproceedings{scholars3298,
         journal = {Proceedings of the International Joint Conference on Neural Networks},
           title = {Neural network ensemble: Evaluation of aggregation algorithms in electricity demand forecasting},
         address = {Dallas, TX},
            note = {cited By 6; Conference of 2013 International Joint Conference on Neural Networks, IJCNN 2013 ; Conference Date: 4 August 2013 Through 9 August 2013; Conference Code:102436},
            year = {2013},
             doi = {10.1109/IJCNN.2013.6707005},
        keywords = {Aggregation algorithms; Australian electricities; Bayesian model averaging; Electricity demand forecasting; Forecasting accuracy; Mean absolute percentage error; Neural network ensembles; Predictive performance, Bayesian networks; Forecasting; Neural networks, Algorithms},
             url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84893605961&doi=10.1109\%2fIJCNN.2013.6707005&partnerID=40&md5=2608e9da6bb1925a2b5b236d72dee395},
        abstract = {This paper examines and analyzes different aggregation algorithms to improve accuracy of forecasts obtained using neural network (NN) ensembles. These algorithms include equal-weights combination of Best NN models, combination of trimmed forecasts, and Bayesian Model Averaging (BMA). The predictive performance of these algorithms are evaluated using Australian electricity demand data. The output of the aggregation algorithms of NN ensembles are compared with a Naive approach. Mean absolute percentage error is applied as the performance index for assessing the quality of aggregated forecasts. Through comprehensive simulations, it is found that the aggregation algorithms can significantly improve the forecasting accuracies. The BMA algorithm also demonstrates the best performance amongst aggregation algorithms investigated in this study. {\^A}{\copyright} 2013 IEEE.},
          author = {Hassan, S. and Khosravi, A. and Jaafar, J.},
            isbn = {9781467361293}
}